40 Hz audiovisual stimulation improves sustained attention and related brain oscillations.

Code ↔ Paper

13 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 13 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Results › 40 Hz flicker is correlated with decreases in delta activity during a vigilance task ↔ EEG Analysis/S4_ALLSubjects_NoCut_NoNotch_2_100hz.m, lines 4300–4333 · score 0.87 · 13–30 Hz, 30–37 Hz, 8–13 Hz, 39–41 Hz, power spectral densities, 1–4 Hz
[2] § Results › 40 Hz flicker increases low-alpha functional connectivity which is correlated with better behavior performance ↔ EEG Analysis/S4_ALLSubjects_NoCut_NoNotch_2_100hz.m, lines 2058–2100 · score 0.78 · 10–13 Hz, top quartile, upper alpha, lower alpha, 8–10 Hz, channel pairs
[3] § Results › 40 Hz flicker increases low-alpha functional connectivity which is correlated with better behavior performance ↔ EEG Analysis/S4_ALLSubjects_NoCut_NoNotch_2_100hz.m, lines 2058–2100 · score 0.75 · 10–13 Hz, top quartile, upper alpha, lower alpha, 8–10 Hz, channel pair
[4] § Results › 40 Hz flicker is correlated with decreases in delta activity during a vigilance task ↔ EEG Analysis/S4_ALLSubjects_NoCut_NoNotch_2_100hz.m, lines 3178–3220 · score 0.74 · 30–37 Hz, 8–13 Hz, 39–41 Hz, power spectral density, 4–8 Hz, PSD
[5] § Methods › EEG data analyses ↔ EEG Analysis/S3_CompletePreprocessing.m, lines 14–46 · score 0.70 · high pass filter, EEGLAB, 4 seconds, preprocessed, ICA, epochs
[6] § Methods › EEG data analyses ↔ EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/welchSpecLuTrial.m, the whole file · a weak match · score 0.67 · Signal Processing, power spectral density, Welch, overlapping, windows
[7] § Methods › EEG data analyses ↔ EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_welchPsdTrial.m, the whole file · a weak match · score 0.67 · Signal Processing, power spectral density, Welch, overlapping, windows
[8] § Methods › Statistical approach ↔ EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/myfriedman.m, lines 1–83 · score 0.65 · Kruskal Wallis, Post hoc, ANOVA, mixed, alpha
[9] § Methods › EEG data analyses ↔ EEG Analysis/S0_ConvertBDFtoSet_and_Preprocess_for_Syncing.m, lines 14–44 · score 0.61 · high pass filter, 4 seconds, preprocessed, ICA, epochs, noise
[10] § Methods › EEG data analyses ↔ EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wpli_TrialIndex.m, the whole file · a weak match · score 0.60 · weighted phase lag, volume conduction, WPLI, connectivity, signal
[11] § Results › 40 Hz flicker increases low-alpha functional connectivity which is correlated with better behavior performance ↔ EEG Analysis/S4_ALLSubjects_NoCut_NoNotch_2_100hz.m, lines 1745–1808 · score 0.56 · High functional connectivity, alpha band, channel pairs, permutation, frequency band, WPLI
[12] § Results › 40 Hz flicker improved accuracy and reaction time in a vigilance task ↔ EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/myfriedman.m, lines 1–83 · score 0.56 · Kruskal Wallis, post hoc, rank, sum
[13] § Results › 40 Hz flicker increases low-alpha functional connectivity which is correlated with better behavior performance ↔ EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wpli_TrialIndex.m, the whole file · a weak match · score 0.55 · Weighted Phase Lag, volume conduction, WPLI, connectivity

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The authors' code

MATLAB · 4,365 lines · 195 KB · no license · 5 matches

%% Step 4 - Does PSD. (derived from Step2_WPLI by Lu Z)TrialType
% Must add GenMatCode-main and all subfolders to path before running
S4Start = tic;
currDate = strrep(datestr(datetime), ':', '_');
currDate = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
scriptName = mfilename;
%% Load All EEGs
% LoadPath='Y:\singer\LuZhang\Project6-EEG\Results\Step0-PreparingData\';
cd("2_CheckSync_Outputs\")
%% Get newest file
newestFolder = getNewestFolder();
%cd(newestFolder);
%newestEEGData = getNewestFile(); % Automatically gets the newest .mat file in folder
%newestEEGData=
%% Select file to load
inputDatafile = '01-Apr-2024 13_38_57EEGDataStep2CheckSyncOutput.mat' %#ok<NOPTS> % can replace variable with hardcoded EEG file
tic
load(inputDatafile)
%load('01-Apr-2024 13_38_57EEGDataStep2CheckSyncOutput.mat')
%% Create folder
disp(['EEG data loaded:', inputDatafile])
toc
% load('02-Feb-2024 17_46_26EEGData4EyeBlinkRemoval.mat')
cd ..
%cd ..
SaveFolder=['4_COH\' currDate '_' scriptName];
mkdir(SaveFolder);
save([SaveFolder '\Step4DataInputSummary.mat'],'inputDatafile','currDate','scriptName')
%% Create output text
%Generate output message
SummaryTextOutput='';
% if isempty(SummaryTextOutput)
% SummaryTextOutput = 'None';
% end
SummaryTextOutput = sprintf('Step4 started on %s\nScript used: %s \n', currDate, scriptName);
SummaryTextOutput = sprintf('%s \nInput dataset: %s \n', SummaryTextOutput, inputDatafile);
% SummaryTextOutput = sprintf('%s \n%d out of %d files synced:\n%s', SummaryTextOutput, iCsv, nCsv, subjectsList);
% endDateAndTime = char(datetime('now','TimeZone','local','Format','d-MMM-y HH:mm:ss'));
% endDuration = toc(tStart);
% SummaryTextOutput = sprintf('%s\nStep1Syncing completed at: %s\nSyncing duration: %g seconds.', SummaryTextOutput, endDateAndTime, endDuration);
% Add toc
%Display the output message in the command window
disp(SummaryTextOutput);
%Create a .txt file with the output message
filePath = fullfile(SaveFolder, [scriptName, currDate '_Summary.txt']);
fileID = fopen(filePath, 'w');
if fileID == -1
error('Failed to open or create the file: %s', filePath);
else
fprintf(fileID, '%s', SummaryTextOutput);
fclose(fileID);
fprintf('File created and written: %s\n', filePath);
end
%% Set Channels, Locations for Heatmap
% (Necessary if already done in Step 0?)
channels = {'Fp1', 'AF3', 'F7', 'F3', 'FC1', 'FC5', 'T7', 'C3', 'CP1', 'CP5', 'P7', 'P3', 'Pz', 'PO3', 'O1', 'Oz', 'O2', 'PO4', 'P4', 'P8', 'CP6', 'CP2', 'C4', 'T8', 'FC6', 'FC2', 'F4', 'F8', 'AF4', 'Fp2', 'Fz', 'Cz', 'EXG1', 'EXG2', 'EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8'};
EEGchInd=1:32;
EEGch=channels(EEGchInd);
ChNTotal=length(channels);
NeedFields={'labels','theta','radius'};
tempN=fieldnames(EEGList{1}.chanlocs);
tempN1=tempN;
tempN2=tempN;
tempN1([1 2 11 12])=[];
NeedI=setdiff(1:length(tempN),[1 2 12]);
tempN2([2 12])=[];
ChanPos=FieldName2Struct(tempN1);
TempTable=cell2table(squeeze(struct2cell(EEGList{1}.chanlocs)));
ChInd=table2array(cell2table(table2array(TempTable(11,:))));
IndEx=find(ChInd>=33);
ChIndWritten=[];
DataTemp=zeros(length(tempN1),length(EEGchInd))+nan;
for iFile=1:length(EEGList)
TempTable=cell2table(squeeze(struct2cell(EEGList{iFile}.chanlocs)));
ChI=table2array(cell2table(table2array(TempTable(11,:))));
IndEx=find(ChI>=33);
Invalid=find(ChI>=33);
ChIndV=ChI;
ChIndV(Invalid)=[];
TempTable(:,Invalid)=[];
InforAdd=TempTable;
InforAdd([1 2 11 12],:)=[];
[ChAdd,I1]=setdiff(ChIndV,ChIndWritten);
temp=table2array(cell2table(table2array(InforAdd)));
DataTemp(:,ChAdd)=temp(:,I1);
ChIndWritten=find(isnan(DataTemp(1,:))==0);
if length(ChIndWritten)==length(EEGchInd)
break
end
end
for ifield=1:length(tempN1)
ChanPos=setfield(ChanPos,{1,1},tempN1{ifield},DataTemp(ifield,:));
end
ChanPos.labels=EEGch;
ChanEEGLab=rmfield(ChanPos,'labels');
tempName=fieldnames(ChanEEGLab);
for iCh=1:length(EEGchInd)
for iN=1:length(tempName)
dataTemp=getfield(ChanPos,{1},tempName{iN});
ChanEEGLab=setfield(ChanEEGLab,{1,iCh},tempName{iN},dataTemp(iCh));
end
ChanEEGLab(iCh).labels=EEGch{iCh};
ChanEEGLab(iCh).urchan=iCh;
end
%% Assign Condition Groups *** Run to set GroupName
FlickerSubj{4}=[20:27 30:33 44:51 53 55:58 62:64 66:69 71:73 75 77:81 83:87]; % All control subjects (Random and Light) no cuts
GroupName{4}='BothControls'; % 'BothControls' = Random and Light together. previously 'Random' or 'Light'
FlickerSubj{5}=[20:27 30:33 44:51 53 55:58 62:63 66 68 69 71:73 75 77:81 83:87]; % All control subjects minus 64 and 67 (for RT)
GroupName{5}='BothControlsRT'; % 'BothControls' = Random and Light together. previously 'Random' or 'Light'
FlickerSubj{1}=[10:19 34:42 52 54 59:61 65 70 74 76 82]; % SubjID of 40Hz group.
GroupName{1}='40Hz';
FlickerSubj{2} = [62:64 66:69 71:73 75 77:81 83:87]; % All SubjID for Light Group (no cuts)
GroupName{2}='Light';
FlickerSubj{3}=[20:27 30:33 44:51 53 55:58]; % SubjID of Random flicker group
GroupName{3} = 'Random';
FlickerSubj{6} = [62:63 66 68 69 71:73 75 77:81 83:87]; % All SubjID for Light Group for RT (removed s064 and s067 due to incorrect average RTs
GroupName{6}='LightRT'; % No 64 67 for WPLI vs RT
% Updated Groups (includes 2023 EEGs)
% FlickerSubj{1}=[20:28 30:33 45 48:50 56:58]; % (20 total) SubjID of Random flicker group (Accuracy cuts)
% FlickerSubj{2}=[10:19 39 40 52 54 59 60 74 76 82]; % (20 total) % SubjID of 40Hz group. Newest 40 Hz EEGs added (2023) (Accuracy Cut
% FlickerSubj{3}=[]; % check if ID is still matching
%% Get Accuracies for all subjects
Acc=zeros(length(FileStruct),1)+nan;
SubjsAvgRT = zeros(length(FileStruct),1)+nan;
for iFile=1:length(FileStruct)
if ~isempty(FileStruct(iFile).Subj)
%% May need to uncomment out the below
SubjID(iFile) = str2num(FileStruct(iFile).Subj(end-1:end));
Acc(iFile)=length(FileStruct(iFile).hits)/(length(FileStruct(iFile).hits)+length(FileStruct(iFile).misses));
%% Calculate Avg RT per subject using data found in FileStruct->dotsynch
isHit = (FileStruct(iFile).dotsynch(:,3)==1); % get logical index for all trials that are Hits (misses and premature hits will = 0)
colorchangeTimes = FileStruct(iFile).dotsynch(isHit,1); % get time of color change for Hit trials only
subjRTtimes = FileStruct(iFile).dotsynch(isHit,2); % get RT time for Hit trials (this should ignore premature hits)
subjRTduration = subjRTtimes - colorchangeTimes; % subtract RT time from color change time to get duration of RT (AKA the reaction time)
subjRTdurInSecs = subjRTduration/512; % sample rate is generally 512 samples per second
SubjsAvgRT(iFile) = mean(subjRTdurInSecs);
end
end
%% SubjG assignment for WPLI calculation
SubjG{1}=[];
SubjG{2}=[];
SubjG{3}=[];
SubjG{4}=[];
SubjG{5}=[];
SubjG{6}=[];
[~,SubjG{1},~]=intersect(SubjID,FlickerSubj{1}); % 40Hz
[~,SubjG{2},~]=intersect(SubjID,FlickerSubj{2}); % Light
[~,SubjG{3},~]=intersect(SubjID,FlickerSubj{3}); % Random
[~,SubjG{4},~]=intersect(SubjID,FlickerSubj{4}); % Both Controls
[~,SubjG{5},~]=intersect(SubjID,FlickerSubj{5}); % BothControlsRT
[~,SubjG{6},~]=intersect(SubjID,FlickerSubj{6}); % LightRT
%% Set Trial Groups (Hit, Miss, Etc.)
ChCount=[];
ChIndList={};
TrialType{1}=1; %%%%%Hit trial
TrialType{2}=0; %%%%%Miss trial
TrialType{3}=[0 1]; %%%%%All trial
% TrialType{4}=-1; %%%%%Premature trial
TrialTypeName{1}='Hit'; %%%%%Hit trial
TrialTypeName{2}='Miss'; %%%%%Miss trial
TrialTypeName{3}='HitAndMiss'; %%%%%All trial
% TrialTypeName{4}='Premature'; %%%%%Premature trial
%% PSD parameters
psdParameter.Fs=512;
psdParameter.window=1024; % can increase window to 1024 from 512 8/18/24 - less smoth, but higher res
psdParameter.noverlap = psdParameter.window/2; % can change overlap to 256 or half window size
psdParameter.nfft=1024; % decrease nfft from 1024 to 512 - less smooth but decrease resolution
nFre=psdParameter.nfft/2+1; % modified pwelch see Lu genmat code on git
%% Epoch, Samp Rate, Make Save Trial Folder
% (When is data epoched? What if data is already epoched from preprocessing)
DataTimeRange=[-4 1]; %%%4 seconds before color-change and 1s after.
AnaRange=[-4 0]; %%4s before color-change
% AnaRange=[0 1]; %%1s after color change
SampRate=512;
SampI=(AnaRange-DataTimeRange(1))*SampRate;
SampI=SampI(1)+1:SampI(2);
% clear CohGroup
parfor iFile=1:length(EEGList)
ChTempN(iFile)=size(EEGList{iFile}.AllChData,1);
end
SavePath = ['4_COH\' currDate '_' scriptName '\'];
if ~exist(SavePath, 'dir')
mkdir(SavePath)
end
%% Get coherence for all pairs of channels PER SUBJECT!
% This takes a looong time! (Data from here goes in TrialCrossSpec)
% SaveTrialSubj='Y:\singer\LuZhang\Project6-EEG\Results\Step2-COH\TrialCrossSpec\';
SaveTrialSubj=['4_COH\' currDate '_' scriptName '\TrialCrossSpec\'];
mkdir(SaveTrialSubj);
parpool(12)
tic
for iFile=1:length(EEGList)
SaveTemp=[SaveTrialSubj EEGList{iFile}.filename(1:4) '\'];
mkdir(SaveTemp);
if ~isempty(EEGList{iFile})
ChTempN=size(EEGList{iFile}.AllChData,1);
TrialTypeTemp=EEGList{iFile}.TrialType;
TrialI=[];
%% Calculate for pair of channels
tic
for iCh=1:ChTempN
for jCh=iCh:ChTempN
clear TempTrial1 tempSig1 TempTrial2 tempSig2 TrialSpec
tempSig1=squeeze(EEGList{iFile}.AllChData(iCh,SampI,:));
tempSig2=squeeze(EEGList{iFile}.AllChData(jCh,SampI,:));
if sum(sum(isnan(tempSig1)))>1||sum(sum(isnan(tempSig2)))>1
continue
end
% tic
for iTrial=1:length(TrialTypeTemp) % loop by trial
if length(TrialTypeTemp)>1
TempTrial1(iTrial).Data=tempSig1(:,iTrial);
TempTrial2(iTrial).Data=tempSig2(:,iTrial);
else
TempTrial1(iTrial).Data=tempSig1;
TempTrial2(iTrial).Data=tempSig2;
end
TempTrial1(iTrial).Time=([1:length(TempTrial1(iTrial).Data)]-1)/512;
TempTrial2(iTrial).Time=([1:length(TempTrial2(iTrial).Data)]-1)/512;
end
% clear TrialSpec
%%%Old version to calculate CrossSpec,tested equal to
%%%new version
% [TrialSpec1.Sxy,TrialSpec1.Sxx,TrialSpec1.Syy,TrialSpec1.w,TrialSpec1.options,ValidIndex]=coh_TrialData(TempTrial1,TempTrial2,psdParameter);
%%%Old version to calculate CrossSpec,tested equal to
% %%%new version
% psdParameter.noverlap=500;
% psdParameter.nfft=512;
% psdParameter.window=512;
% nFre=psdParameter.nfft/2+1;
[TrialSpec.Sxy,TrialSpec.Sxx,TrialSpec.Syy,TrialSpec.w,TrialSpec.options,ValidIndex]=crossspec_EqualTriL(TempTrial1,TempTrial2,psdParameter); %% find in genmat code
save([SaveTemp 'Ch' num2str(iCh) 'Ch' num2str(jCh) '.mat'],'TrialSpec','ValidIndex','psdParameter');
% a=crossspec_Trial(TrialSpec);
% figure;
% plot(a.Fre,abs(((a.wpli))))
% figure;
% plot(a.Fre,abs(mean((a.wpli(1:30,:)))))
% figure;
% plot(a.Fre,abs(mean((a.wpli(1:30,:)))))
% figure;
% plot(TempTrial1(4).Data);hold on;plot(TempTrial2(4).Data,'r.')
for iTrialType=1:length(TrialType) % group: hit, miss, etc.
TrialI=[];
parfor j=1:length(TrialType{iTrialType})
TrialI=union(TrialI,find(TrialTypeTemp==TrialType{iTrialType}(j)));
end
TrialI=intersect(TrialI,ValidIndex);
if ~isempty(TrialI)
% CohGroup{iGroup,iFile}{iCh,jCh}=Coh_TrialIndex(TrialSpec1,TrialI);
CohGroup{iTrialType,iFile}{iCh,jCh}=crossspec_TrialIndex(TrialSpec,TrialI);
%% %confirmed Old and New version of Cross-Spectrum results in same coherence results.
% D1=Coh_TrialIndex(TrialSpec1,TrialI);
% D2=crossspec_TrialIndex(TrialSpec,TrialI);
% figure;
% plot(D1.Fre,(D1.Cxy));hold on;
% plot(D2.Fre,(D2.Cxy),'r.');hold on;c
%%%confirmed Old and New version of Cross-Spectrum results in same coherence results.
end
end
% toc
%% figure;
% Temp=CohGroup{1,iFile}{iCh,jCh};
% subplot(2,1,1)
% plot(Temp.Fre,(Temp.Cxy));
% subplot(2,1,2)
%
% plot(Temp.Fre,log(abs(Temp.Pxx)));
% hold on;
% plot(Temp.Fre,log(abs(Temp.Pyy)));
end
end
toc
end
end
toc
% Check if everything above works ***
%% Save workspace to be used for Step 5: WPLITrial Group. This step takes a long time, creates 20gb file!
COHSaveFileName =['COHdata_forWPLITrialType_' currDate '_' scriptName '.mat'];
COH_Save_Path = [SavePath COHSaveFileName];
save(COH_Save_Path,'-v7.3') % Check this
% load(COH_Save_Path) % loading should not be necessary as all variables in workspace should be in that save file
%% For visualization - Set-up - Must run before plotting anything below
% (of what? COH,WPLI and PSD?)
load('chanPosColin27');
%
Fre=CohGroup{1,1}{1,2}.Fre; % may need to import CohGroup from a COHdata file
FBand=[2 100]; % consider changing [1 100] to [2 100] due to normalization
FreInd=find(Fre>=FBand(1)&Fre<=FBand(2));
Fplot=Fre(FreInd);
PSDall=zeros(length(FileStruct),length(FreInd),ChNTotal,length(TrialType))+nan;
COHall=zeros(length(FileStruct),length(FreInd),ChNTotal,ChNTotal-1,length(TrialType))+nan;
WPLIall=zeros(length(FileStruct),length(FreInd),ChNTotal,ChNTotal-1,length(TrialType))+nan;
%
for iTrialType=1:length(TrialType)
for iFile=1:length(FileStruct)
for iCh=1:size(CohGroup{iTrialType,iFile},1)
for jCh=iCh+1:size(CohGroup{iTrialType,iFile},2)
if isempty(CohGroup{iTrialType,iFile}{iCh,jCh})
continue;
end
if iCh==1
PSDall(iFile,:,iCh,iTrialType)=CohGroup{iTrialType,iFile}{iCh,jCh}.Pxx(FreInd);
PSDall(iFile,:,jCh,iTrialType)=CohGroup{iTrialType,iFile}{iCh,jCh}.Pyy(FreInd);
end
COHall(iFile,:,iCh,jCh,iTrialType)=CohGroup{iTrialType,iFile}{iCh,jCh}.Cxy(FreInd);
WPLIall(iFile,:,iCh,jCh,iTrialType)=CohGroup{iTrialType,iFile}{iCh,jCh}.wpli(FreInd);
end
end
end
end
%% Peak Alpha WPLI Distribution Histogram
% Get all alpha values
alphaLowerLimitFreqHz = 8;
alphaUpperLimitFreqHz = 13;
% Find indices of values in Fre between 8 and 13 (inclusive)
allAlphaFreIndices = find(Fre >= alphaLowerLimitFreqHz & Fre <= alphaUpperLimitFreqHz);
HitMissTrialType = 3;
WPLIallAlpha = squeeze(WPLIall(:,allAlphaFreIndices,1:32,1:32,HitMissTrialType)); % size(WPLIall) ans = 67 197 40 39 3- 67subs x allFreqs x iCh x jCh x TrialType
% Find the peak value within alpha (dimension 2)
[peakAlphaValues, peakAlphaIndices] = max(WPLIallAlpha, [], 2);
% Reshape the result to 3D
PeakWPLIallAlpha = squeeze(peakAlphaValues);
%% Plot distribution of PeakWPLIallAlpha
% Flatten PeakWPLIallAlpha to 1D for distribution analysis
PeakWPLIallAlphaFlat = PeakWPLIallAlpha(:);
% Remove NaN values
PeakWPLIallAlphaFlat = PeakWPLIallAlphaFlat(~isnan(PeakWPLIallAlphaFlat));
% Calculate the total number of data points (channel pairs)
nDataPoints = numel(PeakWPLIallAlphaFlat);
% Compute top percentiles
top25Percent = prctile(PeakWPLIallAlphaFlat, 75);
top10Percent = prctile(PeakWPLIallAlphaFlat, 90);
top5Percent = prctile(PeakWPLIallAlphaFlat, 95);
top1Percent = prctile(PeakWPLIallAlphaFlat, 99);
top0_1Percent = prctile(PeakWPLIallAlphaFlat, 99.9);
top0_01Percent = prctile(PeakWPLIallAlphaFlat, 99.99);
% Count data points greater than each percentile
countAbove25Percent = sum(PeakWPLIallAlphaFlat > top25Percent);
countAbove10Percent = sum(PeakWPLIallAlphaFlat > top10Percent);
countAbove5Percent = sum(PeakWPLIallAlphaFlat > top5Percent);
countAbove1Percent = sum(PeakWPLIallAlphaFlat > top1Percent);
countAbove0_1Percent = sum(PeakWPLIallAlphaFlat > top0_1Percent);
countAbove0_01Percent = sum(PeakWPLIallAlphaFlat > top0_01Percent);
% Display the results
fprintf('Top 25%% WPLI value: %.4f, Data points above: %d\n', top25Percent, countAbove25Percent);
fprintf('Top 10%% WPLI value: %.4f, Data points above: %d\n', top10Percent, countAbove10Percent);
fprintf('Top 5%% WPLI value: %.4f, Data points above: %d\n', top5Percent, countAbove5Percent);
fprintf('Top 1%% WPLI value: %.4f, Data points above: %d\n', top1Percent, countAbove1Percent);
fprintf('Top 0.1%% WPLI value: %.4f, Data points above: %d\n', top0_1Percent, countAbove0_1Percent);
fprintf('Top 0.01%% WPLI value: %.4f, Data points above: %d\n', top0_01Percent, countAbove0_01Percent);
% Plot the histogram of PeakWPLIallAlpha
figure;
histogram(PeakWPLIallAlphaFlat, 'Normalization', 'probability', 'BinWidth', 0.02);
hold on;
% Add vertical lines for top percentiles
xline(top25Percent, '--k', 'Top 25%', 'LineWidth', 1.5);
xline(top10Percent, '--c', 'Top 10%', 'LineWidth', 1.5);
xline(top5Percent, '--r', 'Top 5%', 'LineWidth', 1.5);
xline(top1Percent, '--g', 'Top 1%', 'LineWidth', 1.5);
xline(top0_1Percent, '--b', 'Top 0.1%', 'LineWidth', 1.5);
xline(top0_01Percent, '--m', 'Top 0.01%', 'LineWidth', 1.5);
% Label the axes
xlabel('WPLI');
ylabel('Fraction');
% Add a title including the total number of data points
title(['Distribution of Peak Alpha WPLI (Total data points: ', num2str(nDataPoints), ')']);
% Improve plot appearance
grid on;
% % Display the size of the resulting array
% disp('Size of the resulting 3D array:');
% disp(size(PeakWPLIallAlpha));
%% Plot the histogram of PeakWPLIallAlpha (SuppFig4A MS version)
% May need to run the previous section first! MKA 2025-03-18
figure;
histogram(PeakWPLIallAlphaFlat, 'Normalization', 'probability', 'BinWidth', 0.02);
hold on;
% Add vertical lines for top percentiles
xline(top25Percent, '--k', 'Top 25%', 'LineWidth', 1.5);
% Label the axes
xlabel('WPLI');
ylabel('Fraction');
% Add a title including the total number of data points
title(['Distribution of Peak Alpha WPLI (Total data points: ', num2str(nDataPoints), ')']);
% Improve plot appearance
grid on;
saveas(gcf, fullfile('Fig4Panels', 'SuppFig4A_PeakWPLI_Alpha.svg'));
%% Peak Alpha WPLI 40Hz vs Light ALL CHANNELS (not used) - search "stats preceding fig4D" for signif channels only
% Find the peak WPLI value within alpha (dimension 2)
[peakAlphaValues, peakAlphaIndices] = max(WPLIallAlpha, [], 2, "includemissing");
alphaFreqHz = 8:.5:13;
% Initialize a copy of peakAlphaIndices
peakAlphaIndicesNaN = peakAlphaIndices;
% If all alpha WPLI values are NaN, then set the max index to NaN
peakAlphaIndicesNaN(all(isnan(WPLIallAlpha),2)) = NaN;
% Reshape the result to 3D
PeakAlphaIndicesNaN3D = squeeze(peakAlphaIndicesNaN);
% % Check with single participant
% singleparticiantAllChPairPeakAlpha = squeeze(PeakAlphaIndicesNaN3D(1,:,:))
%
% % Convert Indices to Correct Corresponding Frequnecy (Hz)
% % Find valid indices (values between 1 and 11)
% validIdx = singleparticiantAllChPairPeakAlpha >= 1 & singleparticiantAllChPairPeakAlpha <= 11;
%
% % Initialize the output array with NaN, preserving original shape
% singleallchpfreqs = NaN(size(singleparticiantAllChPairPeakAlpha));
%
% % Perform mapping only for valid indices
% singleallchpfreqs(validIdx) = alphaFreqHz(singleparticiantAllChPairPeakAlpha(validIdx));
% Convert Indices (3D) to Correct Corresponding Frequncy (Hz)
validIdx = PeakAlphaIndicesNaN3D >= 1 & PeakAlphaIndicesNaN3D <= 11;
% Initialize the output array with NaN, preserving original shape
PeakAlphaFreqs = NaN(size(PeakAlphaIndicesNaN3D));
% Perform mapping only for valid indices
PeakAlphaFreqs(validIdx) = alphaFreqHz(PeakAlphaIndicesNaN3D(validIdx));
% singleallchpfreqs = alphaFreqHz(singleparticiantAllChPairPeakAlpha)
GroupSubjs_40Hz = 1; % 40Hz group
GroupSubjs_Light = 6; % LightRT group
% % Extract Peak Alpha WPLI into groups: 40 Hz & Light
% % PeakWPLIallAlpha: 67 subs x 32ch x 32ch
% PeakAlphaWPLI_40HzGroup = PeakWPLIallAlpha(SubjG{GroupSubjs_40Hz},:,:);
% PeakAlphaWPLI_LightGroup = PeakWPLIallAlpha(SubjG{GroupSubjs_Light},:,:);
% % single particiapn
% singledudeWPLIallpair = squeeze(PeakWPLIallAlpha(1,:,:))
% Extract Peak Alpha WPLI freq into groups: 40 Hz & Light
% PeakWPLIallAlpha: 67 subs x 32ch x 32ch
PeakAlphaFreqs_40HzGroup = PeakAlphaFreqs(SubjG{GroupSubjs_40Hz},:,:);
PeakAlphaFreqs_LightGroup = PeakAlphaFreqs(SubjG{GroupSubjs_Light},:,:);
% Get average peak alpha WPLI frequncy for each group for all channel pairs
MeanPeakAlphaFreq_40Hz = squeeze(mean(PeakAlphaFreqs_40HzGroup, 1));
MeanPeakAlphaFreq_Light = squeeze(mean(PeakAlphaFreqs_LightGroup, 1));
% Display average peak alpha frequency tables (32x32)
ChannelLabels = {'Fp1', 'AF3', 'F7', 'F3', 'FC1', 'FC5', 'T7', 'C3', 'CP1', 'CP5', ...
'P7', 'P3', 'Pz', 'PO3', 'O1', 'Oz', 'O2', 'PO4', 'P4', 'P8', ...
'CP6', 'CP2', 'C4', 'T8', 'FC6', 'FC2', 'F4', 'F8', 'AF4', 'Fp2', 'Fz', 'Cz'};
fprintf('\nMean Peak Alpha Frequency (40Hz Group):\n');
disp(array2table(MeanPeakAlphaFreq_40Hz, 'VariableNames', ChannelLabels, 'RowNames', ChannelLabels));
fprintf('\nMean Peak Alpha Frequency (Light Group):\n');
disp(array2table(MeanPeakAlphaFreq_Light, 'VariableNames', ChannelLabels, 'RowNames', ChannelLabels));
% Flatten both 32 x 32 arrays into two 1D-arrays (492 channel pairs each)
UpperTriIdx = find(triu(ones(32, 32), 1)); % Indices of upper triangular elements
FlattenedAlphaFreq_40Hz = MeanPeakAlphaFreq_40Hz(UpperTriIdx);
FlattenedAlphaFreq_Light = MeanPeakAlphaFreq_Light(UpperTriIdx);
% Compute average peak alpha frequency across all channel pairs for each group
AvgPeakAlphaFreq_40Hz = mean(FlattenedAlphaFreq_40Hz);
AvgPeakAlphaFreq_Light = mean(FlattenedAlphaFreq_Light);
fprintf('\nAverage Peak Alpha Frequency Across All Channel Pairs:\n');
fprintf('40Hz Group: %.4f Hz\n', AvgPeakAlphaFreq_40Hz);
fprintf('Light Group: %.4f Hz\n', AvgPeakAlphaFreq_Light);
% Test for normality (to decide if t-test or ranksum)
[H_40Hz, p_40Hz] = kstest(FlattenedAlphaFreq_40Hz);
[H_Light, p_Light] = kstest(FlattenedAlphaFreq_Light);
fprintf('\nNormality Test Results:\n');
fprintf('40Hz Group: H = %d, p = %.16f\n', H_40Hz, p_40Hz);
fprintf('Light Group: H = %d, p = %.16f\n', H_Light, p_Light);
% Decide on statistical test
if H_40Hz == 0 && H_Light == 0
% Normally distributed: Use independent t-test
[h_ttest, p_ttest] = ttest2(FlattenedAlphaFreq_40Hz, FlattenedAlphaFreq_Light);
test_used = 't-test';
p_value = p_ttest;
else
% Non-normally distributed: Use Wilcoxon rank-sum test
[p_ranksum, h_ranksum] = ranksum(FlattenedAlphaFreq_40Hz, FlattenedAlphaFreq_Light);
test_used = 'Wilcoxon rank-sum test';
p_value = p_ranksum;
end
% Display results
fprintf('Statistical Test Used: %s\n', test_used);
fprintf('p-value: %.16f\n', p_value);
% Perform one-sided Wilcoxon rank-sum test
[p_ranksum_right, h_ranksum_right] = ranksum(FlattenedAlphaFreq_40Hz, FlattenedAlphaFreq_Light, 'tail', 'right');
[p_ranksum_left, h_ranksum_left] = ranksum(FlattenedAlphaFreq_40Hz, FlattenedAlphaFreq_Light, 'tail', 'left');
% Display results
fprintf('\nOne-Sided Wilcoxon Rank-Sum Test Results:\n');
fprintf('H0: 40Hz <= Light | p-value (right-tailed, 40Hz > Light): %.16f\n', p_ranksum_right);
fprintf('H0: 40Hz >= Light | p-value (left-tailed, 40Hz < Light): %.16f\n', p_ranksum_left);
% Create Violin plots showing distribution
% Create figure
figure;
hold on;
% Combine data for violin plot
groupLabels = [repmat({'40Hz'}, length(FlattenedAlphaFreq_40Hz), 1); ...
repmat({'Light'}, length(FlattenedAlphaFreq_Light), 1)];
data = [FlattenedAlphaFreq_40Hz; FlattenedAlphaFreq_Light];
% Create violin plot
violinplot(data, groupLabels);
% Format plot
title('Violin Plot of Peak Alpha Frequency');
ylabel('Peak Alpha Frequency (Hz)');
xlabel('Group');
ylim([8 13]); % Set y-axis range from 8 Hz to 13 Hz
grid on;
hold off;
%% PSD related parameters
LogPSDraw=log(abs(PSDall));
NoiseInd=find(Fplot>=58&Fplot<=62);
NormBandI=setdiff(1:length(Fplot),NoiseInd);
PSDall=PSDall./repmat(nansum(PSDall(:,NormBandI,:,:),2),1,length(Fplot),1,1);
LogPSD=log(abs(PSDall));
PlotColor2=[1 0 0;0 0 1];
% ParamPSD.ANOVAstats='Anova';
ParamPSD.PlotType=3;
ParamPSD.SigPlot='Anova';
ParamPSD.SigPlot='Ttest';
ParamPSD.CorrName='fdr'; %%%methold for multi-compairson
ParamPSD.Q=0.1;
ParamPSD.Ytick=[0 0.002 0.004];
ParamPSD.LegendShow=0;
ParamPSD.Legend=[];
ParamPSD.TimeRepeatAnova=1;
ParamPSD.GroupRepeatAnova=0;
ParamPSD.RepeatAnova=0;
ParamPSD.TimeCol=Fplot;
ParamPSD.Paired=1;
ParamPSD.BinName='Fre';
ParamPSD.Bin=Fplot;
ParamPSD.TimeComparison=0;
ParamPSD.statisP=1; % 1 to do stats and plot. 0 will do stats, but not plot, will be faster. Uses R. If error, set as 0
ParamPSD.Ytick=[-8:4:0];
ParamPSD.Crit_p=0.05;
% One color for ea of the 3 groups. Blue for 40, Gold/yellow/orange for Light, Red for Random
FlickerColor=[31 125 184; 219 129 50; 150 27 27]/255;
FlickerColor=[31 125 184; 150 27 27; 219 129 50]/255; %40, Random, Light
% FlickerColor=[0.5 0.5 0.5;0.9 0.1 0.3];
load('Functions\GenMatCode-main\Plotfun\Color\colorMapPN.mat')
load('Functions\GenMatCode-main\Plotfun\Color\colorMapPNraw.mat')
%% WPLI - Weight Phase Lag Index - Parameters
ParamWPLI=ParamPSD;
ParamWPLI.Ytick= [0:0.1:0.2]; %#ok<NBRAK2>
SubSaveWPLI=[SavePath 'WPLI\'];
ParamWPLI.SigPlot='Anova';
mkdir(SubSaveWPLI)
ParamWPLI.statisP=1;
P.xLeft=0.01; %%%%%%Left Margin
P.xRight=0.01; %%%%%%Right Margin
P.yTop=0.01; %%%%%%Top Margin
P.yBottom=0.01; %%%%%%Bottom Margin
P.xInt=0.005; %%%%%%Width-interval between subplots
P.yInt=0.005; %%%%%%Height-interval between subplots
%% WPLI - Weight Phase Lag Index - Calculation *** Fig4b - this takes a long time
WPLIStimGroupIndices = [1 3 6]; % 1=40, 3=Random, 6=LightRT
SubjGWPLI = SubjG(WPLIStimGroupIndices); % Which three groups to include
GroupNameWPLI = GroupName(WPLIStimGroupIndices);
todayDate = datestr(now, 'yymmdd');
for iTrialType=3%1:length(TrialType)
SaveTemp=[SubSaveWPLI todayDate '\' TrialTypeName{iTrialType} '\'];
mkdir(SaveTemp)
SubSaveFig=[SaveTemp 'Chan\'];
mkdir(SubSaveFig)
% CH-Ch WPLI plot.tif figure;
iPlot=0;
alphaPeakAmplitudeList = zeros(length(EEGchInd),length(EEGchInd),length(SubjGWPLI));
alphaPeakFrequencyList = zeros(length(EEGchInd),length(EEGchInd),length(SubjGWPLI));
% alphaPeakAmplitudeListEmpty = double.empty(length(EEGchInd),length(EEGchInd),length(SubjG),0)
for iCh=1:length(EEGchInd)
for jCh=iCh+1:length(EEGchInd)
clear DataPlot
for iStimGroup=1:length(SubjGWPLI)
DataPlot{iStimGroup}= squeeze(WPLIall(SubjGWPLI{iStimGroup},:,EEGchInd(iCh),EEGchInd(jCh),iTrialType));
Invalid=isnan(DataPlot{iStimGroup}(:,1));
DataPlot{iStimGroup}(Invalid,:)=[];
end
if isempty(DataPlot{1})||isempty(DataPlot{2})
continue;
end
iPlot=iPlot+1;
subplotLU(length(EEGchInd),length(EEGchInd),iCh,jCh,P);
ParamWPLI.PathSave=[SaveTemp 'Light40Rand' EEGch{iCh} '-' EEGch{jCh}];
% [~,COHComStatis{iCom,iCh,jCh}]=RateHist_GroupPlot(Fplot,DataPlot,FlickerColor,ParamCOH);
tic
RateHist_GroupPlot(Fplot,DataPlot,FlickerColor,ParamWPLI);
% toc
text(50,ParamWPLI.Ytick(end),[EEGch{iCh} '-' EEGch{jCh}]);
set(gca,'xlim',FBand,'xtick',[0:20:120],'xticklabel',[],'yticklabel',[]);
end
end
%% Permutation to set WPLI threshold ***fig4bc
% Create "significant" WPLI threhold curve using permutation of current channel pair data.
% -MKA 2024-12-11
%Set up save folder
saveDate = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
SaveTemp=[SaveTrialSubj 'WPLIPermutation_' saveDate '\'];
mkdir(SaveTemp);
% Define parameters
nPermutations = 10000; % Number of permutations, 10k or 1mil
nSubjs = length(SubjID); % total number of subjects in analysis
% Define the frequency range of interest: 2-55Hz and every half frequency inbetween
% frequencies = [2:0.5:55]; % start at 1 or 2 hz? Cut off before 60 Hz
% Pre-allocate storage for maximum WPLI curves across frequencies
% WPLIperms = zeros(nPermutations, length(frequencies));
% WPLIallPerm=zeros(length(FileStruct),length(FreInd),ChNTotal,ChNTotal-1,length(TrialType))+nan;
%% Preallocate CohGroupPerm as a cell array
CohGroupPerm = cell(3, nPermutations);
% Begin permutations
tStartPerm = tic;
for iPermutation = 1:nPermutations
% Step 1: Randomly select two subjects and a channel pair
randSubjs = randperm(nSubjs, 2); %randomly choose a random Subject A and Subject B from all three groups.
SubAInd = randSubjs(1);
SubAData = EEGList{1,SubAInd};
SubBInd = randSubjs(2);
SubBData = EEGList{1,SubBInd};
randChs = randperm(32,2); %randomly choose a chan Chi from subject i, Chj for subject j
Chi = randChs(1);
Chj = randChs(2);
% Step 2: Find the lower number of trials between the two selected subjects
nTrials_SubA = SubAData.trials;
nTrials_SubB = SubBData.trials;
minTrials = min(nTrials_SubA, nTrials_SubB);
minTrialsIndex = 1:minTrials;
% Step 3: Get `minTrials` from each subject
clear TempTrial1 DataA TempTrial2 DataB TrialSpec
DataA=squeeze(SubAData.AllChData(Chi,SampI,minTrialsIndex)); % analog to "tempSigA"
DataB=squeeze(SubBData.AllChData(Chj,SampI,minTrialsIndex)); % analog to "tempSigB"
%% Step 4: Calculate WPLI for selected trials
% WPLIperms(iPermuation, :) = calculateWPLI(DataA, DataB);
% Preallocate TempTrial1 and TempTrial2 as structure arrays "FOR SPEED"
TempTrial1(minTrials).Data = []; % Preallocate Data field
TempTrial1(minTrials).Time = []; % Preallocate Time field
TempTrial2(minTrials).Data = []; % Preallocate Data field
TempTrial2(minTrials).Time = []; % Preallocate Time field
for iTrial=1:minTrials % loop by trial
if minTrials>1
TempTrial1(iTrial).Data=DataA(:,iTrial);
TempTrial2(iTrial).Data=DataB(:,iTrial);
else
TempTrial1(iTrial).Data=DataA;
TempTrial2(iTrial).Data=DataB;
end
TempTrial1(iTrial).Time=([1:length(TempTrial1(iTrial).Data)]-1)/512;
TempTrial2(iTrial).Time=([1:length(TempTrial2(iTrial).Data)]-1)/512;
end
[TrialSpec.Sxy,TrialSpec.Sxx,TrialSpec.Syy,TrialSpec.w,TrialSpec.options,ValidIndex]=crossspec_EqualTriL(TempTrial1,TempTrial2,psdParameter); %% find in genmat code
% save([SaveTemp 'Ch' num2str(Chi) 'Ch' num2str(Chj) '.mat'],'TrialSpec','ValidIndex','psdParameter');
jTrialType = iTrialType;
for jTrialType=3%1:length(TrialType) % group: hit, miss, etc.
%iTrialType hardcoded to 3; 3=Hit&Miss (all trials)
TrialI=[];
parfor j=1:length(TrialType{jTrialType}) % starting pool takes time; does this need to be parfor?
TrialI=union(TrialI,find(TrialTypeTemp==TrialType{jTrialType}(j)));
end
TrialI=intersect(TrialI,ValidIndex);
if ~isempty(TrialI)
% CohGroup{iGroup,iFile}{iCh,jCh}=Coh_TrialIndex(TrialSpec1,TrialI);
CohGroupPerm{iPermutation}=crossspec_TrialIndex(TrialSpec,TrialI);
% CohGroup -> {3x67 cell} -> {3 trialTypes x 67 subjects}
end
end
end
elapsedTime = toc(tStartPerm);
disp(['Elapsed time for script: ', num2str(elapsedTime), ' seconds']);
%Save Permuation variable
filename = [nPermutations 'CohGroupPerm_', datestr(now, 'yyyy-mm-dd'), '.mat'];
save(filename, 'CohGroupPerm');
%% Get WPLI from coherence
Fre=CohGroupPerm{1}.Fre; % may need to import CohGroup from a COHdata file
FBand=[2 100]; % consider changing [1 100] to [2 100] due to normalization
FreInd=find(Fre>=FBand(1)&Fre<=FBand(2));
Fplot=Fre(FreInd);
PSDallPerm=zeros(length(FileStruct),length(FreInd),ChNTotal,length(TrialType))+nan;
COHallPerm=zeros(length(FileStruct),length(FreInd),ChNTotal,ChNTotal-1,length(TrialType))+nan;
WPLIallPerm=zeros(length(FileStruct),length(FreInd),ChNTotal,ChNTotal-1,length(TrialType))+nan;
for iPermutation = 1:nPermutations
if isempty(CohGroupPerm{iPermutation})
continue;
end
if iCh==1
PSDallPerm(iPermutation,:,iCh)=CohGroupPerm{iPermutation}.Pxx(FreInd);
PSDallPerm(iPermutation,:,jCh)=CohGroupPerm{iPermutation}.Pyy(FreInd);
end
COHallPerm(iPermutation,:,iCh,jCh)=CohGroupPerm{iPermutation}.Cxy(FreInd);
WPLIallPerm(iPermutation,:,iCh,jCh)=CohGroupPerm{iPermutation}.wpli(FreInd);
end
end
%% Step 5: Determine the significance threshold for each frequency
% EXTRACT relevant data for wpli threshold
HitMissDataset = CohGroupPerm(3,:);
% Initialize an output cell array of the same size.
extractedStructs = cell(1, numel(HitMissDataset));
% Extract the (31, 32) cell data from each cell in CohGroupPerm.
extractedStructs = cellfun(@(x) x{31, 32}, HitMissDataset, 'UniformOutput', false);
% Convert the extracted 'wpli' data to a 10,000x513 double matrix.
wpliMatrix = cell2mat(cellfun(@(x) x.wpli, extractedStructs, 'UniformOutput', false)');
wpliFreqs = extractedStructs{1,1}.Fre;
% FIND WPLI threshold (value_of_topk)
% Sort each column of wpliMatrix in descending order
sortedMatrix = sort(wpliMatrix, 1, 'descend');
% Extract the top_kth greatest value from each column
p_value_threshold = 0.01; % p-val Significance threshold analogus to alpha value of 0.01
top_0_01 = ceil(nPermutations * p_value_threshold); % Top K for p-value cutoff
value_of_top0_01 = sortedMatrix(top_0_01, :);
p_value_threshold2 = 0.001; % p-val Significance threshold analogus to alpha value of 0.01
top_0_001 = ceil(nPermutations * p_value_threshold2); % Top K for p-value cutoff
value_of_top0_001 = sortedMatrix(top_0_001, :);
p_value_threshold3 = 0.0001; % p-val Significance threshold analogus to alpha value of 0.01
top_0_0001 = ceil(nPermutations * p_value_threshold3); % Top K for p-value cutoff
value_of_top0_0001 = sortedMatrix(top_0_0001, :);
%% Step 6: Plot permutation WPLI threshold curve
% Plot wpliFreqs (x-axis) against value100 (y-axis)
figure;
hold on;
upperFreqLimit = 55; % Upper frequency limit of plot in (hz). Otherwise plot will go to >250 Hz
upperFreqIndex = find(wpliFreqs >= upperFreqLimit, 1, 'first');
% Plot the first line
plot(wpliFreqs, value_of_top0_01, 'LineWidth', 2, 'DisplayName', ...
['p = ' num2str(p_value_threshold) ', Top K = ' num2str(top_0_01)]);
% Plot the second line
plot(wpliFreqs, value_of_top0_001, 'LineWidth', 2, 'DisplayName', ...
['p = ' num2str(p_value_threshold2) ', Top K = ' num2str(top_0_001)]);
% Plot the third line
plot(wpliFreqs, value_of_top0_0001, 'LineWidth', 2, 'DisplayName', ...
['p = ' num2str(p_value_threshold3) ', Top K = ' num2str(top_0_0001)]);
% Add a legend
legend('show', 'Location', 'best');
% Add axis labels and title
xlabel('Frequency (Hz)', 'FontSize', 12);
ylabel('WPLI', 'FontSize', 12);
title([num2str(size(wpliMatrix, 1)) ' Permutations. Threshold Curves for Different p-Values'], 'FontSize', 14);
% Set y-axis to start at zero
xlim([0 55])
% ylim([0, max(max(value_of_top0_01(1:upperFreqIndex)), max(value_of_top0_001(1:upperFreqIndex)), max(value_of_top0_0001(1:upperFreqIndex)))]);
ylim([0, max([max(value_of_top0_01(1:upperFreqIndex)), ...
max(value_of_top0_001(1:upperFreqIndex)), ...
max(value_of_top0_0001(1:upperFreqIndex))])]);
% plot(wpliFreqs(1:index), value_of_topk(1:index), 'LineWidth', 2);
% plot(wpliFreqs(1:index), value_of_topk2(1:index), 'LineWidth', 2);
%
% legend()
%
% % Add axis labels and title
% xlabel('Frequency (Hz)', 'FontSize', 12);
% ylabel('WPLI', 'FontSize', 12);
% title([num2str(nPermutations) ' permutations. Top ' num2str(top_k) ' WPLI value. pvalue threshold of ' num2str(p_value_threshold)], 'FontSize', 14);
%
% % Set y-axis to start at zero
% ylim([0, max(value_of_topk(1:index))]);
grid on; % improve readability of plot
hold off;
% wpli_threshold_curve = zeros(32, 32, length(frequencies));
% for freq_idx = 1:length(frequencies)
% % Sort WPLI values for the current frequency across all permutations
% sorted_values = sort(perm_idx_WPLICurve(:, freq_idx), 'descend');
%
% % Find the WPLI value corresponding to the top `top_k` value
% wpli_threshold_curve(iCh, jCh, freq_idx) = sorted_values(top_k);
% end
%% Real > Permutated WPLI
% get real WPLI - from
load('10kCohGroupPerm_2024-12-17.mat')
upperFreqLimit = 55; % Upper frequency limit of plot in (hz). Otherwise plot will go to >250 Hz
upperFreqIndex = find(wpliFreqs >= upperFreqLimit, 1, 'first');
lowerFreqLimit = 2;
lowerFreqIndex = find(wpliFreqs >= lowerFreqLimit, 1, 'first');
averagePermutatedWPLI_0to55_top0_01 = mean(value_of_top0_01(1:upperFreqIndex), 'omitnan');
averagePermutatedWPLI_0to55_top0_001 = mean(value_of_top0_001(1:upperFreqIndex), 'omitnan');
averagePermutatedWPLI_0to55_top0_0001 = mean(value_of_top0_0001(1:upperFreqIndex), 'omitnan');
averagePermutatedWPLI_2to55_top0_01 = mean(value_of_top0_01(lowerFreqIndex:upperFreqIndex), 'omitnan');
averagePermutatedWPLI_2to55_top0_001 = mean(value_of_top0_001(lowerFreqIndex:upperFreqIndex), 'omitnan');
averagePermutatedWPLI_2to55_top0_0001 = mean(value_of_top0_0001(lowerFreqIndex:upperFreqIndex), 'omitnan');
permutatedWPLI = value_of_top0_001;
%% Initialize the average WPLI storage
AvgRealWPLI = cell(length(EEGchInd), length(EEGchInd)); % Cell array for all channel pairs
% AvgRealWPLI is a 32x32 cell containing 3x197 doubles (average WPLI per stim group (3) per freq (197)
% Loop through all channel pairs 32x32
for iCh = 1:length(EEGchInd)
for jCh = iCh+1:length(EEGchInd) % Avoid duplicates and diagonal (upper triangle)
% Initialize a 2D array to store averages for this channel pair
AvgRealWPLI{iCh, jCh} = zeros(length(SubjGWPLI), size(WPLIall, 2)); % Rows: StimGroups, Cols: Frequencies
% Loop through stimulus groups
for iStimGroup = 1:length(SubjGWPLI)
% Extract WPLI data for this stimulus group and channel pair
DataPlot = squeeze(WPLIall(SubjGWPLI{iStimGroup}, :, EEGchInd(iCh), EEGchInd(jCh), iTrialType));
% Remove invalid rows containing NaN
Invalid = isnan(DataPlot(:, 1));
DataPlot(Invalid, :) = [];
% Compute the average across all valid rows for this stim group
if ~isempty(DataPlot) % Ensure there is data after removing NaN
AvgRealWPLI{iCh, jCh}(iStimGroup, :) = mean(DataPlot, 1); % Average across rows
end
end
end
end
% outcome:
% AvgRealWPLI is a 32x32 cell containing 3x197 doubles (average WPLI per stim group (3) per freq (197)
%% Peak WPLI Extraction
%%% set up
BOIAlphas=[1 4 8 8 10 13 30 39.5 43;4 8 13 10 13 30 37 41.5 100];
BOI2HzAlphas=[2 4 8 8 10 13 30 39.5 43 55;4 8 13 10 13 30 37 41.5 55 100];
BOI2Hz=[2 4 8 13 30 39.5 43 55;4 8 13 30 37 41.5 55 100]; % No LOWER or UPPER ALPHA
BandName={'Delta','Theta','Alpha','LowAlpha','HighAlpha','Beta','Gamma-1','Gamma-E', 'Gamma-55','Gamma-2'};
BandHzNameAlphas={'1-4 Hz','4-8 Hz','8-13Hz','8-10Hz','10-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-55 Hz', '55-100 Hz'};
BandHzName2HzAlphas={'2-4 Hz','4-8 Hz','8-13Hz','8-10Hz','10-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-55 Hz', '55-100 Hz'};
BandHzName2HzAlphas={'2-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-55 Hz', '55-100 Hz'};
lowerboundHz = 2;
upperboundHZ = 39; % can change to 55 Hz
%%%
% $% Filter frequency bands up to upperbound Hz
BOI_filtered = BOI2Hz(:, BOI2Hz(2, :) <= upperboundHZ);
BandName_filtered = BandName(BOI2Hz(2, :) <= upperboundHZ);
BandHzName_filtered = BandHzName2Hz(BOI2Hz(2, :) <= upperboundHZ);
%
% Preallocate results (cell array for flexibility)
numBands = size(BOI_filtered, 2);
[numChannels, ~] = size(AvgRealWPLI);
peakWPLI = cell(numChannels, numChannels);
% %% TESTING ONLY - Create BOIsFreqIndices - comment when fixed
% % Example: Fre is the frequency vector (197x1 double), BOI_filtered contains the bands up to 55 Hz
% BOIsFreqIndices = cell(1, numBands);
% BOIsFreqValues = cell(1, numBands); % To store frequencies for each band
%
% % Calculate the indices and corresponding frequencies for each band
% for b = 1:numBands
% BOIsFreqIndices{b} = find(Fplot >= BOI_filtered(1, b) & Fplot <= BOI_filtered(2, b));
% BOIsFreqValues{b} = Fplot(BOIsFreqIndices{b}); % Map indices to frequencies
% end
%
% % Display the indices and their corresponding frequencies for each band
% for b = 1:numBands
% fprintf('Band: %s (%s)\n', BandName_filtered{b}, BandHzName_filtered{b});
% fprintf('Indices: %s\n', mat2str(BOIsFreqIndices{b}));
% fprintf('Frequencies (Hz): %s\n', mat2str(BOIsFreqValues{b}));
% fprintf('\n');
% end
%%% Loop over channel pairs
for iCh = 1:length(EEGchInd)
for jCh = 1:length(EEGchInd)
if isempty(AvgRealWPLI{iCh, jCh})
continue; % Skip empty cells
end
data = AvgRealWPLI{iCh, jCh}; % data: 3x197 double (stimulation groups x frequencies)
% Preallocate storage for this channel pair
peakWPLI{iCh, jCh} = zeros(size(data, 1), numBands); % stim groups x frequency bands
% Loop over bands of interest
for iBand = 1:numBands
% Find indices corresponding to the frequency band
freqIndices = Fplot >= BOI_filtered(1, iBand) & Fplot <= BOI_filtered(2, iBand);
% Get peak WPLI for each stimulation group
for group = 1:size(data, 1)
peakWPLI{iCh, jCh}(group, iBand) = max(data(group, freqIndices));
% peakWPLI: 32x32 cell of 3x5 double (3 stim groups, 5 BOIs)
end
end
end
end
% Result is stored in peakWPLI{i, j}(group, iBand), where:
% i, j = channel indices
% group = stimulation group
% iBand = frequency band index
% %% TESTING ONLY - Max WPLI Frequency per band
% % Example variables
% maxWPLIFreqHz = cell(length(EEGchInd), numChannels); % To store the frequencies of max WPLI
%
% % Loop through all channel pairs
% for i = 1:numChannels
% for j = 1:numChannels
% if isempty(AvgRealWPLI{i, j})
% continue; % Skip empty cells
% end
%
% data = AvgRealWPLI{i, j}; % 3x197 double (stimulation groups x frequencies)
% maxWPLIFreqHz{i, j} = zeros(size(data, 1), numBands); % Preallocate for groups x bands
%
% % Loop through stimulation groups
% for group = 1:size(data, 1)
% % Loop through each band
% for iBand = 1:numBands
% freqIndices = BOIsFreqIndices{iBand}; % Get indices for the band
% [~, maxIndex] = max(data(group, freqIndices)); % Find index of max value
% maxWPLIFreqHz{i, j}(group, iBand) = Fplot(freqIndices(maxIndex)); % Map to frequency in Hz
% end
% end
% end
% end
%
% % Display example output for a specific channel pair
% exampleChannelPair = [1, 2]; % Change to any valid pair
% if ~isempty(maxWPLIFreqHz{exampleChannelPair(1), exampleChannelPair(2)})
% fprintf('Max WPLI frequencies for channel pair (%d, %d):\n', exampleChannelPair(1), exampleChannelPair(2));
% for group = 1:numGroups
% fprintf(' %s:\n', GroupNameWPLI{group}); % Use the group name
% for iBand = 1:numBands
% fprintf(' Band: %s (%s) - Max WPLI at %.2f Hz\n', ...
% BandName_filtered{iBand}, BandHzName_filtered{iBand}, ...
% maxWPLIFreqHz{exampleChannelPair(1), exampleChannelPair(2)}(group, iBand));
% end
% end
% end
%%% Plot fraction of channel pairs with WPLI greater than p=0.01 permutated WPLI
% Initialize fraction of channel pairs exceeding the threshold
numGroups = 3; % Number of stimulation groups
numBands = size(BOI_filtered, 2); % Number of frequency bands
fractionExceed = zeros(numGroups, numBands);
averagePermutatedWPLI_2to55_top0_0001 = 0.0894;
% averagePermutatedWPLI_2to55_top0_001 = 0.04
% 0.1202; % top quartile threshold
averagePermutatedWPLIvalue = averagePermutatedWPLI_2to55_top0_0001; % top quartile threshold % averagePermutatedWPLI_2to55_top0_001;
% Count total non-empty cells
numChannels = size(AvgRealWPLI, 1);
totalPairs = 0;
for i = 1:numChannels
for j = 1:numChannels
if ~isempty(AvgRealWPLI{i, j})
totalPairs = totalPairs + 1;
end
end
end
% Loop through stimulation groups and frequency bands
for iGroup = 1:numGroups
for iBand = 1:numBands
exceedCount = 0;
% Loop over all channel pairs
for i = 1:numChannels
for j = 1:numChannels
if isempty(AvgRealWPLI{i, j})
continue; % Skip empty cells
end
% Check if the value for the group and band exceeds the threshold
if peakWPLI{i, j}(iGroup, iBand) > averagePermutatedWPLIvalue %
exceedCount = exceedCount + 1;
end
end
end
% Calculate the fraction for this group and band
fractionExceed(iGroup, iBand) = exceedCount / totalPairs;
end
end
%% Peak Alpha WPLI 40Hz vs Light SIGNIFICANT CH ONLY ( stats preceding fig4D stats)
%%% Groups
GroupSubjs_40Hz = 1; % 40Hz group
GroupSubjs_Light = 6; % LightRT group
%%% Get all alpha values
alphaLowerLimitFreqHz = 8;
alphaUpperLimitFreqHz = 13;
% Find indices of values in Fre between 8 and 13 (inclusive)
allAlphaFreIndices = find(Fre >= alphaLowerLimitFreqHz & Fre <= alphaUpperLimitFreqHz);
HitMissTrialType = 3;
WPLIallAlpha = squeeze(WPLIall(:,allAlphaFreIndices,1:32,1:32,HitMissTrialType)); % size(WPLIall) ans = 67 197 40 39 3- 67subs x allFreqs x iCh x jCh x TrialType
%WPLIallAlpha: (67subs, x 11 freqs x 32 x 32chs)
%%% Find the peak WPLI value within alpha (dimension 2) and the corresponding Index (correspond to
% Freq Hz) of the peak alpha WPLI value
[peakAlphaValues4D, peakAlphaIndices4D] = max(WPLIallAlpha, [], 2);
peakAlphaValues = squeeze(peakAlphaValues4D);
peakAlphaIndices = squeeze(peakAlphaIndices4D);
%%%%%%%%%%%%%% pre-process peak alpha WLPI values
% peakAlphaValues = squeeze(peakAlphaValues); % 4d to 3d
% test with single
peakAlphaValueSingleSub = squeeze(peakAlphaValues(1,:,:));
% Extract Peak Alpha WPLI values for each sub into groups: 40 Hz & Light
PeakAlphaWPLIs_40HzGroup = peakAlphaValues(SubjG{GroupSubjs_40Hz},:,:);
PeakAlphaWPLIs_LightGroup = peakAlphaValues(SubjG{GroupSubjs_Light},:,:);
% Get average peak alpha WPLI for each group for all channel pairs
MeanAlphaPeakWPLI_40Hz = squeeze(mean(PeakAlphaWPLIs_40HzGroup, 1));
MeanAlphaPeakWPLI_Light = squeeze(mean(PeakAlphaWPLIs_LightGroup, 1));
%%% Get alpha peak WPLI frequency from peakWPLI_Freq (see: %% Plot bar graph of # channels exceeding
%%% p=0.0001 WPLI threshold (New Fig4C MS) MKA 2025-02-06)
% Preallocate with NaN
peakAlphaFreq_40HzGroup = NaN(numChannels, numChannels);
peakAlphaFreq_LightGroup = NaN(numChannels, numChannels);
% Apply cellfun with error handling
validCells = ~cellfun(@isempty, peakWPLI_Freq); % Logical mask for non-empty cells
peakAlphaFreq_40HzGroup(validCells) = cellfun(@(x) x(1,3), peakWPLI_Freq(validCells));
peakAlphaFreq_LightGroup(validCells) = cellfun(@(x) x(1,3), peakWPLI_Freq(validCells));
% %%%%%%%%%%%% Get frequency value of the peak alpha WPLI
% % Initialize a copy of peakAlphaIndices
% peakAlphaIndicesNaN = peakAlphaIndices;
%
% % test with single participant
% singleSubPeakAlphaIndexAllChPairs = squeeze(peakAlphaIndices(1,:,:)); % should contain values between 1 and 11
%
% % Convert Indices to Correct Corresponding Frequnecy (Hz)
% % Find valid indices (values between 1 and 11)
% validIdx = singleSubPeakAlphaIndexAllChPairs >= 1 & singleSubPeakAlphaIndexAllChPairs <= 11;
%
% % Initialize the output array with NaN, preserving original shape
% singleallchpfreqs = NaN(size(singleSubPeakAlphaIndexAllChPairs));
%
% % Perform mapping only for valid indices
% singleallchpfreqs(validIdx) = alphaFreqHz(singleSubPeakAlphaIndexAllChPairs(validIdx));
%
% % If all alpha WPLI values are NaN, then set the max index to NaN
% peakAlphaIndicesNaN(all(isnan(WPLIallAlpha),2)) = NaN;
%
% % % Reshape the result to 3D: 67x32x32 double
% % PeakAlphaIndicesNaN3D = squeeze(peakAlphaIndicesNaN);
%
% % Convert Indices (3D) to Correct Corresponding Frequency (Hz)
% validIdx = peakAlphaIndicesNaN >= 1 & peakAlphaIndicesNaN <= 11;
%
% % Initialize the output array with NaN, preserving original shape
% PeakAlphaFreqs = NaN(size(peakAlphaIndicesNaN));
%
% % Perform mapping only for valid indices
% PeakAlphaFreqs(validIdx) = alphaFreqHz(peakAlphaIndicesNaN(validIdx));
%
%
% % Extract Peak Alpha WPLI frequency into groups: 40 Hz & Light
% % PeakWPLIallAlpha: 67 subs x 32ch x 32ch
% PeakAlphaFreqs_40HzGroup = PeakAlphaFreqs(SubjG{GroupSubjs_40Hz},:,:);
% PeakAlphaFreqs_LightGroup = PeakAlphaFreqs(SubjG{GroupSubjs_Light},:,:);
%
% % Get average peak alpha WPLI frequncy for each group for all channel pairs
% MeanPeakAlphaFreq_40Hz = squeeze(mean(PeakAlphaFreqs_40HzGroup, 1));
% MeanPeakAlphaFreq_Light = squeeze(mean(PeakAlphaFreqs_LightGroup, 1));
%%% Find channel pairs that exceed WPLI threshold
% WPLI threshold:
averagePermutatedWPLI_2to55_top0_0001 = 0.0894;
averagePermutatedWPLIvalue = averagePermutatedWPLI_2to55_top0_0001;
% display the averagePermutatedWPLIvalue
fprintf("averagePermutatedWPLI_2to55_top0_0001: %.4f\n", averagePermutatedWPLIvalue);
% create logical mask of all channel pairs that have a peak band WPLI that exceeds the WPLI threshold (e.g. 0.0894) for 40 Hz and Light
% Groups. peakWPLI: 32x32 cell of 3x5 doubles
% SignificantWPLI_40Hz = MeanAlphaPeakWPLI_40Hz > averagePermutatedWPLIvalue;
% SignificantWPLI_Light = MeanAlphaPeakWPLI_Light > averagePermutatedWPLIvalue;
SignificantWPLI_40Hz = exceedMask(:,:,1,3);
SignificantWPLI_Light = exceedMask(:,:,3,3);
% count the total number of channel pairs that exceed the WPLI threshold, display the result
numSignificantPairs_40Hz = sum(sum(SignificantWPLI_40Hz));
numSignificantPairs_Light = sum(sum(SignificantWPLI_Light));
fprintf('\nNumber of Significant Channel Pairs (exceeding WPLI threshold) in 40Hz Group: %d\n', numSignificantPairs_40Hz);
fprintf('Number of Significant Channel Pairs (exceeding WPLI threshold) in Light Group: %d\n', numSignificantPairs_Light);
%%% use logical mask to extract peak alpha frequencies of signficant WPLI (exceeding threshold) channel pairs for 40 Hz and Light from
% , display the result
% SignificantAlphaFreqs_40Hz = MeanPeakAlphaFreq_40Hz;
SignificantAlphaFreqs_40Hz = peakAlphaFreq_40HzGroup .* SignificantWPLI_40Hz;
SignificantAlphaFreqs_40Hz(~SignificantWPLI_40Hz) = NaN;
SignificantAlphaFreqs_Light = peakAlphaFreq_LightGroup .* SignificantWPLI_Light;
SignificantAlphaFreqs_Light(~SignificantWPLI_Light) = NaN;
% Compute average peak alpha frequency across all channel pairs that exceed WPLI thresold for each
% group, display the result
MeanSignificantAlphaFreq_40Hz = mean(SignificantAlphaFreqs_40Hz(SignificantAlphaFreqs_40Hz > 0));
MeanSignificantAlphaFreq_Light = mean(SignificantAlphaFreqs_Light(SignificantAlphaFreqs_Light > 0));
fprintf('\nMean Peak Alpha Frequency (Significant 40Hz Group): %.2f Hz\n', MeanSignificantAlphaFreq_40Hz);
fprintf('Mean Peak Alpha Frequency (Significant Light Group): %.2f Hz\n', MeanSignificantAlphaFreq_Light);
% Compute median values
MedianSignificantAlphaFreq_40Hz = median(SignificantAlphaFreqs_40Hz(SignificantAlphaFreqs_40Hz > 0));
MedianSignificantAlphaFreq_Light = median(SignificantAlphaFreqs_Light(SignificantAlphaFreqs_Light > 0));
fprintf('\nMedian Peak Alpha Frequency (Significant 40Hz Group): %.2f Hz\n', MedianSignificantAlphaFreq_40Hz);
fprintf('Median Peak Alpha Frequency (Significant Light Group): %.2f Hz\n', MedianSignificantAlphaFreq_Light);
% Compute 25th and 75th percentiles
Q1_40Hz = prctile(SignificantAlphaFreqs_40Hz(SignificantAlphaFreqs_40Hz > 0), 25);
Q3_40Hz = prctile(SignificantAlphaFreqs_40Hz(SignificantAlphaFreqs_40Hz > 0), 75);
Q1_Light = prctile(SignificantAlphaFreqs_Light(SignificantAlphaFreqs_Light > 0), 25);
Q3_Light = prctile(SignificantAlphaFreqs_Light(SignificantAlphaFreqs_Light > 0), 75);
fprintf('\n25th Percentile (Significant 40Hz Group): %.2f Hz\n', Q1_40Hz);
fprintf('75th Percentile (Significant 40Hz Group): %.2f Hz\n', Q3_40Hz);
fprintf('25th Percentile (Significant Light Group): %.2f Hz\n', Q1_Light);
fprintf('75th Percentile (Significant Light Group): %.2f Hz\n', Q3_Light);
% Compute STE (Standard Error of the Mean)
std_40Hz = nanstd(SignificantAlphaFreqs_40Hz(:)); % Standard deviation
std_Light = nanstd(SignificantAlphaFreqs_Light(:));
N_40Hz = sum(~isnan(SignificantAlphaFreqs_40Hz(:))); % Sample size
N_Light = sum(~isnan(SignificantAlphaFreqs_Light(:)));
STE_40Hz = std_40Hz / sqrt(N_40Hz);
STE_Light = std_Light / sqrt(N_Light);
fprintf('\nStandard Error of the Mean (STE) - 40Hz Group: %.4f Hz\n', STE_40Hz);
fprintf('Standard Error of the Mean (STE) - Light Group: %.4f Hz\n', STE_Light);
% Test the distribution of alpha peak frequencies for normality, display results
[h_40Hz, p_40Hz] = kstest(SignificantAlphaFreqs_40Hz(:));
[h_Light, p_Light] = kstest(SignificantAlphaFreqs_Light(:));
fprintf('\nKolmogorov–Smirnov Normality Test (40Hz Group): p = %.8f\n', p_40Hz);
fprintf('Kolmogorov–Smirnov Normality Test (Light Group): p = %.8f\n', p_Light);
% Create violin plot of 40 Hz and Light alpha peak frequency distributions
figure;
v = violinplot([SignificantAlphaFreqs_40Hz(:), SignificantAlphaFreqs_Light(:)], {'40Hz', 'Light'});
ylabel('Peak Alpha Frequency (Hz)');
title(sprintf('Violin Plot of Peak Alpha Frequency Distributions\nWPLI threshold: %.4f', averagePermutatedWPLIvalue));
% Compute means and number of non-NaN data points
mean_40Hz = nanmean(SignificantAlphaFreqs_40Hz(:));
mean_Light = nanmean(SignificantAlphaFreqs_Light(:));
N_40Hz = sum(~isnan(SignificantAlphaFreqs_40Hz(:)));
N_Light = sum(~isnan(SignificantAlphaFreqs_Light(:)));
% Annotate mean values on the plot
hold on;
plot(1, mean_40Hz, 'kd', 'MarkerFaceColor', 'k', 'MarkerSize', 8); % Mean for 40Hz
plot(2, mean_Light, 'kd', 'MarkerFaceColor', 'k', 'MarkerSize', 8); % Mean for Light
% Annotate median values on the plot
plot(1, MedianSignificantAlphaFreq_40Hz, 'bs', 'MarkerFaceColor', 'b', 'MarkerSize', 8); % Median for 40Hz
plot(2, MedianSignificantAlphaFreq_Light, 'bs', 'MarkerFaceColor', 'b', 'MarkerSize', 8); % Median for Light
% Annotate 25th and 75th percentiles on the plot
plot(1, Q1_40Hz, 'm^', 'MarkerFaceColor', 'm', 'MarkerSize', 6); % 25th Percentile 40Hz
plot(1, Q3_40Hz, 'm^', 'MarkerFaceColor', 'm', 'MarkerSize', 6); % 75th Percentile 40Hz
plot(2, Q1_Light, 'm^', 'MarkerFaceColor', 'm', 'MarkerSize', 6); % 25th Percentile Light
plot(2, Q3_Light, 'm^', 'MarkerFaceColor', 'm', 'MarkerSize', 6); % 75th Percentile Light
% Display text for mean, median, and percentiles
text(1, mean_40Hz + 0.2, sprintf('Mean: %.2f Hz', mean_40Hz), 'HorizontalAlignment', 'center');
text(2, mean_Light + 0.2, sprintf('Mean: %.2f Hz', mean_Light), 'HorizontalAlignment', 'center');
text(1, MedianSignificantAlphaFreq_40Hz - 0.2, sprintf('Median: %.2f Hz', MedianSignificantAlphaFreq_40Hz), 'HorizontalAlignment', 'center', 'Color', 'b');
text(2, MedianSignificantAlphaFreq_Light - 0.2, sprintf('Median: %.2f Hz', MedianSignificantAlphaFreq_Light), 'HorizontalAlignment', 'center', 'Color', 'b');
text(1, Q1_40Hz - 0.3, sprintf('Q1: %.2f Hz', Q1_40Hz), 'HorizontalAlignment', 'center', 'Color', 'm');
text(1, Q3_40Hz + 0.3, sprintf('Q3: %.2f Hz', Q3_40Hz), 'HorizontalAlignment', 'center', 'Color', 'm');
text(2, Q1_Light - 0.3, sprintf('Q1: %.2f Hz', Q1_Light), 'HorizontalAlignment', 'center', 'Color', 'm');
text(2, Q3_Light + 0.3, sprintf('Q3: %.2f Hz', Q3_Light), 'HorizontalAlignment', 'center', 'Color', 'm');
% Display sample sizes
text(1, min(ylim) + 1, sprintf('N = %d', N_40Hz), 'HorizontalAlignment', 'center');
text(2, min(ylim) + 1, sprintf('N = %d', N_Light), 'HorizontalAlignment', 'center');
hold off;
% Rank sum test: testing if Light has significantly greater alpha peak frequency than 40 Hz
[p_ranksum, h_ranksum] = ranksum(SignificantAlphaFreqs_40Hz(:), SignificantAlphaFreqs_Light(:), 'tail', 'left');
fprintf('\nRank Sum Test p-value: %.16f\n', p_ranksum);
% Display peak alpha frequency of channel pairs exceeding WPLI threshold in tables (32x32)
ChannelLabels = {'Fp1', 'AF3', 'F7', 'F3', 'FC1', 'FC5', 'T7', 'C3', 'CP1', 'CP5', ...
'P7', 'P3', 'Pz', 'PO3', 'O1', 'Oz', 'O2', 'PO4', 'P4', 'P8', ...
'CP6', 'CP2', 'C4', 'T8', 'FC6', 'FC2', 'F4', 'F8', 'AF4', 'Fp2', 'Fz', 'Cz'};
fprintf('\nMean Peak Alpha Frequency (Significant channel pairs only - in 40Hz Group):\n');
disp(array2table(SignificantAlphaFreqs_40Hz, 'VariableNames', ChannelLabels, 'RowNames', ChannelLabels));
fprintf('\nMean Peak Alpha Frequency (Significant channel pairs only - in Light Group):\n');
disp(array2table(SignificantAlphaFreqs_Light, 'VariableNames', ChannelLabels, 'RowNames', ChannelLabels));
fprintf('\nMean Peak Alpha WPLI (All channel pairs in 40Hz Group):\n');
disp(array2table(MeanAlphaPeakWPLI_40Hz, 'VariableNames', ChannelLabels, 'RowNames', ChannelLabels));
fprintf('\nMean Peak Alpha WPLI (All channel pairs in Light Group):\n');
disp(array2table(MeanAlphaPeakWPLI_Light, 'VariableNames', ChannelLabels, 'RowNames', ChannelLabels));
%% Peak Alpha WPLI 40Hz vs Light SIGNIFICANT CH ONLY (abandoned approached)
% Initialize logical masks for valid channel pairs
numChannels = 32;
validChannels_40Hz = false(numChannels, numChannels);
validChannels_Light = false(numChannels, numChannels);
group40HzIndex = 1;
groupLightIndex = 3;
% Identify valid channels where WPLI exceeds threshold in each group
for i = 1:numChannels
for j = 1:numChannels
if isempty(peakWPLI{i, j})
continue; % Skip empty cells
end
% Check if peak WPLI is greater than threshold for 40Hz and Light groups
if peakWPLI{i, j}(group40HzIndex, 1) > averagePermutatedWPLIvalue
validChannels_40Hz(i, j) = true;
end
if peakWPLI{i, j}(groupLightIndex, 1) > averagePermutatedWPLIvalue
validChannels_Light(i, j) = true;
end
end
end
% Find common channel pairs where both groups exceed the threshold
validChannels = validChannels_40Hz & validChannels_Light;
% Collect flattened peak alpha frequencies for valid channels only
selectedFreqs_40Hz = [];
selectedFreqs_Light = [];
for i = 1:numChannels
for j = 1:numChannels
if validChannels(i, j)
selectedFreqs_40Hz = [selectedFreqs_40Hz; PeakAlphaFreqs_40HzGroup(:, i, j)];
selectedFreqs_Light = [selectedFreqs_Light; PeakAlphaFreqs_LightGroup(:, i, j)];
end
end
end
%% Plot the results (no counts)
figure;
for iGroup = 1:numGroups
subplot(1, numGroups, iGroup);
bar(fractionExceed(iGroup, :));
title(GroupNameWPLI{iGroup}); % Use group name as title
xlabel('Frequency Band');
ylabel('Fraction of Channel Pairs');
% Customize x-axis labels with both BandName and BandHzName
xticks(1:numBands);
xticklabels(arrayfun(@(iBand) [BandName_filtered{iBand}, ' (', BandHzName_filtered{iBand}, ')'], ...
1:numBands, 'UniformOutput', false));
xtickangle(45);
ylim([0 1]); % Fractions are between 0 and 1
end
% Add super title
sgtitle('Fraction of Channel Pairs Exceeding average permutated p=0.01 WPLI value');
%% Same as above but with counts above each bar
figure;
for iGroup = 1:numGroups
subplot(1, numGroups, iGroup);
barHandle = bar(fractionExceed(iGroup, :));
title(GroupNameWPLI{iGroup}); % Use group name as title
xlabel('Frequency Band');
ylabel('Fraction of Channel Pairs');
% Customize x-axis labels with both BandName and BandHzName
xticks(1:numBands);
xticklabels(arrayfun(@(iBand) [BandName_filtered{iBand}, ' (', BandHzName_filtered{iBand}, ')'], ...
1:numBands, 'UniformOutput', false));
xtickangle(45);
ylim([0 1]); % Fractions are between 0 and 1
%%% Add exceed count above each bar
barHeights = fractionExceed(group, :);
for iBand = 1:numBands
% Position the text slightly above the bar height
text(iBand, barHeights(iBand) + 0.02, num2str(round(barHeights(iBand) * totalPairs)), ...
'HorizontalAlignment', 'center', 'FontSize', 10);
end
end
% Add super title
sgtitle('Fraction of Channel Pairs Exceeding average permutated p=0.0001 WPLI value');
%% Bar graph with counts below
figure;
for group = 1:numGroups
subplot(1, numGroups, group);
barHandle = bar(fractionExceed(group, :));
title(GroupNameWPLI{group}); % Use group name as title
xlabel('Frequency Band');
ylabel('Fraction of Channel Pairs');
% Customize x-axis labels with both BandName and BandHzName
xticks(1:numBands);
xticklabels(arrayfun(@(iBand) [BandName_filtered{iBand}, ' (', BandHzName_filtered{iBand}, ')'], ...
1:numBands, 'UniformOutput', false));
xtickangle(45);
ylim([0 1]); % Fractions are between 0 and 1
% Add exceed count below the top of each bar
barHeights = fractionExceed(group, :);
for iBand = 1:numBands
% Position the text slightly below the bar height
text(iBand, barHeights(iBand) - 0.02, num2str(round(barHeights(iBand) * totalPairs)), ...
'HorizontalAlignment', 'center', 'FontSize', 10, 'VerticalAlignment', 'top');
end
end
% Add super title
sgtitle('Fraction of Channel Pairs Exceeding average permutated p=0.01 WPLI value');
%% Plot the results 2025-01-08 MKA fig4D supp? - top quartile
% BOI=[1 4 8 13 30 39.5;4 8 13 30 37 41.5];
% BOI2HzFiveBands=[2 4 8 13 39.5;4 8 13 30 41.5];
% BandNameFiveBands={'Delta','Theta','Alpha','Beta','Gamma-E'};
% BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','39-41 Hz'};
% BandHzName2HzFiveBands={'2-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','39-41 Hz'};
BOI2Hz=[2 4 8 8 10 13 30 39.5 43 55;4 8 13 10 13 30 37 41.5 55 100];
BandName={'Delta','Theta','Alpha','LowAlpha','HighAlpha','Beta','Gamma-1','Gamma-E', 'Gamma-55','Gamma-2'};
BandHzName2Hz={'2-4 Hz','4-8 Hz','8-13Hz','8-10Hz','10-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-55 Hz', '55-100 Hz'};
numBands = size(BOI2Hz, 2);
%%% Get WPLI for each stim group for each BOI and ch Pair
%%%% Loop thru all ch pairs
for i = 1:numChannels
for j = 1:numChannels
if isempty(AvgRealWPLI{i, j})
continue; % Skip empty cells
end
data = AvgRealWPLI{i, j}; % 3x197 double (stimulation groups x frequencies)
% Preallocate storage for this channel pair
peakWPLI{i, j} = zeros(size(data, 1), numBands); % stim groups x frequency bands
% Loop over bands of interest
for iBand = 1:numBands
% Find indices corresponding to the frequency band
freqIndices = Fplot >= BOI2Hz(1, iBand) & Fplot <= BOI2Hz(2, iBand);
% Get peak WPLI for each stimulation group
for group = 1:size(data, 1)
peakWPLI{i, j}(group, iBand) = max(data(group, freqIndices));
end
end
end
end
% Result is stored in peakWPLI{i, j}(group, iBand), where:
% i, j = channel indices
% group = stimulation group
% iBand = frequency band index
%%% Plot fraction of channel pairs with WPLI greater than p=0.01 permutated WPLI
% Example variables (replace these with actual data)
% averagePermutatedWPLItop0_01 = 0.5; % Replace with actual value
% GroupNameWPLI = {'Group 1', 'Group 2', 'Group 3'}; % Replace with actual group names
% Initialize fraction of channel pairs exceeding the threshold
numGroups = 3; % Number of stimulation groups
numBands = size(BOI2Hz, 2)-1; % Number of frequency bands
fractionExceed = zeros(numGroups, numBands);
% averagePermutatedWPLI_2to55_top0_0001 = 0.0894
% averagePermutatedWPLI_2to55_top0_001 = 0.04
averagePermutatedWPLIvalue = 0.1202; % top quartile threshold %
% Count total non-empty cells
numChannels = size(AvgRealWPLI, 1);
totalPairs = 0;
for i = 1:numChannels
for j = 1:numChannels
if ~isempty(AvgRealWPLI{i, j})
totalPairs = totalPairs + 1;
end
end
end
% Loop through stimulation groups and frequency bands
for group = 1:numGroups
for iBand = 1:numBands
exceedCount = 0;
% Loop over all channel pairs
for i = 1:numChannels
for j = 1:numChannels
if isempty(AvgRealWPLI{i, j})
continue; % Skip empty cells
end
% Check if the value for the group and band exceeds the threshold
if peakWPLI{i, j}(group, iBand) > averagePermutatedWPLIvalue %averagePermutatedWPLItop0_01
exceedCount = exceedCount + 1;
end
end
end
% Calculate the fraction for this group and band
fractionExceed(group, iBand) = exceedCount / totalPairs;
end
end
figure;
for iGroup = 1:numGroups
subplot(1, numGroups, iGroup);
barHandle = bar(fractionExceed(iGroup, :));
title(GroupNameWPLI{iGroup}); % Use iGroup name as title
xlabel('Frequency Band');
ylabel('Fraction of Channel Pairs');
% Customize x-axis labels with both BandName and BandHzName
xticks(1:numBands);
xticklabels(arrayfun(@(iBand) [BandName{iBand}, ' (', BandHzName2Hz{iBand}, ')'], ...
1:numBands, 'UniformOutput', false));
xtickangle(45);
ylim([0 1]); % Fractions are between 0 and 1
% Add exceed count conditionally above or below the bar
barHeights = fractionExceed(iGroup, :);
for iBand = 1:numBands
exceedCount = round(barHeights(iBand) * totalPairs); % Calculate exceed count
if barHeights(iBand) < 0.2
% Place count above the bar
text(iBand, barHeights(iBand) + 0.04, num2str(exceedCount), ...
'HorizontalAlignment', 'center', 'FontSize', 10);
else
% Place count below the top of the bar
text(iBand, barHeights(iBand) - 0.01, num2str(exceedCount), ...
'HorizontalAlignment', 'center', 'FontSize', 10, 'VerticalAlignment', 'top');
end
end
% Store percentage of Alpha band exceedance for this iGroup
alphaExceedPercent(iGroup) = barHeights(3) * 100; % Alpha band is iBand = 4
end
% Add super title
sgtitle(['Fraction of Channel Pairs Exceeding top quartile WPLI value: ' num2str(averagePermutatedWPLIvalue)]);
% Calculate and display the average percentage of Alpha band exceedance
averageAlphaPercent = mean(alphaExceedPercent);
disp(['Average Percentage of Channel Pairs Exceeding Threshold for Alpha Band: ', num2str(averageAlphaPercent), '%']);
% Display percentages for each group
for group = 1:numGroups
disp(['Group ', GroupNameWPLI{group}, ': ', num2str(alphaExceedPercent(group)), '%']);
end
%% Plot bar graph of # channels exceeding p=0.0001 WPLI threshold (New Fig4C MS) MKA 2025-02-06
BOI2HzFiveBands=[2 4 8 13 30; 4 8 13 30 37];
BandNameFiveBands={'Delta','Theta','Alpha','Beta','Slow Gamma'};
BandHzName2HzFiveBands={'2-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz'};
numBands = size(BOI2HzFiveBands, 2);
%%% Initialize storage for peak frequencies
peakWPLI_Freq = cell(numChannels, numChannels);
%%% Loop over channel pairs
for i = 1:numChannels
for j = 1:numChannels
if isempty(AvgRealWPLI{i, j})
continue; % Skip empty cells
end
AvgRealWPLI_ijChPair = AvgRealWPLI{i, j}; % AvgRealWPLI_ijChPair = 3x197 double (stimulation groups x frequencies)
% Preallocate storage for this channel pair
peakWPLI{i, j} = zeros(size(AvgRealWPLI_ijChPair, 1), numBands); % stim groups x frequency bands
peakWPLI_Freq{i, j} = zeros(size(AvgRealWPLI_ijChPair, 1), numBands); % Store peak frequency
% Loop over bands of interest
for iBand = 1:numBands
% Find indices corresponding to the frequency band
iBandFreqIndices = Fplot >= BOI2HzFiveBands(1, iBand) & Fplot <= BOI2HzFiveBands(2, iBand);
Fplot_iBand = Fplot(iBandFreqIndices); % Extract frequencies in this band
% Get peak WPLI for each stimulation group
for iGroup = 1:size(AvgRealWPLI_ijChPair, 1)
AvgRealWPLI_ijChPair_iBand_iGroup = AvgRealWPLI_ijChPair(iGroup, iBandFreqIndices); %
% Find peak WPLI value
[peakWPLI{i, j}(iGroup, iBand), maxIdx] = max(AvgRealWPLI_ijChPair_iBand_iGroup);
% Store corresponding frequency
peakWPLI_Freq{i, j}(iGroup, iBand) = Fplot_iBand(maxIdx);
end
end
end
end
% Result is stored in peakWPLI{i, j}(group, iBand), where:
% i, j = channel indices
% group = stimulation group
% iBand = frequency band index
%%% Plot fraction of channel pairs with WPLI greater than p=0.01 permutated WPLI
% Example variables (replace these with actual data)
% averagePermutatedWPLItop0_01 = 0.5; % Replace with actual value
% GroupNameWPLI = {'Group 1', 'Group 2', 'Group 3'}; % Replace with actual group names
% Initialize fraction of channel pairs exceeding the threshold
numGroups = 3; % Number of stimulation groups
numBands = size(BOI2HzFiveBands, 2); % Number of frequency bands
fractionExceed = zeros(numGroups, numBands);
% averagePermutatedWPLI_2to55_top0_0001 = 0.0894
% averagePermutatedWPLI_2to55_top0_001 = 0.04
averagePermutatedWPLIvalue = averagePermutatedWPLI_2to55_top0_0001; % top quartile threshold % averagePermutatedWPLI_2to55_top0_001;
% Count total non-empty cells
numChannels = size(AvgRealWPLI, 1);
totalPairs = 0;
%%% count the number of total channel pairs based on number of AvgRealWPLI values (use mask)
for i = 1:numChannels
for j = 1:numChannels
if ~isempty(AvgRealWPLI{i, j})
totalPairs = totalPairs + 1;
end
end
end
totalPairsMaskSum = sum(~cellfun(@isempty, AvgRealWPLI), 'all');
% Preallocate arrays
peakWPLIarray = nan(numChannels, numChannels, numGroups, numBands);
exceedMask = false(numChannels, numChannels, numGroups, numBands);
fractionExceed = zeros(numGroups, numBands);
% Create a logical mask for non-empty channel pairs
validPairs = ~cellfun(@isempty, AvgRealWPLI);
% Loop through stimulation groups and frequency bands
for iGroup = 1:numGroups
for iBand = 1:numBands
% % Extract peakWPLI values into an array (set NaN for empty pairs)
% peakWPLIarray(validPairs, iGroup, iBand) = cellfun(@(x) x(iGroup, iBand), peakWPLI(validPairs));
% peakWPLIarray(~validPairs) = NaN; % Ensure empty cells remain NaN
% Extract peakWPLI values into an array (set NaN for empty pairs)
tempValues = nan(numChannels, numChannels); % Temporary storage
tempValues(validPairs) = cellfun(@(x) x(iGroup, iBand), peakWPLI(validPairs));
% Store in preallocated array
peakWPLIarray(:, :, iGroup, iBand) = tempValues;
% Create a logical mask for values exceeding the threshold
exceedMask(:, :, iGroup, iBand) = peakWPLIarray(:, :, iGroup, iBand) > averagePermutatedWPLIvalue;
% Count the number of exceeding pairs
exceedCount = sum(exceedMask(:, :, iGroup, iBand), 'all');
% Calculate the fraction
fractionExceed(iGroup, iBand) = exceedCount / totalPairs;
end
end
% Loop through stimulation groups and frequency bands
for group = 1:numGroups
for iBand = 1:numBands
exceedCount = 0;
% Loop over all channel pairs
for i = 1:numChannels
for j = 1:numChannels
if isempty(AvgRealWPLI{i, j})
continue; % Skip empty cells
end
% Check if the value for the group and band exceeds the threshold
if peakWPLI{i, j}(group, iBand) > averagePermutatedWPLIvalue %averagePermutatedWPLItop0_01
exceedCount = exceedCount + 1;
end
end
end
% Calculate the fraction for this group and band
fractionExceed(group, iBand) = exceedCount / totalPairs;
end
end
%%% Plot High Functional Connectivity Bar Plot *** fig4c
figure;
for iGroup = 1:numGroups
subplot(1, numGroups, iGroup);
barHandle = bar(fractionExceed(iGroup, :));
if iGroup ~= 3
title(GroupNameWPLI{iGroup}); % Use iGroup name as title
else
title("Light"); % Use "Light" instead of "LightRT"
end
if iGroup ==2
xlabel('Frequency Band');
end
if iGroup == 1
ylabel('Fraction of Channel Pairs');
end
% Customize x-axis labels with both BandName and BandHzName
iBand = 1;
xticks(1:numBands);
xticklabels(arrayfun(@(iBand) [BandNameFiveBands{iBand}], ...
1:numBands, 'UniformOutput', false));
xtickangle(45);
ylim([0 0.55]); % Fractions are between 0 and 1
% Add exceed count conditionally above or below the bar
barHeights = fractionExceed(iGroup, :);
for iBand = 1:numBands
exceedCount = round(barHeights(iBand) * totalPairs); % Calculate exceed count
if barHeights(iBand) < 0.2
% Place count above the bar
text(iBand, barHeights(iBand) + 0.06500000000000000000000001, num2str(exceedCount), ...
'HorizontalAlignment', 'center', 'FontSize', 10, 'VerticalAlignment', 'top'); %
else
% Place count below the top of the bar
text(iBand, barHeights(iBand) + 0.065, num2str(exceedCount), ...
'HorizontalAlignment', 'center', 'FontSize', 10, 'VerticalAlignment', 'top');
end
end
% Store percentage of Alpha band exceedance for this iGroup
alphaExceedPercent(iGroup) = barHeights(3) * 100; % Alpha band is iBand = 4
end
% Add super title
sgtitle(['Fraction of Channel Pairs Out of 492 Exceeding permutated p=0.0001 WPLI value: ' num2str(averagePermutatedWPLIvalue)], 'FontSize', 6);
% Calculate and display the average percentage of Alpha band exceedance
averageAlphaPercent = mean(alphaExceedPercent);
disp(['Average Percentage of Channel Pairs Exceeding Threshold for Alpha Band: ', num2str(averageAlphaPercent), '%']);
% Display percentages for each group
for group = 1:numGroups
disp(['Group ', GroupNameWPLI{group}, ': ', num2str(alphaExceedPercent(group)), '%']);
end
% Set figure size
set(gcf, 'PaperUnits', 'inches', 'PaperPosition', [0 0 4 2.5]); % 6x4 inches
% Save as SVG
saveDateFig4C = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
saveas(gcf, ['Fig4C_WPLI_exceed' saveDateFig4C '.svg']);
saveas(gcf, ['Fig4C_WPLI_exceed' saveDateFig4C '.png']);
%% Get Alpha Peak Amplitude and Alpha Peak Frequency *** fig4b
iWPLICh = 1; % 1 = FP1
jWPLICh = 5; % 5 = FC1
for iStimGroup = 1:length(SubjGWPLI)
groupMean = mean(DataPlot{iStimGroup});
alphaMean = groupMean(13:23);
[alphaPeakAmplitude, alphaPeakIndex] = max(groupMean(13:23));
alphaFreqs = Fplot(13:23); % 13 to 23 should be 8 to 13 Hz
alphaPeakFrequency = alphaFreqs(alphaPeakIndex);
alphaPeakAmplitudeList(iWPLICh,jWPLICh,iStimGroup) = alphaPeakAmplitude;
alphaPeakFrequencyList(iWPLICh,jWPLICh,iStimGroup) = alphaPeakFrequency;
end
% Create the actual plot fig4b (commented for getting alpha measurements) UNCOMMENT TO PLOT!
figure;
xrange = [0 37]; % x axis range (frequency)
subplot('Position',[0.1 0.1 0.88 0.88])
% Cut data down to 55Hz
% Fplot55=Fplot(1:107);
% DataPlot55 = cellfun(@(x) x(1:107), DataPlot, 'UniformOutput', false); % Works like this-> DataPlot55=DataPlot(1:107);
RateHist_GroupPlot(Fplot,DataPlot,FlickerColor,ParamWPLI); %% Lu's function
% Add a dotted line at averagePermutatedWPLIvalue
yline(averagePermutatedWPLIvalue, '--', 'Color', 'k', 'LineWidth', 1.2);
text(range(xrange)/2-5,ParamWPLI.Ytick(end),[EEGch{iWPLICh} '-' EEGch{jWPLICh}],'FontSize', 10);
% set(gca,'xlim',[0 100],'xtick',[0:20:120],'ylim',[0 0.3],'ytick',ParamWPLI.Ytick);
set(gca,'xlim',xrange,'xtick',[1 4 8 13 30 37 40 50 60 80 100],'ylim',[0 0.15],'ytick',[0 0.05 0.1 0.15]);
xlabel('Frequency Hz')
% xlim(xrange);
ylabel('WPLI')
title(['EEG Channel Pair: ' EEGch{iWPLICh} '-' EEGch{jWPLICh}]);
ax=gca;
ax.XGrid = 'on';
ax.YGrid = 'off';
ax.FontSize = 7; % Set the desired font size for tick marks
LuFontStandard
papersizePX=[0 0 8 8];
papersizePX=1.3*[0 0 5 3.2]; % 09/09/24
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SubSaveFig '40LightRandBands_' EEGch{iWPLICh} '-' EEGch{jWPLICh}],'svg');
% saveas(gcf,[SubSaveFig '40LightRandBands_' EEGch{iWPLICh} '-' EEGch{jWPLICh}],'tiff');
close all
nonzerosGroup1= nonzeros(alphaPeakAmplitudeList(:,:,1));
nonzerosGroup2= nonzeros(alphaPeakAmplitudeList(:,:,2));
nonzerosGroup3= nonzeros(alphaPeakAmplitudeList(:,:,3));
AlphaPeakSaveFilename = fullfile(SaveFolder, 'AlphaPeakAmpAndFreq') %#ok<NOPTS>
save(AlphaPeakSaveFilename,"alphaPeakAmplitudeList","alphaPeakFrequencyList");
% papersizePX=[0 0 6*length(EEGchInd) 6*length(EEGchInd)];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllCh40Random' TrialTypeName{iCom}],'pdf');
% saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllCh40Random' TrialTypeName{iCom}],'png');
% saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllCh40Random' TrialTypeName{iCom} '.eps'],'epsc');
close all
close all
%% Frequency Band Definition, for maps - May need to start running from here for Fig4
BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
% Gamma-E is 40
BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
FreqFunc{1}=@nanmean;
FreqFunc{2}=@nanmedian;
FreqFunc{3}=@nanmax;
FreqFuncNames={'mean','median','peak'}; % Check three different spots in band - previously called 'FunGroupName'
%comparison between groups w/ freq data
diffTPmap=zeros(length(ChanEEGLab),length(ChanEEGLab),size(BOI,2),length(TrialType)); % for T test
diffRPmap=diffTPmap;
diffTmap=diffTPmap;
rSpear=diffTPmap;
pSpear=diffTPmap;
% % TNodeTh=10;
% FC_BrainEEGLu(ChanPos,AdjWeight,NodeWeight,Param)
%% Preallocate ChannelPairName Cell Array
signifChPairNameTopo = cell(3,3,3,3,2);
signifChPairNameTopo{3,3,3,3,2} = [];
% WPLIBResults = struct();
%% FC parameters for plotting; (may contain p-value variable)
clear FCpara
FCpara.ColorMap=colorMapPN; %%%Color map for correlation link
%% Create orange indigo colormap
% Number of colors in the colormap
n = 64;
% Define orange and indigo RGB values
orange = [1, 0.75, 0];
indigo = [0.2, 0.1, 1];
% Create a colormap by interpolating between orange and indigo
custom_cmap = [linspace(indigo(1), orange(1), n)', ...
linspace(indigo(2), orange(2), n)', ...
linspace(indigo(3), orange(3), n)'];
% Apply the custom colormap
% colormap(custom_cmap);
FCpara.ColorMap=custom_cmap; %%%Color map for correlation link
%% Other parameters
FCpara.NodeColor=[0.8 0.8 0.8]; %%%Node Color of Nodes for FC, not important, it is actually defined in ChanPos
FCpara.Clim=[-1 1]; %%%Color Limit FC
% FCpara.MarkerSize=8; %%%MarkerSize of scatter
% FCpara.EdgeColor=[1 0 0]; %%% This is not needed as ColorMap field and Clim field would determine the edge color
FCpara.EdgeTh=0.1; %%%
FCpara.NodeTh=0.05; %%%
FCparaT=FCpara; %%%%T test parameters
FCparaT.EdgeTh=3; %%%
FCparaT.NodeTh=0.001; %%%
FCspear=FCpara; %%%%Spearman r parameters
FCspear.EdgeTh=0.1; %%% This is the plotting threshold
FCspear.NodeTh=0.001; %%%
WPLIEdgeTh=0.6;
WPLINodeTh=0.05;
TEdgeTh=1;
TNodeTh=0.001;
pTEdgeTh=0.05;
pSpearEdgeTh=0.05; %8/8/24 0.05 to 0.1
rSpearEdgeTh=0.1;
rSpearNodeTh=0.001;
ScaleWPLI=0.5;
ScaleT=1;
ScaleSpear=0.2;
% BOI=[5;15];
BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
%% FC calculation - WPLI-Behavior - Spearman Topo Plots *** (old fig4c) fig4d fig4e fig4f
FCpara.EdgeTh=0.1; %%%
BOI=[2 4 8 8 10 13 30 39 43;4 8 13 10 13 30 37 41 100]; %[1 4 8 8 10 13 30 39.5 43;4 8 13 10 13 30 37 41.5 100];
BandName={'Delta','Theta','Alpha','LowAlpha','HighAlpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
BandHzName={'2-4 Hz','4-8 Hz','8-13Hz','8-10Hz','10-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
for iFreqFunc=[3] %1:length(FreqFuncNames) % mean = 1, median =2, peak = 3
todayDate = datestr(now, 'yymmdd');
for iTrialType=3 %1:length(TrialType) % 1=Hit, 2=Miss, 3=HitANDMiss
%% Create save folder
SaveTemp=[SubSaveWPLI TrialTypeName{iTrialType} '\'];
SaveTemp=[SaveTemp todayDate '_' FreqFuncNames{iFreqFunc} '_p' num2str(pSpearEdgeTh) '_colored\' ];
mkdir(SaveTemp)
%% WPLI figure - ALL groups (6 groups) & ALL BOI - *** (old fig4c) fig4d Complete version - see MS version below
% Fig 4 C and D are composed of panels created by this section, cut and pasted together in
% illustrator
% FCpara.EdgeTh=0.06;
% FCpara.EdgeTh=0.15; % previous arbitray threshold
FCpara.EdgeTh=0.1202; % top quartile threshold
% FCpara.EdgeTh=averagePermutatedWPLI_2to55_top0_0001; % = 0.0894 - top 0.0001 permutation threshold
% FCpara.EdgeTh=averagePermutatedWPLI_2to55_top0_001; % = 0.0484 - top 0.001 permutation threshold
FCgroups = [1,3,6]; % [1,3,6] = [40hz , Random, LightRT]
figure;
nGroups = length(FCgroups); % (1:3) Just first 3 groups
for iBOI=1:size(BOI,2)-1
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<BOI(2,iBOI));
for iStimGroup=1:length(FCgroups)
if iFreqFunc==3
Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),[],2),1));
else
Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),2));
MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),2),1));
end
EdgeColor=[0.8 0.8 0.8];
% FC_BrainEEGLu(ChanPosColin27,MapGroup{iG,iFF,iCom},[],EdgeTh,NodeTh,EdgeColor,[])
% axis off
subplotLU(nGroups,size(BOI,2),iStimGroup,iBOI);
% WPLIsForPlot(iStimGroup,iBOI,iTrialType) = MapGroup{iStimGroup,iBOI,iTrialType};
% nChPairAboveThreshold(iStimGroup,iBOI,iTrialType) = sum(WPLIsForPlot>FCpara.EdgeTh);
% Calculate the number of channel pairs with WPLI > EdgeTh
currentMap = MapGroup{iStimGroup, iBOI, iTrialType};
nChPairAboveThreshold = sum(currentMap(:) > FCpara.EdgeTh);
AboveThreholdChannelPairAllGroups(iStimGroup,iBOI,iTrialType) = nChPairAboveThreshold;
%%% The plotting function
FC_BrainEEGLu(ChanPosColin27,MapGroup{iStimGroup,iBOI,iTrialType},[],FCpara)
axis off
% Add text below the plot
text(0.5, -0.15, sprintf('Pairs > Th: %d', nChPairAboveThreshold), ...
'Units', 'normalized', 'HorizontalAlignment', 'center', 'FontSize', 10);
if iStimGroup==nGroups
xlabel(BandName{iBOI});
text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
end
if iBOI==1
ylabel(GroupName{FCgroups(iStimGroup)})
yt=text(0,0.1,0.1,GroupName{FCgroups(iStimGroup)},'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
end
end
% Define the edge threshold title
edgeThresholdTitle = sprintf('Edge Threshold: %.4f', FCpara.EdgeTh);
% Add the title displaying the edge threshold
sgtitle(edgeThresholdTitle, 'FontSize', 6, 'FontWeight', 'bold', 'Interpreter', 'none');
% % Add the title displaying the edge threshold and position it higher
% titleHandle = sgtitle(edgeThresholdTitle, 'FontSize', 12);
% titleHandle.Position = [0.5, 0.98, 0]; % [x, y, z] position in normalized figure units
% subplot('position',[0.5 0.51 0.3 0.01]);
% b=colorbar('southoutside');
% set(gca,'xtick',[],'ytick',[])
% set(b,'position',[0.5 0.5 0.3 0.03],'Limits',[0 1],'Ticks',[0 1],'Ticklabels',PowerLab);
% xlabel(b,'Log Normalized Power')
LuFontStandard;
papersizePX=[0 0 6*size(BOI,2) 6*nGroups+3];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% Adjust title spacing relative to the subplots
t = sgtitle(edgeThresholdTitle, 'FontSize', 6);
% t.Position(2) = t.Position(2) + 0.03; % Raise the title slightly
saveWPLIFigName = ['WPLI40_' num2str(nGroups) 'Groups_' num2str(100*FCpara.EdgeTh) 'E-2EdgeThrs620.png'];
% saveas(gcf,[SaveTemp saveWPLIFigName],'pdf');
saveas(gcf,[SaveTemp saveWPLIFigName],'png');
saveas(gcf,[SaveTemp saveWPLIFigName],'svg');
% saveas(gcf,[SaveTemp saveWPLIFigName],'epsc');
FCpara.EdgeTh=0.1; % reset
%% WPLI figure *** fig4d MS version
% Fig 4 C and D are composed of panels created by this section, cut and pasted together in
% illustrator
% Lower Alpha and Upper Alpha only
BOI=[8 10; 10 13]; %[1 4 8 8 10 13 30 39.5 43;4 8 13 10 13 30 37 41.5 100];
BandName={'LowerAlpha','UpperAlpha'};
BandHzName={'8-10Hz','10-13Hz'};
% FCpara.EdgeTh=0.06;
% FCpara.EdgeTh=0.15; % previous arbitray threshold
FCpara.EdgeTh=0.1202; % top quartile threshold
% FCpara.EdgeTh=averagePermutatedWPLI_2to55_top0_0001; % = 0.0894 - top 0.0001 permutation threshold
% FCpara.EdgeTh=averagePermutatedWPLI_2to55_top0_001; % = 0.0484 - top 0.001 permutation threshold
FCgroups = [1,3,6]; % [1,3,6] = [40hz , Random, LightRT]
figure;
nGroups = length(FCgroups); % (1:3) Just first 3 groups
for iBOI=1:size(BOI,2) % just lower alpha and upper alpha
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<BOI(2,iBOI));
for iStimGroup=1:length(FCgroups)
if iFreqFunc==3
Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),[],2),1));
else
Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),2));
MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(WPLIall(SubjG{FCgroups(iStimGroup)},NeedI,EEGchInd,EEGchInd,iTrialType),2),1));
end
EdgeColor=[0.8 0.8 0.8];
% FC_BrainEEGLu(ChanPosColin27,MapGroup{iG,iFF,iCom},[],EdgeTh,NodeTh,EdgeColor,[])
% axis off
% subplotLU(nGroups,size(BOI,2),iStimGroup,iBOI); % old 3x2
% subplotLU(1, nGroups*size(BOI,2), 1, iStimGroup+3*(iBOI-1)); % 1x6 grid
subplotLU(size(BOI,2), nGroups, iBOI, iStimGroup); % 2x3 grid
% WPLIsForPlot(iStimGroup,iBOI,iTrialType) = MapGroup{iStimGroup,iBOI,iTrialType};
% nChPairAboveThreshold(iStimGroup,iBOI,iTrialType) = sum(WPLIsForPlot>FCpara.EdgeTh);
% Calculate the number of channel pairs with WPLI > EdgeTh
currentMap = MapGroup{iStimGroup, iBOI, iTrialType};
nChPairAboveThreshold = sum(currentMap(:) > FCpara.EdgeTh);
AboveThreholdChannelPairAllGroups(iStimGroup,iBOI,iTrialType) = nChPairAboveThreshold;
%%% The plotting function
FC_BrainEEGLu(ChanPosColin27,MapGroup{iStimGroup,iBOI,iTrialType},[],FCpara)
axis off
% Add text below the plot
% text(0.5, -0.15, sprintf('Pairs > Th: %d', nChPairAboveThreshold), ...
% 'Units', 'normalized', 'HorizontalAlignment', 'center', 'FontSize', 10);
if iStimGroup==2
xlabel(BandName{iBOI});
text(0.125,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',18)
end
xlabel(GroupName{FCgroups(iStimGroup)})
yt=text(-0.125,0,0.1,GroupName{FCgroups(iStimGroup)},'horizontalalignment','center','verticalalignment','bottom','fontsize',14);
end
end
% Define the edge threshold title
% edgeThresholdTitle = sprintf('Edge Threshold: %.4f', FCpara.EdgeTh);
% % Add the title displaying the edge threshold
% sgtitle(edgeThresholdTitle, 'FontSize', 6, 'FontWeight', 'bold', 'Interpreter', 'none');
% t.Position(2) = t.Position(2) + 0.05; % Move title slightly up
% % Add the title displaying the edge threshold and position it higher
% titleHandle = sgtitle(edgeThresholdTitle, 'FontSize', 12);
% titleHandle.Position = [0.5, 0.98, 0]; % [x, y, z] position in normalized figure units
% subplot('position',[0.5 0.51 0.3 0.01]);
% b=colorbar('southoutside');
% set(gca,'xtick',[],'ytick',[])
% set(b,'position',[0.5 0.5 0.3 0.03],'Limits',[0 1],'Ticks',[0 1],'Ticklabels',PowerLab);
% xlabel(b,'Log Normalized Power')
LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2)*4 6/3*nGroups+3]; % 1x6
% 3x2 grid: papersizePX=[0 0 6*size(BOI,2) 6*nGroups+3];
papersizePX=[0 0 nGroups*6 size(BOI,2)*6+2.5]; % 2x3 : [0 0 width{x} heigth{y}]
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% Adjust title spacing relative to the subplots
% t = sgtitle(edgeThresholdTitle, 'FontSize', 6);
% t.Position(2) = t.Position(2) + 0.03; % Raise the title slightly
% saveas(gcf,[SaveTemp saveWPLIFigName],'pdf');
saveDateFig4D = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
saveWPLIFigName = ['WPLI40_' num2str(nGroups) 'Groups_' saveDateFig4D '_' num2str(100*FCpara.EdgeTh) 'E-2EdgeThrs620.png'];
saveas(gcf,['Fig4Panels/' saveWPLIFigName ],'png');
% saveas(gcf,['Fig4Panels/' saveWPLIFigName],'svg');
% saveas(gcf,[SaveTemp saveWPLIFigName],'epsc');
FCpara.EdgeTh=0.1; % reset
%% Perform Chi-Squared on proportions on elevated channels: 40vL & 40VRand
% Data
total_pairs = 496;
elevated_40Hz = 93;
elevated_Light = 32;
% Define contingency table
table_40Hz_Light = [elevated_40Hz, total_pairs - elevated_40Hz;
elevated_Light, total_pairs - elevated_Light];
% % Perform chi-squared test and compute effect size
% [chi2_40Hz_Light, p_40Hz_Light, V_40Hz_Light] = analyzeChiSquared(table_40Hz_Light, total_pairs);
%
% % Display results
% fprintf('Results for 40Hz vs Light:\n');
% fprintf('Chi-squared (X^2): %.2f\n', chi2_40Hz_Light);
% fprintf('p-value: %.4f\n', p_40Hz_Light);
% fprintf('Cramér''s V (Effect size): %.4f\n', V_40Hz_Light);
%% Chi Squared Tests: 40vLight, 40vRandom for Lower and Upper Alpha Fig4D stats - OLD
% Contingency tables for the tests
% Lower Alpha:
% 40Hz vs Light
observed_LowerAlpha_40vLight = [93, 403;
32, 464];
% Lower Alpha: 40Hz vs Random
observed_LowerAlpha_40vRandom = [93, 403;
7, 489];
% Upper Alpha:
% 40Hz vs Light
observed_UpperAlpha_40vLight = [63, 433;
7, 489];
% Upper Alpha: 40Hz vs Random
observed_UpperAlpha_40vRandom = [63, 433;
267, 229];
% Perform chi-squared tests
fprintf('Lower Alpha (40Hz vs Light):\n');
[chi2_LA_40vLight, p_LA_40vLight, V_LA_40vLight, dof_LA_40vLight, total_LA_40vLight] = chi_squared_test(observed_LowerAlpha_40vLight);
fprintf('\nLower Alpha (40Hz vs Random):\n');
[chi2_LA_40vRandom, p_LA_40vRandom, V_LA_40vRandom, dof_LA_40vRandom, total_LA_40vRandom] = chi_squared_test(observed_LowerAlpha_40vRandom);
fprintf('\nUpper Alpha (40Hz vs Light):\n');
[chi2_UA_40vLight, p_UA_40vLight, V_UA_40vLight, dof_UA_40vLight, total_UA_40vLight] = chi_squared_test(observed_UpperAlpha_40vLight);
fprintf('\nUpper Alpha (40Hz vs Random):\n');
[chi2_UA_40vRandom, p_UA_40vRandom, V_UA_40vRandom, dof_UA_40vRandom, total_UA_40vRandom] = chi_squared_test(observed_UpperAlpha_40vRandom);
% FDR correction:
% Lower Alpha p-values
p_values_LowerAlpha = [p_LA_40vLight, p_LA_40vRandom];
% Upper Alpha p-values
p_values_UpperAlpha = [p_UA_40vLight, p_UA_40vRandom];
% Apply FDR correction to Lower Alpha and Upper Alpha
adjusted_p_LowerAlpha = fdr_correction(p_values_LowerAlpha);
adjusted_p_UpperAlpha = fdr_correction(p_values_UpperAlpha);
% Display results
disp('FDR-corrected p-values for Lower Alpha:');
disp(adjusted_p_LowerAlpha);
disp('FDR-corrected p-values for Upper Alpha:');
disp(adjusted_p_UpperAlpha);
% Display FDR-corrected p-values for Lower Alpha in scientific notation
disp('FDR-corrected p-values for Lower Alpha (scientific notation):');
fprintf('%.15e\n', adjusted_p_LowerAlpha);
% Display FDR-corrected p-values for Upper Alpha in scientific notation
disp('FDR-corrected p-values for Upper Alpha (scientific notation):');
fprintf('%.15e\n', adjusted_p_UpperAlpha);
%% Chi-squared test of top quartile ch pairs & permutated channel pairs - no longer needed 1/16/24
% % Assuming AboveThresholdChannelPairAllGroupsTopQuart and
% % AboveThresholdChannelPairAllGroupsPermutated are already loaded.
%
% % Set denominator for proportions
% denominator = 496;
%
% % Extract dimensions
% dims = size(AboveThreholdChannelPairAllGroupsTopQuart);
%
% % Initialize matrices for storing results
% chi2_stat = zeros(dims); % Chi-squared statistic
% p_value = zeros(dims); % P-value
% h_test = zeros(dims); % Hypothesis test result (1: reject null, 0: fail to reject)
%
% % Loop through each element
% for i = 1:dims(1)
% for j = 1:dims(2)
% for k = 1:dims(3)
% % Observed data
% obs1 = AboveThreholdChannelPairAllGroupsTopQuart(i,j,k);
% obs2 = AboveThreholdChannelPairAllGroupsPermutated(i,j,k);
%
% % Proportions
% prop1 = obs1 / denominator;
% prop2 = obs2 / denominator;
%
% % Pooled proportion under null hypothesis
% pooled_p = (obs1 + obs2) / (2 * denominator);
%
% % Expected counts under null hypothesis
% exp1 = pooled_p * denominator;
% exp2 = pooled_p * denominator;
%
% % Chi-squared statistic for this pair
% chi2_stat(i,j,k) = ((obs1 - exp1)^2 / exp1) + ((obs2 - exp2)^2 / exp2);
%
% % Degrees of freedom
% df = 1;
%
% % Compute p-value
% p_value(i,j,k) = 1 - chi2cdf(chi2_stat(i,j,k), df);
%
% % Hypothesis test: reject null if p < 0.05
% h_test(i,j,k) = p_value(i,j,k) < 0.05;
% end
% end
% end
%
% % Display results for inspection
% disp('Chi-squared statistics:');
% disp(chi2_stat);
%
% disp('P-values (3D matrix):');
% disp(p_value);
%
% disp('Hypothesis test results (3D matrix, 1: reject null, 0: fail to reject):');
% disp(h_test);
%% WPLI figure (Both Controls)
% figure;
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<BOI(2,iBOI));
% for iStimGroup=1:length(SubjG)
% if iFreqFunc==3
% Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(WPLIall(SubjG{iStimGroup},NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
% MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(WPLIall(SubjG{iStimGroup},NeedI,EEGchInd,EEGchInd,iTrialType),[],2),1));
% else
% Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(WPLIall(SubjG{iStimGroup},NeedI,EEGchInd,EEGchInd,iTrialType),2));
% MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(WPLIall(SubjG{iStimGroup},NeedI,EEGchInd,EEGchInd,iTrialType),2),1));
% end
%
% EdgeColor=[0.8 0.8 0.8];
% % FC_BrainEEGLu(ChanPosColin27,MapGroup{iG,iFF,iCom},[],EdgeTh,NodeTh,EdgeColor,[])
% % axis off
%
% subplotLU(2,size(BOI,2),iStimGroup,iBOI);
% FC_BrainEEGLu(ChanPosColin27,MapGroup{iStimGroup,iBOI,iTrialType},[],FCpara)
% axis off
% if iStimGroup==2
% xlabel(BandName{iBOI});
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
% end
%
% if iBOI==1
% ylabel(GroupName{iStimGroup})
% yt=text(0,0.1,0.1,GroupName{iStimGroup},'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% end
% end
% % subplot('position',[0.5 0.51 0.3 0.01]);
% % b=colorbar('southoutside');
% % set(gca,'xtick',[],'ytick',[])
% % set(b,'position',[0.5 0.5 0.3 0.03],'Limits',[0 1],'Ticks',[0 1],'Ticklabels',PowerLab);
% % xlabel(b,'Log Normalized Power')
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6*2+3];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
% WPLI40BothControlsFigName = 'WPLI40BothControls620';
% % saveas(gcf,[SaveTemp WPLI40BothControlsFigName],'pdf');
% saveas(gcf,[SaveTemp WPLI40BothControlsFigName],'png');
% % saveas(gcf,[SaveTemp WPLI40BothControlsFigName],'epsc');
%% WPLI Difference Figure (VARIABLES NEED TO BE RENAMED IN THIS SECTION)
% figure;
% pTEdgeTh=0.1;
% %comparison between groups w/ freq data
% diffTPmap=zeros(length(ChanEEGLab),length(ChanEEGLab),size(BOI,2),length(TrialType)); % for T test
% diffRPmap=diffTPmap;
% diffTmap=diffTPmap;
%
% % Group selection: 1=40Hz, 2=Light, 3=Random, 4=LightRT
% Group1 = 1;
% Group2 = 2;
%
% for iBOI=1:size(BOI,2)-1 % minus 1 to remove BOI with 60 Hz
% for iCh=1:length(ChanEEGLab)
% for jCh=iCh+1:length(ChanEEGLab)
% [~,diffTPmap(iCh,jCh,iBOI,iTrialType),~,stats]=ttest2(Tdata{Group1,iBOI,iTrialType}(:,iCh,jCh),Tdata{Group2,iBOI,iTrialType}(:,iCh,jCh));
% [diffRPmap(iCh,jCh,iBOI,iTrialType),~,~]=ranksum(Tdata{Group1,iBOI,iTrialType}(:,iCh,jCh),Tdata{Group2,iBOI,iTrialType}(:,iCh,jCh));
% diffTmap(iCh,jCh,iBOI,iTrialType)=stats.tstat;
% end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
% Adj=diffTmap(:,:,iBOI,iTrialType);
% AdjP=diffTPmap(:,:,iBOI,iTrialType);
% Adj(AdjP>pTEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCparaT)
% axis off
%
% if iBOI==1
% yt=text(-0.0,0.1,0.1,['T, Sig-Diff FC, ' GroupName{Group1} '-' GroupName{Group2} ],'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
%
% % % subplotLU(2,size(BOI,2),2,iFF);
% % % xlabel(BName{iFF});
% % % text(0,-0.55,[BName{iFF} ' (' BName2{iFF} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
% % % if iFF==1
% % % a=ylabel('40Hz-Random');
% % % % a.Position=[0.01 0.5 0.03 0.4];
% % % % a.verticalalignment='middle';
% % % % set(a,'Position',[0.01 0.5 0.03 0.4],'Verticalalignment','middle')
% % % set(a,'Verticalalignment','middle')
% % % yt=text(-120,0,'P, 40-Rand.','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% % % end
%
% % xlabel(BName{iFF});
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% WPLIDiffFigName = ['WPLIDiff ' GroupName{Group1} '-' GroupName{Group2} ' p' num2str(pTEdgeTh)];
% sgtitle(WPLIDiffFigName)
%
%
% % saveas(gcf,[SaveTemp WPLIDiffBothControlsfigName],'pdf');
% saveas(gcf,[SaveTemp WPLIDiffFigName '.png'],'png');
% % saveas(gcf,[SaveTemp WPLIDiffBothControlsfigName],'epsc');
%% Define Group Set Names - beginning of fig4e fig4f
GroupSetsName{1} ='40andL'; % 40 and Light
GroupSetsName{2}='40andR'; % 40 and Random
GroupSetsName{3}='All3Groups'; % All three groups
%% Loop thru all groups *** fig4e fig4f
pSpearEdgeTh = 0.1;
for iGroupSet = [1 2]% 1:length(GroupSetsName)
if iGroupSet == 1
%% 40&LightRT
% included subs
Group1 = 1; % 1= 40Hz flicker group
Group2 = 6; % 2 = LightRT group
dataName = [TrialTypeName{iTrialType} ' ' FreqFuncNames{iFreqFunc} ' p' num2str(pSpearEdgeTh) ' ' GroupName{Group1} GroupName{Group2}];
IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 + LightRT
elseif iGroupSet == 2
%% 40andR
Group1 = 1; % 1= 40Hz flicker group
Group2 = 3; % 3 = Random group
dataName = [TrialTypeName{iTrialType} ' ' FreqFuncNames{iFreqFunc} ' p' num2str(pSpearEdgeTh) ' ' GroupName{Group1} GroupName{Group2}];
IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 + Random
elseif iGroupSet == 3
%% All3Groups
Group1 = 1; % 1= 40Hz flicker group
Group2 = 6; % 6 = LightRT
Group3 = 3; % 3 = Random group
dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2} GroupName{Group3}];
IncludedSubj=union(SubjG{Group1},SubjG{Group2},SubjG{Group3}); %40 + LightRT + Random
end
%% Topo plot ALPHA GENERAL
%% Define Bands of Interest
% BOI=[8 8 10; 13 10 13];
% BandName={'Alpha','LowAlpha','HighAlpha'};
% BandHzName={'8-13Hz','8-10Hz','10-13Hz'};
BOI=[ 8 10; 10 13];
BandName={'LowAlpha','HighAlpha'};
BandHzName={'8-10Hz','10-13Hz'};
%% Spearman Acc-FC
SpearmanAccFctopo = figure;
for iBOI=1:size(BOI,2)
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
if iFreqFunc==3
WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
else
WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
end
Acctemp=Acc(IncludedSubj);
%% Include only subjects with high accuracy (>80%) - Accuracy cuts
highAccThreshold = 0.8;
highAccSubjectsIndex = Acctemp>highAccThreshold;
WPLItemp = WPLItemp(highAccSubjectsIndex,:,:);
Acctemp80 = Acctemp(highAccSubjectsIndex);
%% loop thru each channel - calculate Spearman correlation R and p-value
for iCh=1:length(ChanEEGLab)-1
% for jCh=2:length(ChanEEGLab)
[rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),Acctemp80,'type','spearman','rows','pairwise');
% end
end
subplotLU(1,size(BOI,2),1,iBOI); %can change to 2 for second row
Adj=rSpear(:,:,iBOI,iTrialType); %32x32 of r values?
AdjP=pSpear(:,:,iBOI,iTrialType); %32x32 of p values?
Adj(AdjP>pSpearEdgeTh)=0; % deletes all r values of ch-pairs with p-value > threshold
signifchPairRAccTopo = Adj~=0; % creates 32x32 logical of significant channel pairs
signifChPairNameTopo{iFreqFunc,iTrialType,iGroupSet,iBOI,1} = getListofSignifChPairs(signifchPairRAccTopo,EEGch); % get list of channel names not equal to zero
%% Now, plot only for significant positive WPLI-accuracy correlations
% posWPLIAccChPairFolder = ['PositiveAccCorr\' GroupSetsName{iGroupSet} '\' BName{iBOI} '\'];
% mkdir([SaveTemp posWPLIAccChPairFolder])
% for iCh = 1:length(EEGchInd)
% for jCh = iCh+1:length(EEGchInd)
% % Check if the channel pair has a positive significant correlation
% if Adj(iCh, jCh) > 0
% clear DataPlot;
%
% for iStimGroup = 1:length(SubjGWPLI)
% DataPlot{iStimGroup} = squeeze(WPLIall(SubjGWPLI{iStimGroup}, :, EEGchInd(iCh), EEGchInd(jCh), iTrialType));
% Invalid = isnan(DataPlot{iStimGroup}(:, 1));
% DataPlot{iStimGroup}(Invalid, :) = [];
% end
%
% if isempty(DataPlot{1}) || isempty(DataPlot{2})
% continue;
% end
%
% iPlot = iPlot + 1;
%
% % Plot WPLI data for the channel pair
% figure;
% xrange = [0 50]; % Frequency range for x-axis
% subplot('Position', [0.1 0.1 0.88 0.88]);
% RateHist_GroupPlot(Fplot, DataPlot, FlickerColor, ParamWPLI); % Custom function for plotting
%
% text(range(xrange)/2 - 5, ParamWPLI.Ytick(end), [EEGch{iCh} '-' EEGch{jCh}], 'FontSize', 10); % Add channel pair label
% set(gca, 'xlim', xrange, 'xtick', [1 4 8 13 30 40 50 60 80 100], 'ylim', [0 0.2], 'ytick', ParamWPLI.Ytick);
% xlabel('Frequency (Hz)');
% ylabel('WPLI');
%
% % Formatting for grid and font size
% ax = gca;
% ax.XGrid = 'on';
% ax.YGrid = 'off';
% ax.FontSize = 7;
%
% % Set paper size and save the figure
% LuFontStandard; % Custom function for standard fonts
% papersizePX = 1.3 * [0 0 5 3.2]; % Custom figure size
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf, 'PaperPosition', papersizePX, 'PaperSize', papersizePX(3:4));
%
% % Save the figure in SVG format
% % saveas(gcf, [SaveTemp 'PositiveAccCorr\' '40LightRandBands_' EEGch{iCh} '-' EEGch{jCh}], 'svg');
% saveas(gcf, [SaveTemp posWPLIAccChPairFolder 'WPLI_40LR_' EEGch{iCh} '-' EEGch{jCh}], 'png');
% end
% end
% end
% figure(SpearmanAccFctopo);
%% PLotting and axises ****fig4e
FC_BrainEEGLu(ChanPosColin27,Adj,[sum(Adj,1)/2],FCspear) % plotting function! (3rd parameter is node weight)
nSignifChPairs = sum(AdjP<pSpearEdgeTh & AdjP>0, 'all');
axis off
if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-Acc','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
text(-0.11,0,-0.1,['nChpairs=' num2str(nSignifChPairs)],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==2
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0]) % MKA 9/23
% title(dataName, 'Units', 'normalized', 'Position', [0, 0.9, 0])
end
%% Display the list of significant channel pairs below the subplot
% signifChPairNames = getListofSignifChPairs(signifchPairRAccTopo, EEGch);
% % text(0.5, -0.2, strjoin(signifChPairNames, ', '), 'Units', 'normalized', 'HorizontalAlignment', 'center', 'FontSize', 8);
% % verticalSignifChPairs = strjoin(signifChPairNames, '\n');
% % text(-0.11, -0.2, verticalSignifChPairs, 'Units', 'normalized', 'HorizontalAlignment', 'center', 'FontSize', 8);
%
% % New subplot for channel pair names (as three columns)
% subplotLU(2, size(BOI, 2), 2, iBOI); % New row (2nd row) for the names
%
% % Divide the list into three columns
% numNames = length(signifChPairNames);
% numPerCol = ceil(numNames / 3);
%
% % Split the list of significant channel pairs into three columns
% col1 = signifChPairNames(1:numPerCol);
% col2 = signifChPairNames(numPerCol+1:min(2*numPerCol, numNames));
% col3 = signifChPairNames(2*numPerCol+1:end);
%
% % Prepare the text to display in columns
% colText = sprintf('%s\n', col1{:});
% colText2 = sprintf('%s\n', col2{:});
% colText3 = sprintf('%s\n', col3{:});
%
% % Display the three columns of significant channel pairs
% text(0.2, 0.5, colText, 'Units', 'normalized', 'HorizontalAlignment', 'left', 'FontSize', 6);
% text(0.5, 0.5, colText2, 'Units', 'normalized', 'HorizontalAlignment', 'left', 'FontSize', 6);
% text(0.8, 0.5, colText3, 'Units', 'normalized', 'HorizontalAlignment', 'left', 'FontSize', 6);
% axis off
end
%% Set up and save figure
LuFontStandard;
papersizePX=[0 0 6*size(BOI,2) 6+2]; % size of paper - height and width of brain plot 9/10. gain increase the papersize to try to increase the resolution
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
SpearmanFCAccName1 = ['SpearmanFCAcc_ALPHA' '_' dataName];
% saveas(gcf,[SaveTemp SpearmanFCAccName1],'pdf');
saveas(gcf,[SaveTemp SpearmanFCAccName1 '.png'],'png');
print(gcf, '-dsvg', [SaveTemp SpearmanFCAccName1 '.svg'] , '-r0'); % -r0 ensures full vector output.
% saveas(gcf,[SaveTemp SpearmanFCAccName1],'svg'); % -r0 ensures full vector output.);
% saveas(gcf,[SaveTemp SpearmanFCAccName1 '.eps'],'epsc');
close all
%% Spearman RT-FC - Topo plot GENERAL ALPHA ***fig4f
SpearmanRTFCtopo = figure;
for iBOI=1:size(BOI,2)
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
if iFreqFunc==3
WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
else
WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
end
RTtemp=SubjsAvgRT(IncludedSubj);
%% Include only subjects with high accuracy (>80%) - Accuracy cuts
highAccThreshold = 0.8;
highAccSubjectsIndex = Acctemp>highAccThreshold;
WPLItemp = WPLItemp(highAccSubjectsIndex,:,:);
RTtemp = RTtemp(highAccSubjectsIndex);
%%
for iCh=1:length(ChanEEGLab)-1
% for jCh=2:length(ChanEEGLab)
[rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),RTtemp,'type','spearman','rows','pairwise');
% end
end
subplotLU(1,size(BOI,2),1,iBOI); %can change to 2 for second row
Adj=rSpear(:,:,iBOI,iTrialType); %32x32 of r values?
AdjP=pSpear(:,:,iBOI,iTrialType); %32x32 of p values?
Adj(AdjP>pSpearEdgeTh)=0;
signifchPairRRTTopo = Adj~=0; % creates 32x32 logical of significant channel pairs
signifChPairNameTopo{iFreqFunc,iTrialType,iGroupSet,iBOI,2} = getListofSignifChPairs(signifchPairRRTTopo,EEGch); % get list of channel names not equal to zero
%% Now, plot only for significant negative WPLI-accuracy correlations
% negWPLIRTChPairFolder = ['NegativeRTCorr\' GroupSetsName{iGroupSet} '\' BName{iBOI} '\'];
% mkdir([SaveTemp negWPLIRTChPairFolder])
% for iCh = 1:length(EEGchInd)
% for jCh = iCh+1:length(EEGchInd)
% % Check if the channel pair has a positive significant correlation
% if Adj(iCh, jCh) < 0
% clear DataPlot;
%
% for iStimGroup = 1:length(SubjGWPLI)
% DataPlot{iStimGroup} = squeeze(WPLIall(SubjGWPLI{iStimGroup}, :, EEGchInd(iCh), EEGchInd(jCh), iTrialType));
% Invalid = isnan(DataPlot{iStimGroup}(:, 1));
% DataPlot{iStimGroup}(Invalid, :) = [];
% end
%
% if isempty(DataPlot{1}) || isempty(DataPlot{2})
% continue;
% end
%
% iPlot = iPlot + 1;
%
% % Plot WPLI data for the channel pair
% figure;
% xrange = [0 50]; % Frequency range for x-axis
% subplot('Position', [0.1 0.1 0.88 0.88]);
% RateHist_GroupPlot(Fplot, DataPlot, FlickerColor, ParamWPLI); % Custom function for plotting
%
% text(range(xrange)/2 - 5, ParamWPLI.Ytick(end), [EEGch{iCh} '-' EEGch{jCh}], 'FontSize', 10); % Add channel pair label
% set(gca, 'xlim', xrange, 'xtick', [1 4 8 13 30 40 50 60 80 100], 'ylim', [0 0.2], 'ytick', ParamWPLI.Ytick);
% xlabel('Frequency (Hz)');
% ylabel('WPLI');
%
% % Formatting for grid and font size
% ax = gca;
% ax.XGrid = 'on';
% ax.YGrid = 'off';
% ax.FontSize = 7;
%
% % Set paper size and save the figure
% LuFontStandard; % Custom function for standard fonts
% papersizePX = 1.3 * [0 0 5 3.2]; % Custom figure size
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf, 'PaperPosition', papersizePX, 'PaperSize', papersizePX(3:4));
%
% % Save the figure in SVG format
% % saveas(gcf, [SaveTemp 'PositiveAccCorr\' '40LightRandBands_' EEGch{iCh} '-' EEGch{jCh}], 'svg');
% saveas(gcf, [SaveTemp negWPLIRTChPairFolder 'WPLI_40LR_' EEGch{iCh} '-' EEGch{jCh}], 'png');
% end
% end
% end
% figure(SpearmanRTFCtopo);
%% Plot & axises ***fig4f
FC_BrainEEGLu(ChanPosColin27,Adj,[sum(Adj,1)/2],FCspear)
nSignifChPairs = sum(AdjP<pSpearEdgeTh & AdjP>0, 'all');
axis off
if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-RT','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
text(-0.11,0,-0.1,['nChpairs=' num2str(nSignifChPairs)],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==2
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0]) % MKA 9/23
% title(dataName, 'Units', 'normalized', 'Position', [0, 0.9, 0])
end
%% Display the list of significant channel pairs below the subplot
% signifChPairNames = getListofSignifChPairs(signifchPairRRTTopo, EEGch);
%
% % New subplot for channel pair names (as three columns)
% subplotLU(2, size(BOI, 2), 2, iBOI); % New row (2nd row) for the names
%
% % Divide the list into three columns
% numNames = length(signifChPairNames);
% numPerCol = ceil(numNames / 3);
%
% % Split the list of significant channel pairs into three columns
% col1 = signifChPairNames(1:numPerCol);
% col2 = signifChPairNames(numPerCol+1:min(2*numPerCol, numNames));
% col3 = signifChPairNames(2*numPerCol+1:end);
%
% % Prepare the text to display in columns
% colText = sprintf('%s\n', col1{:});
% colText2 = sprintf('%s\n', col2{:});
% colText3 = sprintf('%s\n', col3{:});
%
% % Display the three columns of significant channel pairs
% text(0.2, 0.5, colText, 'Units', 'normalized', 'HorizontalAlignment', 'left', 'FontSize', 6);
% text(0.5, 0.5, colText2, 'Units', 'normalized', 'HorizontalAlignment', 'left', 'FontSize', 6);
% text(0.8, 0.5, colText3, 'Units', 'normalized', 'HorizontalAlignment', 'left', 'FontSize', 6);
% axis off
end
LuFontStandard;
papersizePX=[0 0 6*size(BOI,2) 6+2];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
SpearmanFCRTName2 = ['SpearmanFCRT_ALPHA' '_' dataName];
% saveas(gcf,[SaveTemp SpearmanFCRTName2],'pdf');
saveas(gcf,[SaveTemp SpearmanFCRTName2 '.png'],'png');
saveas(gcf,[SaveTemp SpearmanFCRTName2 '.svg'],'svg');
% saveas(gcf,[SaveTemp SpearmanFCRTName2 '.eps'],'epsc');
close all
end
%% Spearman Acc-FC - Topo plot (pre 6/18/24) 40 vs Light (All BANDS)
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 2; % 2 = Light group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 vs Light
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% WPLIijChPair = squeeze(WPLItemp(:,iCh,iCh+1:end));
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(WPLIijChPair,Acctemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-Acc','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==4
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCAccName1 = ['SpearmanFCAcc' dataName];
% saveas(gcf,[SaveTemp SpearmanFCAccName1],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCAccName1],'png');
% saveas(gcf,[SaveTemp SpearmanFCAccName1 '.eps'],'epsc');
% close all
%% Spearman Acc-FC - Topo plot (pre 6/18/24) (40 vs Light) ALPHA
% BOI=[8 8 10; 13 10 13];
% BandName={'Alpha','LowAlpha','HighAlpha'};
% % Gamma-E is 40
% BandHzName={'8-13Hz','8-10Hz','10-13Hz',};
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 2; % 2 = Light group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 + Light
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),Acctemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
%
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-Acc','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==2
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCAccName1 = ['SpearmanFCAccALPHA_p' num2str(pSpearEdgeTh*100) '_' dataName];
% % saveas(gcf,[SaveTemp SpearmanFCAccName1],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCAccName1],'png');
% % saveas(gcf,[SaveTemp SpearmanFCAccName1 '.eps'],'epsc');
% close all
%% Spearman Acc-FC - Topo plot (40 vs Random) All BANDS
% BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
% BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
% % Gamma-E is 40
% BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 3; % 3 = Random group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{1},SubjG{3}); % 40 vs Random
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
%
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),Acctemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-Acc','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
% if iBOI==4
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
% SpearmanFCAccfigName = ['SpearmanFCAcc' dataName];
% saveas(gcf,[SaveTemp SpearmanFCAccfigName],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCAccfigName],'png');
% saveas(gcf,[SaveTemp SpearmanFCAccfigName 'eps'],'epsc');
% close all
%% Spearman Acc-FC - Topo plot (40+RANDOM) ALPHA
% BOI=[8 8 10; 13 10 13];
% BandName={'Alpha','LowAlpha','HighAlpha'};
% % Gamma-E is 40
% BandHzName={'8-13Hz','8-10Hz','10-13Hz',};
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 3; % 2 = Random group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 vs Random
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
%
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),Acctemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-Acc','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==2
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCAccName1 = ['SpearmanFCAccALPHA' dataName];
% % saveas(gcf,[SaveTemp SpearmanFCAccName1],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCAccName1],'png');
% % saveas(gcf,[SaveTemp SpearmanFCAccName1 '.eps'],'epsc');
% close all
%% Spearman RT-FC - Topo plot (40+Light) ALL BANDS
% BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
% BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
% % Gamma-E is 40
% BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
%
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 2; % 2 = Light group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 vs Light
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
%
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% RTtemp=SubjsAvgRT(IncludedSubj);
% %Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),RTtemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-RT','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==4
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCRTName1 = ['SpearmanFCRT' dataName];
% saveas(gcf,[SaveTemp SpearmanFCRTName1],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCRTName1],'png');
% saveas(gcf,[SaveTemp SpearmanFCRTName1 '.eps'],'epsc');
% close all
%% Spearman RT-FC - Topo plot (40 vs Random) ALL BANDS
% BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
% BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
% % Gamma-E is 40
% BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 3; % 3 = Random group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 vs Light
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
%
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% RTtemp=SubjsAvgRT(IncludedSubj);
% %Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),RTtemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-RT','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==4
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCRTName2 = ['SpearmanFCRT' dataName];
% saveas(gcf,[SaveTemp SpearmanFCRTName2],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCRTName2],'png');
% saveas(gcf,[SaveTemp SpearmanFCRTName2 '.eps'],'epsc');
% close all
%% Spearman RT-FC - Topo plot (40+Light) ALPHA ONLY
% BOI=[8 8 10; 13 10 13];
% BandName={'Alpha','LowAlpha','HighAlpha'};
% % Gamma-E is 40
% BandHzName={'8-13Hz','8-10Hz','10-13Hz',};
%
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 2; % 2 = Light group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 vs Light
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
%
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% RTtemp=SubjsAvgRT(IncludedSubj);
% %Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),RTtemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-RT','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==4
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCRTName1 = ['SpearmanFCRTALPHA' dataName];
% % saveas(gcf,[SaveTemp SpearmanFCRTName1],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCRTName1],'png');
% saveas(gcf,[SaveTemp SpearmanFCRTName1 '.eps'],'epsc');
% close all
%% Spearman RT-FC - Topo plot (40 vs Random) ALPHA ONLY
% BOI=[8 8 10; 13 10 13];
% BandName={'Alpha','LowAlpha','HighAlpha'};
% % Gamma-E is 40
% BandHzName={'8-13Hz','8-10Hz','10-13Hz',};
%
% figure;
%
% Group1 = 1; % 1= 40Hz flicker group
% Group2 = 3; % 3 = Random group
% dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
%
% IncludedSubj=union(SubjG{Group1},SubjG{Group2}); %40 vs Random
% for iBOI=1:size(BOI,2)
% NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% if iFreqFunc==3
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),[],2));
%
% else
% WPLItemp=squeeze(FreqFunc{iFreqFunc}(WPLIall(IncludedSubj,NeedI,EEGchInd,EEGchInd,iTrialType),2));
% end
% RTtemp=SubjsAvgRT(IncludedSubj);
% %Acctemp=Acc(IncludedSubj);
%
% for iCh=1:length(ChanEEGLab)-1
% % for jCh=2:length(ChanEEGLab)
% [rSpear(iCh,iCh+1:end,iBOI,iTrialType),pSpear(iCh,iCh+1:end,iBOI,iTrialType)]=corr(squeeze(WPLItemp(:,iCh,iCh+1:end)),RTtemp,'type','spearman','rows','pairwise');
% % end
% end
% subplotLU(1,size(BOI,2),1,iBOI);
%
% Adj=rSpear(:,:,iBOI,iTrialType);
% AdjP=pSpear(:,:,iBOI,iTrialType);
% Adj(AdjP>pSpearEdgeTh)=0;
% FC_BrainEEGLu(ChanPosColin27,Adj,[],FCspear)
% axis off
% if iBOI==1
% yt=text(0,0.1,0.1,'Sig-Corr. FC-RT','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% text(-0.1,0,0.1,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% if iBOI==2
% title(dataName, 'Units', 'normalized', 'Position', [0.5, 0.9, 0])
% end
% end
%
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6+2];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
%
%
% SpearmanFCRTName2 = ['SpearmanFCRTALPHA' dataName];
% % saveas(gcf,[SaveTemp SpearmanFCRTName2],'pdf');
% saveas(gcf,[SaveTemp SpearmanFCRTName2],'png');
% % saveas(gcf,[SaveTemp SpearmanFCRTName2 '.eps'],'epsc');
% close all
end
end
close all
%% PSD fig2b
SubSavePSD=[SavePath 'PSD\'];
PowerLim=[-7 -2];
PowerLab={'-7' '-2'};
TLim=[-5 5];
TLab={'-5' '5'};
PLim=[-4 4];
PLab={'10e-4' '10e-0'};
for iFreqFunc=1:length(FreqFuncNames)
clear MapGroup diffTmap diffMap Tdata;
clear diffTPmap diffRPmap diffTmap
for iTrialType=1:length(TrialType)
% iCom=1;
SaveTemp=[SubSavePSD TrialTypeName{iTrialType} '\'];
SaveTemp=[SaveTemp FreqFuncNames{iFreqFunc} '\' ];
mkdir(SaveTemp)
% Band of interest
BOI=[5;15];
BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
%
figure;
IncludedSubj=union(SubjG{1},SubjG{2});
for iBOI=1:size(BOI,2)
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% for iCh=1:length(ChanEEGLab)
if iFreqFunc==3
PSDtemp=squeeze(FreqFunc{iFreqFunc}(LogPSD(IncludedSubj,NeedI,EEGchInd,iTrialType),[],2));
else
PSDtemp=squeeze(FreqFunc{iFreqFunc}(LogPSD(IncludedSubj,NeedI,EEGchInd,iTrialType),2));
end
Acctemp=Acc(IncludedSubj); % Behavior accuracy
% Power and acuracy correlation
[rSpear(:,iBOI,iTrialType),pSpear(:,iBOI,iTrialType)]=corr(PSDtemp,Acctemp,'type','spearman','rows','pairwise');
% end
%% Visualize the data with topoplot in eeglab
subplotLU(2,size(BOI,2),1,iBOI);
topoplot(rSpear(:,iBOI,iTrialType), ChanEEGLab,'colormap',colorMapPN,'maplimits',[-1 1]);
if iBOI==1
yt=text(-0.55,0,'EEG-Behavior R','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
b=colorbar('southoutside');set(b,'position',[0.52 0.93 0.2 0.01],'xtick',[-1 1],'xticklabel',{'-1' '1'},'xlim',[-1 1]);
xlabel(b,'PSD-Acc Correlation','verticalalignment','top')
end
subplotLU(2,size(BOI,2),2,iBOI);
topoplot(log10(pSpear(:,iBOI,iTrialType)), ChanEEGLab,'colormap',colorMapPN,'maplimits',[-4 4]); % p-value
xlabel(BandName{iBOI});
text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==1
a=ylabel('40Hz-BothControls');
% set(a,'position',[0.01 0.5 0.03 0.4],'verticalalignment','middle')
set(a,'verticalalignment','middle')
yt=text(-0.55,0,'P EEG-Behavior','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
% c=colorbar('southoutside');set(c,'position',[0.52 0.46 0.2 0.03],'xtick',[-4 0],'xticklabel',{'10e-4' '10e-0'},'xlim',[-4 0]);
% xlabel(c,'P values','verticalalignment','top')
c=colorbar('southoutside');
set(gca,'xtick',[],'ytick',[])
set(c,'position',[0.52 0.51 0.2 0.01],'Limits',[PLim(1) 0],'ticks',[PLim(1) 0],'ticklabels',PLab);
xlabel(c,'P values','verticalalignment','top')
end
end
LuFontStandard;
papersizePX=[0 0 6*size(BOI,2) 6*2+3];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SaveTemp 'SpearmanPSDAcc'],'pdf');
saveas(gcf,[SaveTemp 'SpearmanPSDAcc'],'png');
saveas(gcf,[SaveTemp 'SpearmanPSDAcc.eps'],'epsc');
close all
%% Scatter plot with behavior - PSDAcc fig
IncludedSubj=[SubjG{1}(:);SubjG{2}(:)];
FlickerID=[zeros(size(SubjG{1}(:)))+1;zeros(size(SubjG{2}(:)))+2];
Param.Corr='Spearman'; %%%Type of correlation, see Matlab function corr for more details
Param.Pth=0.05; %%%threshold of Pvalue
Param.ColorMap=colorMapPN; %%%Color map for correlation link
Param.NodeColor=repmat([0.8 0.8 0.8],6,1); %%%Color of Nodes for correlation link plot
Param.Clim=[-0.6 0.6]; %%%Color Limit for Correlation
Param.Title='Pool All Sample'; %%Any title for label the figure
Param.MarkerSize=8; %%%MarkerSize of scatter
Param.SubjIDColor=FlickerColor;
Param.SubjID=FlickerID;
for iBOI=1:size(BOI,2)
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
figure;
% for iCh=1:length(ChanEEGLab)
if iFreqFunc==3
PSDtemp=squeeze(FreqFunc{iFreqFunc}(LogPSD(IncludedSubj,NeedI,EEGchInd,iTrialType),[],2));
else
PSDtemp=squeeze(FreqFunc{iFreqFunc}(LogPSD(IncludedSubj,NeedI,EEGchInd,iTrialType),2));
end
Acctemp=Acc(IncludedSubj);
tempName=EEGch;
tempName{end+1}='Acc';
multiCorr2GroupSubplot(6,6,[PSDtemp Acctemp],size(PSDtemp,2)+1,tempName,Param)
LuFontStandard;
papersizePX=[0 0 6*6 6*6];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SaveTemp Param.Corr BandName{iBOI} BandHzName{iBOI} 'PSDAcc'],'pdf');
saveas(gcf,[SaveTemp Param.Corr BandName{iBOI} BandHzName{iBOI} 'PSDAcc'],'png');
saveas(gcf,[SaveTemp Param.Corr BandName{iBOI} BandHzName{iBOI} 'PSDAcc.eps'],'epsc');
close all
end
end
%
% close all
%
end
FreqFunc{1}=@nanmean;
FreqFunc{2}=@nanmedian;
FreqFunc{3}=@nanmax;
FreqFuncNames={'mean','median','peak'};
diffTPmap=zeros(length(ChanEEGLab),length(ChanEEGLab),size(BOI,2),length(TrialType));
diffRPmap=diffTPmap;
diffTmap=diffTPmap;
rSpear=diffTPmap;
pSpear=diffTPmap;
% % TNodeTh=10;
COHEdgeTh=0.2;
COHNodeTh=0.01;
TEdgeTh=3;
TNodeTh=0.001;
pTEdgeTh=0.05;
pSpearEdgeTh=0.05;
rSpearEdgeTh=0.1;
rSpearNodeTh=0.001;
ScaleCOH=0.5;
ScaleT=1;
ScaleSpear=0.2;
close all
%% PSD all three groups together plot- Ranktest fig2b
SubSavePSD=[SavePath 'PSD\'];
% SubSaveCOH=[SavePath 'COH\'];
ParamPSD.Ytick = [-8.1:3:-2.1];
ParamPSD.SigPlot='Ranktest';
saveDate = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
SubSavePSD=[SubSavePSD 'Ranktest_' saveDate '\'];
mkdir(SubSavePSD)
for iTrialType=3 %1:length(TrialType)
SaveTemp=[SubSavePSD TrialTypeName{iTrialType} '\'];
mkdir(SaveTemp)
SubSaveFig=[SaveTemp 'Chan\'];
mkdir(SubSaveFig)
FBand=[1 55];
%% HzAllChPSD40Random
figure;
for iCh=1:length(EEGchInd)
clear DataPlot
for iStimGroup=1:length(SubjG)
DataPlot{iStimGroup}= squeeze(LogPSD(SubjG{iStimGroup},:,EEGchInd(iCh),iTrialType));
Invalid=isnan(DataPlot{iStimGroup}(:,1));
DataPlot{iStimGroup}(Invalid,:)=[];
end
if isempty(DataPlot{1})||isempty(DataPlot{2})
continue;
end
% subplotLUpage(6,6,iCh);
ParamPSD.PathSave=[SaveTemp '40LightRandCh' EEGch{iCh}];
% figure;
% subplot('Position',[0.1 0.1 0.88 0.88])
[~,FlickComStatis{iTrialType,iCh}]=RateHist_GroupPlot(Fplot,DataPlot,FlickerColor,ParamPSD); %plot and stats,
text(27.5,-2,EEGch{iCh}); % text(27.5,0.3,EEGch{iCh}); %pre 6/14/24
set(gca,'xlim',FBand,'xtick',[1 4 8 13 30 37 39 41 43 55],'ylim',[-7 -2],'ytick',[-7 -6 -5 -4 -3 -2]);
xlabel('Frequency Hz')
ylabel('Normalized Power (Log)')
ax=gca;
ax.XGrid = 'on';
ax.YGrid = 'off';
LuFontStandard
papersizePX=[0 0 12 12];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SubSaveFig 'ThreeGroup_' EEGch{iCh}],'tiff');
saveas(gcf,[SubSaveFig 'ThreeGroup_' EEGch{iCh}],'png');
saveas(gcf,[SubSaveFig 'ThreeGroup_' EEGch{iCh}],'svg');
saveas(gcf,[SubSaveFig 'ThreeGroup_' EEGch{iCh} '.eps'],'epsc');
close all
end
%
papersizePX=[0 0 6*6 6*6];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllChPSD40LightRand' TrialTypeName{iTrialType}],'pdf');
saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllChPSD40LightRand' TrialTypeName{iTrialType}],'png');
saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllChPSD40LightRand' TrialTypeName{iTrialType}],'svg');
saveas(gcf,[SaveTemp num2str(FBand(1)) '-' num2str(FBand(2)) 'HzAllChPSD40LightRand' TrialTypeName{iTrialType} '.eps'],'epsc');
end
close all
ParamPSD.Ytick=[-8:4:0]; % reset this param to original value to not mess up other functions: ParamPSD.Ytick=[-8:4:0];
%% Set-up FOOOF save folder
fooof_starttime = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
fooof_save_folder=['FOOOF Results' '\' fooof_starttime '\'];
mkdir(fooof_save_folder)
%% Plot single FOOOF PSD
% set up fooof input variables:
allFreqs = Fplot; % = row vector of frequency values
iStimGroup = 3; % 1=40; 2=Light, 3=Random;
trialtype = 3; % 3=HIT&MISS
singleFOOOFEEGCh = 1;
subjectNumInStimGroup = 1;
input_power_spectrum = PSDall(SubjG{iStimGroup}(subjectNumInStimGroup),:,singleFOOOFEEGCh,trialtype); % size(PSDall) = 67 197 40 3
% PSDall(participants, frequencies, channels, trialtypes)
f_range = [2 55]; % f_range = fitting range - !!different upper ranges yield different results
% set default settings values - % settings = fooof model settings, in a struct
settings = struct(...
'peak_width_limits', [0.5, 12], ...
'max_n_peaks', Inf, ...
'min_peak_height', 0.0, ...
'peak_threshold', 2.0, ...
'aperiodic_mode', 'fixed', ...
'verbose', true);
return_model = 1; % return_model = boolean of whether to return the FOOOF model fit, optional
% run fooof:
firstsubjectfirstCh = fooof(Fplot, input_power_spectrum, f_range, settings, return_model);
% Plot FOOOF results:
% Extract data from the results structure
fooof_freqs = firstsubjectfirstCh.freqs; % Frequency values
power_spectrum = firstsubjectfirstCh.power_spectrum; % Original power spectrum
% fooofed_spectrum = firstsubjectfirstCh.fooofed_spectrum; % Full FOOOF fit
ap_fit = firstsubjectfirstCh.ap_fit; % Aperiodic fit
difference_spectrum = power_spectrum - ap_fit;
% fooofed_difference_spectrum = fooofed_spectrum - ap_fit;
% Plot settings
figure;
hold on;
% Plot the original power spectrum
plot(fooof_freqs, power_spectrum, 'k', 'LineWidth', 1.5, 'DisplayName', 'Power Spectrum');
% Plot the FOOOFed spectrum (from fooof results)
% plot(fooof_freqs, fooofed_spectrum, 'r', 'LineWidth', 1.5, 'DisplayName', 'FOOOFed Spectrum');
% Plot the aperiodic fit
plot(fooof_freqs, ap_fit, 'b--', 'LineWidth', 1.5, 'DisplayName', 'Aperiodic Fit');
% Plot the difference spectrum (subtracted in MATLAB)
plot(fooof_freqs, difference_spectrum, 'g--', 'LineWidth', 1.5, 'DisplayName', 'Adjusted Spectrum (PS-ApFit)');
% % Plot the FOOOFed difference spectrum (subtracted in MATLAB)
% plot(fooof_freqs, fooofed_difference_spectrum, '--', 'LineWidth', 1.5, 'DisplayName', 'fooofedSpect minus apfit');
% Set logarithmic scale for frequency
% set(gca, 'XScale', 'log'); % Logarithmic x-axis
% set(gca, 'YScale', 'log'); % Logarithmic y-axis
% Add labels, legend, and grid
xlabel('Frequency (Hz)');
ylabel('Power');
legend_handle = legend('show');
% legend('show');
grid on;
% Position the legend in the middle right of the plot
set(legend_handle, 'Location', 'east');
fooof_figname = ['S' num2str(SubjG{iStimGroup}(subjectNumInStimGroup)) '_FOOOF_' EEGch{singleFOOOFEEGCh} '_' num2str(f_range(1)) '-' num2str(f_range(2)) ' Hz'];
title(['S' num2str(SubjG{iStimGroup}(subjectNumInStimGroup)) ' (' GroupName{iStimGroup} ') - Ch:' EEGch{singleFOOOFEEGCh} ' - ' num2str(f_range(1)) '-' num2str(f_range(2)) ' Hz']);
hold off;
% Save the figure as a single file
saveas(gcf, [fooof_save_folder fooof_figname '.png']);
%% FOOOF PSD - prep for fig2c
% set up fooof input variables:
allFreqs = Fplot; % = row vector of frequency values
f_range = [2 45]; % f_range = fitting range
% set default settings values - % settings = fooof model settings, in a struct
settings = struct(...
'peak_width_limits', [0.5, 12], ...
'max_n_peaks', Inf, ...
'min_peak_height', 0.0, ...
'peak_threshold', 2.0, ...
'aperiodic_mode', 'fixed', ...
'verbose', true);
return_model = 1; % return_model = boolean of whether to return the FOOOF model fit, optional
trialType = 3;
input_power_spectrum = PSDall(SubjG{iStimGroup}(1),:,1,trialType);
dummy_fooof = fooof(Fplot, input_power_spectrum, f_range, settings, return_model);
dummy_fooof.difference_spectrum = dummy_fooof.power_spectrum - dummy_fooof.ap_fit;
nSubjPSDs = length(PSDall(:,1,1,3)); % effectively gets the total number of EEG subjects that have a PSD
nCh = length(ChanEEGLab);
emptyStruct = struct(); % Create an empty structure
clear allFooofResults allFoooFDiffPSD
allFooofResults = repmat(dummy_fooof, nSubjPSDs, nCh); % Replicate the empty structure 32 times
for iSub = 1:nSubjPSDs
for iCh=1:length(ChanEEGLab)
input_power_spectrum = PSDall(iSub,:,iCh,3); % power_spectrum = row vector of power values
% PSDall(participants, frequencies, channels, trialtypes)
% run fooof:
iSubiChFoofResults = fooof(Fplot, input_power_spectrum, f_range, settings, return_model);
% Extract data from the results structure
fooof_freqs = iSubiChFoofResults.freqs; % Frequency values
power_spectrum = iSubiChFoofResults.power_spectrum; % Original power spectrum
fooofed_spectrum = iSubiChFoofResults.fooofed_spectrum; % Full FOOOF fit
ap_fit = iSubiChFoofResults.ap_fit; % Aperiodic fit
iSubiChFoofResults.difference_spectrum = power_spectrum - ap_fit;
allFooofResults(iCh) = iSubiChFoofResults;
allFooofDiffPSD(iSub,:,iCh,trialType) = iSubiChFoofResults.difference_spectrum;
end
end
%% Plot average FOOOF results:
% % Extract data from the results structure
% fooof_freqs = iChFooofResults.freqs; % Frequency values
% power_spectrum = iChFooofResults.power_spectrum; % Original power spectrum
% fooofed_spectrum = iChFooofResults.fooofed_spectrum; % Full FOOOF fit
% ap_fit = iChFooofResults.ap_fit; % Aperiodic fit
% difference_spectrum = power_spectrum - ap_fit;
% % fooofed_difference_spectrum = fooofed_spectrum - ap_fit;
%
% % Plot settings
% figure;
% hold on;
%
% % Plot the original power spectrum
% plot(fooof_freqs, power_spectrum, 'k', 'LineWidth', 1.5, 'DisplayName', 'Power Spectrum');
%
% % Plot the FOOOFed spectrum (from fooof results)
% plot(fooof_freqs, fooofed_spectrum, 'r', 'LineWidth', 1.5, 'DisplayName', 'FOOOFed Spectrum');
%
% % Plot the aperiodic fit
% plot(fooof_freqs, ap_fit, 'b--', 'LineWidth', 1.5, 'DisplayName', 'Aperiodic Fit');
%
% % Plot the difference spectrum (subtracted in MATLAB)
% plot(fooof_freqs, difference_spectrum, 'g--', 'LineWidth', 1.5, 'DisplayName', 'powerSpect minus apfit');
%
% % % Plot the FOOOFed difference spectrum (subtracted in MATLAB)
% % plot(fooof_freqs, fooofed_difference_spectrum, '--', 'LineWidth', 1.5, 'DisplayName', 'fooofedSpect minus apfit');
%
% % Set logarithmic scale for frequency
% % set(gca, 'XScale', 'log'); % Logarithmic x-axis
% % set(gca, 'YScale', 'log'); % Logarithmic y-axis
%
% % Add labels, legend, and grid
% xlabel('Frequency (Hz)');
% ylabel('Power');
% legend('show');
% grid on;
% title(['FOOOF Analysis Results: ' num2str(f_range(1)) '-' num2str(f_range(2)) ' Hz']);
% hold off;
%%
% Predefine the number of stimulation groups, subjects, and channels
nStimGroups = 3;
nFreqs = length(fooof_freqs);
nCh = 32;
% Preallocate for average difference spectra per channel and stim group
avgDiffSpectraChannels = zeros(nStimGroups, nFreqs, nCh);
% Compute the average difference spectrum for each channel and stim group
for iStimGroup = 1:nStimGroups
for iCh = 1:nCh
% Extract difference spectra for the current stim group and channel
diffSpectraGroup = allFooofDiffPSD(SubjG{iStimGroup},:,iCh,trialtype); % Subj x Freq
% Average across subjects
avgDiffSpectraChannels(iStimGroup, :, iCh) = mean(diffSpectraGroup, 1, 'omitnan');
end
end
% Plot the average difference spectra for each channel, one plot per stim group
for iStimGroup = 1:nStimGroups
figure;
hold on;
colors = lines(nCh); % Generate distinct colors for each channel
for iCh = 1:nCh
plot(fooof_freqs, avgDiffSpectraChannels(iStimGroup, :, iCh), 'LineWidth', 1.5, ...
'DisplayName', EEGch{iCh}, 'Color', colors(iCh, :));
end
% Customize the plot
xlabel('Frequency (Hz)');
ylabel('Difference Spectrum Power');
title(['Average Difference Spectrum by Channel - Stim Group ' GroupName{iStimGroup}]);
legend('show', 'Location', 'eastoutside');
grid on;
hold off;
% Save the figure
saveas(gcf, [fooof_save_folder 'Avg_Diff_Spectrum_by_Channel_StimGroup' GroupName{iStimGroup} '.png']);
end
%% FOOOFed Spectrum Plot + settings
figure;
hold on;
% Plot the original power spectrum
plot(fooof_freqs, power_spectrum, 'k', 'LineWidth', 1.5, 'DisplayName', 'Power Spectrum');
% Plot the FOOOFed spectrum (from fooof results)
% plot(fooof_freqs, fooofed_spectrum, 'r', 'LineWidth', 1.5, 'DisplayName', 'FOOOFed Spectrum');
% Plot the aperiodic fit
plot(fooof_freqs, ap_fit, 'b--', 'LineWidth', 1.5, 'DisplayName', 'Aperiodic Fit');
% Plot the difference spectrum (subtracted in MATLAB)
plot(fooof_freqs, difference_spectrum, 'g--', 'LineWidth', 1.5, 'DisplayName', 'Adjusted Spectrum (PS-ApFit)');
% % Plot the FOOOFed difference spectrum (subtracted in MATLAB)
% plot(fooof_freqs, fooofed_difference_spectrum, '--', 'LineWidth', 1.5, 'DisplayName', 'fooofedSpect minus apfit');
% Set logarithmic scale for frequency
% set(gca, 'XScale', 'log'); % Logarithmic x-axis
% set(gca, 'YScale', 'log'); % Logarithmic y-axis
% Add labels, legend, and grid
xlabel('Frequency (Hz)');
ylabel('Power');
legend_handle = legend('show');
% legend('show');
grid on;
% Position the legend in the middle right of the plot
set(legend_handle, 'Location', 'east');
fooof_figname = ['S' num2str(SubjG{iStimGroup}(subjectNumInStimGroup)) '_FOOOF_' EEGch{singleFOOOFEEGCh} '_' num2str(f_range(1)) '-' num2str(f_range(2)) ' Hz'];
title(['S' num2str(SubjG{iStimGroup}(subjectNumInStimGroup)) ' (' GroupName{iStimGroup} ') - Ch:' EEGch{singleFOOOFEEGCh} ' - ' num2str(f_range(1)) '-' num2str(f_range(2)) ' Hz']);
hold off;
% Save the figure as a single file
saveas(gcf, [fooof_save_folder fooof_figname '.png']);
%% PSD topographical Plots on Brain (heatmaps) - group difference, PSD-Acc correlation brain *** fig2c fig2d fig3
PowerLim=[-7 -2];
PowerLab={'-7' '-2'};
TLim=[-5 5];
TLab={'-5' '5'};
PLim=[-4 4];
PLab={'10e-4' '10e-0'};
fooof_freqs = firstsubjectfirstCh.freqs; % Frequency values
for iFreqFunc=3 %1:length(FreqFuncNames)
clear MapGroup diffTmap diffMap Tdata;
clear diffTPmap diffRPmap diffTmap
for iTrialType=3%1:length(TrialType)
% iCom=1;
PSDsaveDate = datestr(datetime, 'yy-mm-dd_HHMMSSFFF');
SaveTemp=['Fig2Results\' PSDsaveDate '\'];
SaveTemp=[SaveTemp FreqFuncNames{iFreqFunc} '\' ];
mkdir(SaveTemp)
BOI=[5;15];
BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
%
%% PSD on brain ***old Fig2C
% figure;
for iBOI=1:size(BOI,2)
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
for iStimGroup=1:length(SubjG)
if iFreqFunc==3
Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(LogPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),[],2));
MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(LogPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),[],2),1));
else
Tdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(LogPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),2));
MapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(LogPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),2),1));
end
subplotLU(2,size(BOI,2),iStimGroup,iBOI); %
[~,~,~,xmesh,ymesh]=topoplot(MapGroup{iStimGroup,iBOI,iTrialType}, ChanEEGLab,'colormap',jet,'maplimits',PowerLim);
if iStimGroup==2
xlabel(BandName{iBOI});
text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
end
if iBOI==1
ylabel(GroupName{iStimGroup})
yt=text(-0.55,0,GroupName{iStimGroup},'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
end
end
% subplot('position',[0.5 0.51 0.3 0.01]);
% b=colorbar('southoutside');
% set(gca,'xtick',[],'ytick',[])
% set(b,'position',[0.5 0.5 0.3 0.03],'Limits',[0 1],'Ticks',[0 1],'Ticklabels',PowerLab);
% xlabel(b,'Log Normalized Power')
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6*2+3];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% saveas(gcf,[SaveTemp 'GroupPSDonBrain'],'pdf');
% saveas(gcf,[SaveTemp 'GroupPSDonBrain'],'png');
% saveas(gcf,[SaveTemp 'GroupPSDonBrain.eps'],'epsc');
%% FOOOF PSD on brain *** old Fig2C
% % Ensure the desired colormap is loaded
% % batlow is part of the cmocean or scientific colormaps package
% if exist('batlow', 'file') == 2
% cmap = batlow; % Load batlow if available
% else
% cmap = parula; % Fallback to parula if batlow isn't available
% end
%
% figure;
% BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
% BandName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
% BandHzName={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
% PowerLim = [-0.5 1];
% for iBOI=1:size(BOI,2)
% % NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
% NeedI=find(fooof_freqs>=BOI(1,iBOI)&fooof_freqs<=BOI(2,iBOI))';
%
% for iStimGroup=1:length(SubjG)
% if iFreqFunc==3
% FooofTdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(allFooofDiffPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),[],2));
% FooofMapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(allFooofDiffPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),[],2),1));
%
% else
% FooofTdata{iStimGroup,iBOI,iTrialType}=squeeze(FreqFunc{iFreqFunc}(allFooofDiffPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),2));
% FooofMapGroup{iStimGroup,iBOI,iTrialType}=squeeze(nanmean(FreqFunc{iFreqFunc}(allFooofDiffPSD(SubjG{iStimGroup},NeedI,EEGchInd,iTrialType),2),1));
% end
% subplotLU(3,size(BOI,2),iStimGroup,iBOI); %
% [~,~,~,xmesh,ymesh]=topoplot(FooofMapGroup{iStimGroup,iBOI,iTrialType}, ChanEEGLab,'colormap',parula,'maplimits',PowerLim);
% if iStimGroup==3
% xlabel(BandName{iBOI});
% text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
%
% end
% if iBOI==1
% ylabel(GroupName{iStimGroup})
% yt=text(-0.55,0,GroupName{iStimGroup},'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
% end
% end
% end
% % subplot('position',[0.5 0.51 0.3 0.01]);
% b=colorbar('southoutside');
% set(gca,'xtick',[],'ytick',[])
% % set(b,'position',[0.5 0.5 0.3 0.03],'Limits',[0 1],'Ticks',[0 1],'Ticklabels',PowerLab);
% xlabel(b,'(not Log Normalized) Power')
% LuFontStandard;
% papersizePX=[0 0 6*size(BOI,2) 6*2+3];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% % saveas(gcf,[SaveTemp 'FOOOFPSD'],'pdf');
% saveas(gcf,[SaveTemp 'FOOOFPSD_oldColorbarRange'],'png');
% % saveas(gcf,[SaveTemp 'PSD40BothControls.eps'],'epsc');
%% Updated FOOOF color map 1/22/25 *** Fig2C
% % Compute global 10% minimum and 90% maximum across all data
% allData = []; % Initialize an empty array to collect all data values
% for iBOI = 1:size(BOI, 2)
% for iStimGroup = 1:length(SubjG)
% % Collect all FooofMapGroup data into a single array
% allData = [allData; FooofMapGroup{iStimGroup, iBOI, iTrialType}(:)];
% end
% end
%
% % Compute 10% and 90% percentiles
% cbarMin = prctile(allData, 10);
% cbarMax = prctile(allData, 90);
%
% % Update PowerLim based on the computed values
% FOOOFPowerLim = [cbarMin, cbarMax];
%
% figure;
% for iBOI = 1:size(BOI, 2)
% NeedI = find(fooof_freqs >= BOI(1, iBOI) & fooof_freqs <= BOI(2, iBOI))';
%
% for iStimGroup = 1:3 %length(SubjG)
% subplotLU(3, size(BOI, 2), iStimGroup, iBOI);
% [~, ~, ~, xmesh, ymesh] = topoplot(FooofMapGroup{iStimGroup, iBOI, iTrialType}, ChanEEGLab, ...
% 'colormap', parula, 'maplimits', FOOOFPowerLim);
%
% if iStimGroup == 3
% xlabel(BandName{iBOI});
% text(0, -0.55, [BandName{iBOI} ' (' BandHzName{iBOI} ')'], ...
% 'horizontalalignment', 'center', 'verticalalignment', 'top', 'fontsize', 10);
% end
% if iBOI == 1
% ylabel(GroupName{iStimGroup});
% text(-0.55, 0, GroupName{iStimGroup}, ...
% 'horizontalalignment', 'center', 'verticalalignment', 'bottom', ...
% 'fontsize', 10, 'rotation', 90);
% end
% end
% end
%
% % b = colorbar('southoutside');
% % caxis(FOOOFPowerLim); % Set colorbar limits to match PowerLim
% % xlabel(b, '(not Log Normalized) Power');
%
% LuFontStandard;
% papersizePX = [0 0 6 * size(BOI, 2) 6 * 2 + 3];
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf, 'PaperPosition', papersizePX, 'PaperSize', papersizePX(3:4));
% saveas(gcf, [SaveTemp 'FOOOFPSD_updatedColorbarRange'], 'png');
%
%% Plot and save FOOOF vertical colorbar separately *** Fig2C
% figure;
%
% % Create a dummy image to generate a colorbar
% imagesc([0 1]); % Placeholder data
% colormap(parula); % Use the same colormap
% caxis(FOOOFPowerLim); % Apply the same color limits
%
% % Customize colorbar
% b = colorbar('eastoutside'); % Set colorbar orientation to vertical
%
% % Set ticks and format tick labels with 2 significant figures
% tickValues = linspace(FOOOFPowerLim(1), FOOOFPowerLim(2), 5); % Generate 5 evenly spaced tick values
% set(b, 'Ticks', tickValues); % Set tick positions
% set(b, 'TickLabels', arrayfun(@(x) sprintf('%.2g', x), tickValues, 'UniformOutput', false)); % Format tick labels
%
% % Add label to the colorbar
% ylabel(b, '(not Log Normalized) Power', 'fontsize', 12, ...
% 'rotation', 270, 'VerticalAlignment', 'bottom', ...
% 'HorizontalAlignment', 'center');
%
% % Adjust figure layout to fit the colorbar and label
% set(gca, 'Visible', 'off'); % Hide axes
% set(gcf, 'PaperUnits', 'centimeters');
% set(gcf, 'PaperPosition', [0 0 2 10]); % Adjust to fit vertical colorbar
% set(gcf, 'PaperSize', [2 10]);
%
% % Save colorbar as a PNG file
% saveas(gcf, [SaveTemp 'FOOOF_Colorbar'], 'png');
%% Group differences on brain *** old fig2d ? suppfig2
figure;
for iBOI=1:size(BOI,2)
for iCh=1:length(ChanEEGLab)
[~,diffTPmap(iCh,iBOI,iTrialType),~,stats]=ttest2(Tdata{2,iBOI,iTrialType}(:,iCh),Tdata{1,iBOI,iTrialType}(:,iCh));
[diffRPmap(iCh,iBOI,iTrialType),~,~]=ranksum(Tdata{2,iBOI,iTrialType}(:,iCh),Tdata{1,iBOI,iTrialType}(:,iCh));
diffTmap(iCh,iBOI,iTrialType)=stats.tstat;
end
subplotLU(2,size(BOI,2),1,iBOI);
topoplot(diffTmap(:,iBOI,iTrialType), ChanEEGLab,'colormap',colorMapPN,'maplimits',TLim);
if iBOI==1
yt=text(-0.55,0,['T, ' GroupName{Group1} '-' GroupName{Group2}],'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
b=colorbar('southoutside');
% set(b,'position',[0.52 0.93 0.2 0.03],'xtick',[-6 6],'xticklabel',{'-6' '6'},'xlim',[-6 6]);
set(gca,'xtick',[],'ytick',[])
set(b,'position',[0.52 0.93 0.2 0.01],'ticks',TLim,'ticklabels',TLab);
xlabel(b,'T statistics','verticalalignment','top')
end
subplotLU(2,size(BOI,2),2,iBOI);
topoplot(log10(diffTPmap(:,iBOI,iTrialType)), ChanEEGLab,'colormap',colorMapPN,'maplimits',PLim);
xlabel(BandName{iBOI});
text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==1
a=ylabel('40Hz-BothControls');
% a.Position=[0.01 0.5 0.03 0.4];
% a.verticalalignment='middle';
% set(a,'Position',[0.01 0.5 0.03 0.4],'Verticalalignment','middle')
set(a,'Verticalalignment','middle')
yt=text(-0.55,0,['P, ' GroupName{Group1} '-' GroupName{Group2}],'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
c=colorbar('southoutside');
% set(c,'position',[0.52 0.46 0.2 0.03],'xtick',[-4 0],'xticklabel',{'10e-4' '10e-0'},'xlim',[-4 0]);
set(gca,'xtick',[],'ytick',[])
set(c,'position',[0.52 0.51 0.2 0.01],'Limits',[PLim(1) 0],'ticks',[PLim(1) 0],'ticklabels',PLab);
xlabel(c,'P values','verticalalignment','top')
end
end
LuFontStandard;
papersizePX=[0 0 6*size(BOI,2) 6*2+3];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SaveTemp 'PSDDiff40BothControls'],'pdf');
saveas(gcf,[SaveTemp 'PSDDiff40BothControls'],'png');
saveas(gcf,[SaveTemp 'PSDDiff40BothControls.eps'],'epsc');
%% Group differences on brain - Calculate t-test stat and plot *** fig2d - suppfig2
% Define group pairs to compare % see: open: GroupName
groupPairs = [
1, 2; % First pair: Group1 = 1 (40 Hz), Group2 = 2 (Light)
1, 3 % Second pair: Group1 = 1, Group2 = 3 (Random)
];
% Channel subset Fp1, Cz, Oz
% Channels of interest (indices)
PSDttestChSubset = [1, 32, 16]; % [1, 32, 16] = [Fp1, Cz, Oz]
% Initialize a matrix to store p-values for FDR correction
allPValues = [];
% Loop through each group pair
for iPair = 1:size(groupPairs, 1)
Group1 = groupPairs(iPair, 1);
Group2 = groupPairs(iPair, 2);
dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} GroupName{Group1} GroupName{Group2}];
figure;
for iBOI=1:size(BOI,2)
%% calculate stats test
for iCh=1:length(ChanEEGLab)
[~,diffTPmap(iCh,iBOI,iTrialType),~,stats]=ttest2(Tdata{Group1,iBOI,iTrialType}(:,iCh),Tdata{Group2,iBOI,iTrialType}(:,iCh)); % two-sided ttest
[diffRPmap(iCh,iBOI,iTrialType),~,~]=ranksum(Tdata{Group1,iBOI,iTrialType}(:,iCh),Tdata{Group2,iBOI,iTrialType}(:,iCh));
diffTmap(iCh,iBOI,iTrialType)=stats.tstat;
end
% Collect p-values for channels of interest
for chIdx = 1:length(PSDttestChSubset)
iCh = PSDttestChSubset(chIdx);
% allPValues = [p-value, t-stat, groupPair, BOI, Ch]
allPValues(end+1, :) = [diffTPmap(iCh, iBOI, iTrialType), diffTmap(iCh, iBOI, iTrialType), iPair, iBOI, iCh]; % Store p-value with metadata
end
%% Plot T-stat
subplotLU(2,size(BOI,2),1,iBOI);
topoplot(diffTmap(:,iBOI,iTrialType), ChanEEGLab,'colormap',colorMapPN,'maplimits',TLim);
if iBOI==1
yt=text(-0.55,0,['T, ' GroupName{Group1} '-' GroupName{Group2}],'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
b=colorbar('southoutside');
% set(b,'position',[0.52 0.93 0.2 0.03],'xtick',[-6 6],'xticklabel',{'-6' '6'},'xlim',[-6 6]);
set(gca,'xtick',[],'ytick',[])
set(b,'position',[0.52 0.93 0.2 0.01],'ticks',TLim,'ticklabels',TLab);
xlabel(b,'T statistics','verticalalignment','top')
end
%% Plot p-value
subplotLU(2,size(BOI,2),2,iBOI);
topoplot(log10(diffTPmap(:,iBOI,iTrialType)), ChanEEGLab,'colormap',colorMapPN,'maplimits',PLim);
xlabel(BandName{iBOI});
text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==1
a=ylabel([GroupName{Group1} '-' GroupName{Group2}]);
% a.Position=[0.01 0.5 0.03 0.4];
% a.verticalalignment='middle';
% set(a,'Position',[0.01 0.5 0.03 0.4],'Verticalalignment','middle')
set(a,'Verticalalignment','middle')
yt=text(-0.55,0,['P, ' GroupName{Group1} '-' GroupName{Group2}],'horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
c=colorbar('southoutside');
% set(c,'position',[0.52 0.46 0.2 0.03],'xtick',[-4 0],'xticklabel',{'10e-4' '10e-0'},'xlim',[-4 0]);
set(gca,'xtick',[],'ytick',[])
set(c,'position',[0.52 0.51 0.2 0.01],'Limits',[PLim(1) 0],'ticks',[PLim(1) 0],'ticklabels',PLab);
xlabel(c,'P values','verticalalignment','top')
end
end
LuFontStandard;
papersizePX=[0 0 6*size(BOI,2) 6*2+3];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
saveas(gcf,[SaveTemp 'PSDDiff' GroupName{Group1} '-' GroupName{Group2}],'pdf');
saveas(gcf,[SaveTemp 'PSDDiff' GroupName{Group1} '-' GroupName{Group2}],'png');
saveas(gcf,[SaveTemp 'PSDDiff' GroupName{Group1} '-' GroupName{Group2} '.eps'],'epsc');
end
%% PSD ttest FDR Correction fig2d stats
% Filter p-values for the first 5 bands
numBandsToInclude = 5; % Only include the first 5 bands
% allPValues = [p-value, t-stat, groupPiar, BOI, Ch]
filteredPValues = allPValues(allPValues(:, 4) <= numBandsToInclude, :);
% Extract only the raw p-values for FDR correction
pValuesForFDR = filteredPValues(:, 1);
% % Perform FDR Correction (three different methods below)
BHFDR = mafdr(pValuesForFDR, 'BHFDR', true); % Benjamini-Hochberg FDR correction
[FDRST,Q,aPrioriProb] = mafdr(pValuesForFDR); % Storey-Tibshirani method (2002)
% fdh_bh, parameters - gets the critical p value
q=0.05;
method='pdep';
report='yes';
[fdrbh_h, fdrbh_crit_p, fdrbh_adj_p]=fdr_bh(pValuesForFDR,q,method,report);
% Add FDR corrected p-values back to the filtered results
filteredPValuesPlusFDR = [filteredPValues, fdrAdjustedPValues];
% Display Results
fprintf('Channel\tGroup Pair\tBand\tT-stat\tRaw P-value\tFDR Corrected P-value\n');
for i = 1:size(filteredPValuesPlusFDR, 1)
% Get channel name from the struct
chName = ChanEEGLab(filteredPValuesPlusFDR(i, 5)).labels; % Access the 'labels' field of the struct
% Extract group indices
group1Idx = groupPairs(filteredPValuesPlusFDR(i, 3), 1);
group2Idx = groupPairs(filteredPValuesPlusFDR(i, 3), 2);
% Get group pair names
groupPair = sprintf('%s-%s', GroupName{group1Idx}, GroupName{group2Idx});
% Get band name
band = BandName{filteredPValuesPlusFDR(i, 4)}; % Assuming BandName is a cell array
% Get t-test stat
tstat = filteredPValuesPlusFDR(i, 2);
% Get raw and FDR-corrected p-values
rawP = filteredPValuesPlusFDR(i, 1);
fdrP = filteredPValuesPlusFDR(i, 6);
% Print result
fprintf('%s\t%s\t%s\t%.4f\t%.4f\t%.4f\n', chName, groupPair, band, tstat, rawP, fdrP);
end
% Display total number of comparisons
numComparisons = size(filteredPValuesPlusFDR, 1);
fprintf('Total number of comparisons used for FDR correction: %d\n', numComparisons);
%% NEW PSD-ACC and PSD_RT 2025-02-04 MKA
%% % Set-up for PSD-Beh - Spearman PSD-Acc and PSD-RT ***fig3b & fig3c
SaveTemp = ['Fig3Results\' PSDsaveDate '\'];
mkdir(SaveTemp)
% PSD groups: 1=40Hz, 3=Random, 6=LightRT
PSDBehaviorGroup1 = 1;
PSDBehaviorGroup2 = 3;
PSDBehaviorGroup3 = 6;
allGroupNames = [GroupName{PSDBehaviorGroup1} GroupName{PSDBehaviorGroup2} GroupName{PSDBehaviorGroup3}];
dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} allGroupNames];
IncludedSubj = union(SubjG{PSDBehaviorGroup1}, union(SubjG{PSDBehaviorGroup2}, SubjG{PSDBehaviorGroup3}));
figstatsChSubset = PSDttestChSubset;
% Create separate figures for PSD-Acc and PSD-RT
figureAcc = figure;
figureRT = figure;
Acctemp = Acc(IncludedSubj);
RTtemp = SubjsAvgRT(IncludedSubj);
%%% Variables for scatterplot:
FlickerID=[zeros(size(SubjG{PSDBehaviorGroup1}(:)))+1;zeros(size(SubjG{PSDBehaviorGroup2}(:)))+2;zeros(size(SubjG{PSDBehaviorGroup3}(:)))+3];
Param.Corr='Spearman'; %%%Type of correlation, see Matlab function corr for more details
Param.Pth=0.05; %%%threshold of Pvalue
Param.EdgeColor=colorMapPN; %%%Color map for correlation link
Param.NodeColor=repmat([0.8 0.8 0.8],6,1); %%%Color of Nodes for correlation link plot
Param.Clim=[-1 1]; %%%Color Limit for Correlation
Param.Title='Pool All Sample'; %%Any title for label the figure
Param.MarkerSize=8; %%%MarkerSize of scatter
Param.SubjIDColor=FlickerColor;
Param.SubjID=FlickerID;
%%% Loop thru BOI for Fig3b and Fig3c heatmaps & plot
for iBOI = 1:size(BOI, 2)
NeedI = find(Fplot >= BOI(1, iBOI) & Fplot <= BOI(2, iBOI));
if iFreqFunc == 3
PSDtemp = squeeze(FreqFunc{iFreqFunc}(LogPSD(IncludedSubj, NeedI, EEGchInd, iTrialType), [], 2));
else
PSDtemp = squeeze(FreqFunc{iFreqFunc}(LogPSD(IncludedSubj, NeedI, EEGchInd, iTrialType), 2));
end
%% Scatterplot of correlation - indiv behavior and PSD fig3a+
%% % PSD_Acc scatter
PSDAcc_scatterplot = figure;
tempName=EEGch;
tempName{end+1}='Acc';
multiCorr2GroupSubplot(6,6,[PSDtemp Acctemp],size(PSDtemp,2)+1,tempName,Param)
LuFontStandard;
papersizePX=[0 0 6*6 6*6];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames],'pdf');
saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames],'png');
saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames '.eps'],'epsc');
%% % PSD_RT scatter
PSDRT_scatterplot = figure;
tempName=EEGch;
tempName{end+1}='RT';
multiCorr2GroupSubplot(6,6,[PSDtemp RTtemp],size(PSDtemp,2)+1,tempName,Param)
LuFontStandard;
papersizePX=[0 0 6*6 6*6];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames],'pdf');
saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDRT_' allGroupNames],'png');
saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDRT_' allGroupNames '.eps'],'epsc');
%% Plot PSD-Acc and PSD-RT: R values and P Values topoplots
%%% Calculate correlations & pvalues
[PSDAcc_rSpear(:, iBOI, iTrialType), PSDAcc_pSpear(:, iBOI, iTrialType)] = corr(PSDtemp, Acctemp, 'type', 'spearman', 'rows', 'pairwise');
[PSDRT_rSpear(:, iBOI, iTrialType), PSDRT_pSpear(:, iBOI, iTrialType)] = corr(PSDtemp, RTtemp, 'type', 'spearman', 'rows', 'pairwise');
%% % Plot PSD-Acc Correlation
figure(figureAcc);
%%%% Plot R value correlation topoplot PSD-Acc(fig3b)
subplotLU(2,size(BOI,2),1,iBOI);
topoplot(PSDAcc_rSpear(:,iBOI,iTrialType), ChanEEGLab,'colormap',colorMapPN,'maplimits',[-1 1]); % Changed from 'maplimits',[-1 1]
if iBOI==1
yt=text(-0.55,0,'EEG-Behavior R','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
b=colorbar('southoutside');set(b,'position',[0.52 0.93 0.2 0.01],'xtick',[-1 1],'xticklabel',{'-1' '1'},'xlim',[-1 1]);
xlabel(b,'PSD-Acc Correlation','verticalalignment','top')
end
%%%% Plot PSD-Acc P-value topoplot (fig3b supplement)
subplotLU(2,size(BOI,2),2,iBOI);
topoplot(log10(PSDAcc_pSpear(:,iBOI,iTrialType)), ChanEEGLab,'colormap',colorMapPN,'maplimits',[-4 4]);
xlabel(BandName{iBOI});
text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==1
a=ylabel(allGroupNames);
% set(a,'position',[0.01 0.5 0.03 0.4],'verticalalignment','middle')
set(a,'verticalalignment','middle')
yt=text(-0.55,0,'P EEG-Behavior','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
% c=colorbar('southoutside');set(c,'position',[0.52 0.46 0.2 0.03],'xtick',[-4 0],'xticklabel',{'10e-4' '10e-0'},'xlim',[-4 0]);
% xlabel(c,'P values','verticalalignment','top')
c=colorbar('southoutside');
set(gca,'xtick',[],'ytick',[])
set(c,'position',[0.52 0.51 0.2 0.01],'Limits',[PLim(1) 0],'ticks',[PLim(1) 0],'ticklabels',PLab);
xlabel(c,'P values','verticalalignment','top')
end
%% % Plot PSD-RT Correlation
figure(figureRT);
%%%% Plot PSD-RT R value (correlation) heatmap (fig3c)
subplotLU(2,size(BOI,2),1,iBOI);
topoplot(PSDRT_rSpear(:,iBOI,iTrialType), ChanEEGLab,'colormap',colorMapPN,'maplimits',[-1 1]); % Changed from 'maplimits',[-1 1]
if iBOI==1
yt=text(-0.55,0,'EEG-Behavior R','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
if iBOI==size(BOI,2)
b=colorbar('southoutside');set(b,'position',[0.52 0.93 0.2 0.01],'xtick',[-1 1],'xticklabel',{'-1' '1'},'xlim',[-1 1]);
xlabel(b,'PSD-RT Correlation','verticalalignment','top')
end
%%%% Plot PSD-RT P-value fig3c supp
subplotLU(2,size(BOI,2),2,iBOI);
topoplot(log10(PSDRT_pSpear(:,iBOI,iTrialType)), ChanEEGLab,'colormap',colorMapPN,'maplimits',[-4 4]);
xlabel(BandName{iBOI});
text(0,-0.55,[BandName{iBOI} ' (' BandHzName{iBOI} ')'],'horizontalalignment','center','verticalalignment','top','fontsize',10)
if iBOI==1
a=ylabel(allGroupNames);
% set(a,'position',[0.01 0.5 0.03 0.4],'verticalalignment','middle')
set(a,'verticalalignment','middle')
yt=text(-0.55,0,'P EEG-Behavior','horizontalalignment','center','verticalalignment','bottom','fontsize',10,'rotation',90);
end
%%%%% add colorbar
if iBOI==size(BOI,2)
% c=colorbar('southoutside');set(c,'position',[0.52 0.46 0.2 0.03],'xtick',[-4 0],'xticklabel',{'10e-4' '10e-0'},'xlim',[-4 0]);
% xlabel(c,'P values','verticalalignment','top')
c=colorbar('southoutside');
set(gca,'xtick',[],'ytick',[])
set(c,'position',[0.52 0.51 0.2 0.01],'Limits',[PLim(1) 0],'ticks',[PLim(1) 0],'ticklabels',PLab);
xlabel(c,'P values','verticalalignment','top')
end
end
%%% Apply formatting and save figures for heatmaps
LuFontStandard;
papersizePX = [0 0 6*size(BOI,2) 6*2+3];
set(figureAcc, 'PaperUnits', 'centimeters');
set(figureAcc, 'PaperPosition', papersizePX, 'PaperSize', papersizePX(3:4));
saveas(figureAcc, [SaveTemp 'SpearmanPSDAcc_' allGroupNames], 'png');
set(figureRT, 'PaperUnits', 'centimeters');
set(figureRT, 'PaperPosition', papersizePX, 'PaperSize', papersizePX(3:4));
saveas(figureRT, [SaveTemp 'SpearmanPSDRT_' allGroupNames], 'png');
close all;
%% Perform FDR correction for PSD_Acc and PSD_RT *** stats for fig3b and fig3c
% Define the subset of channels and bands
figstatsChSubset = [1, 32, 16]; % Indices for Fp1, Cz, Oz
numBandsToInclude = 5; % First 5 bands
% Initialize container for p-values
PSDAcc_allPValues = [];
PSDRT_allPValues = [];
%%% Extract p-values and r-values for specified channels and bands
for iBOI = 1:numBandsToInclude
for iCh = figstatsChSubset
% Get the p-value and r-value for the current channel and band
PSDAcc_pValue = PSDAcc_pSpear(iCh, iBOI, iTrialType);
PSDAcc_rValue = PSDAcc_rSpear(iCh, iBOI, iTrialType);
PSDRT_pValue = PSDRT_pSpear(iCh, iBOI, iTrialType);
PSDRT_rValue = PSDRT_rSpear(iCh, iBOI, iTrialType);
% Append to the list of all p-values with associated metadata
PSDAcc_allPValues = [PSDAcc_allPValues; PSDAcc_rValue, PSDAcc_pValue, iBOI, iCh];
PSDRT_allPValues = [PSDRT_allPValues; PSDRT_rValue, PSDRT_pValue, iBOI, iCh];
end
end
%%% Perform FDR correction
% Extract raw p-values for FDR correction
PSDAcc_pValuesForFDR = PSDAcc_allPValues(:, 2);
PSDRT_pValuesForFDR = PSDRT_allPValues(:, 2);
% Perform FDR correction
[PSDAcc_fdrAdjustedPValues,Q_Acc,aPrioriProb_Acc,R_squared_Acc] = mafdr(PSDAcc_pValuesForFDR, 'BHFDR', true);
[PSDRT_fdrAdjustedPValues,Q_RT,aPrioriProb_RT,R_squared_RT] = mafdr(PSDRT_pValuesForFDR, 'BHFDR', true);
% Append FDR-adjusted p-values
PSDAcc_allPValues = [PSDAcc_allPValues, PSDAcc_fdrAdjustedPValues];
PSDRT_allPValues = [PSDRT_allPValues, PSDRT_fdrAdjustedPValues];
%%% Display and Save Results for PSD_Acc
outputFile_Acc = fullfile(SaveTemp, 'PSD_Acc_FDR_corrected_stats.txt');
fid_Acc = fopen(outputFile_Acc, 'w');
fprintf(fid_Acc, 'Channel\tBand\tR-value\tRaw_P-value\tFDR Corrected P-value\n');
fprintf('FDR-corrected p-values for PSD-Acc:\n');
% Display total number of comparisons
numComparisons = size(PSDAcc_allPValues, 1);
fprintf('Total number of comparisons used for FDR correction: %d\n', numComparisons);
fprintf('Channel\tBand\tR-value\tRaw_P-value\tFDR Corrected P-value\n');
for i = 1:size(PSDAcc_allPValues, 1)
chName = ChanEEGLab(PSDAcc_allPValues(i, 4)).labels;
band = BandName{PSDAcc_allPValues(i, 3)};
rValue = PSDAcc_allPValues(i, 1);
rawP = PSDAcc_allPValues(i, 2);
fdrP = PSDAcc_allPValues(i, 5);
fprintf('%s\t%s\t%.4f\t%.4f\t%.4f\n', chName, band, rValue, rawP, fdrP);
fprintf(fid_Acc, '%s\t%s\t%.4f\t%.4f\t%.4f\n', chName, band, rValue, rawP, fdrP);
end
fclose(fid_Acc);
fprintf('PSD-Acc results saved to %s\n', outputFile_Acc);
%%% Display and Save Results for PSD_RT
outputFile_RT = fullfile(SaveTemp, 'PSD_RT_FDR_corrected_stats.txt');
fid_RT = fopen(outputFile_RT, 'w');
fprintf(fid_RT, 'Ch\tBand\tR-value\tRaw_P-value\tFDR Corrected P-value\n');
fprintf('FDR-corrected p-values for PSD-RT:\n');
% Display total number of comparisons
numComparisons = size(PSDRT_allPValues, 1);
fprintf('Total number of comparisons used for FDR correction: %d\n', numComparisons);
fprintf('Ch\tBand\tR-value\tRaw_P-value\tFDR Corrected P-value\n');
for i = 1:size(PSDRT_allPValues, 1)
chName = ChanEEGLab(PSDRT_allPValues(i, 4)).labels;
band = BandName{PSDRT_allPValues(i, 3)};
rValue = PSDRT_allPValues(i, 1);
rawP = PSDRT_allPValues(i, 2);
fdrP = PSDRT_allPValues(i, 5);
fprintf('%s\t%s\t%.4f\t%.4f\t%.4f\n', chName, band, rValue, rawP, fdrP);
fprintf(fid_RT, '%s\t%s\t%.4f\t%.4f\t%.4f\n', chName, band, rValue, rawP, fdrP);
end
fclose(fid_RT);
fprintf('PSD-RT results saved to %s\n', outputFile_RT);
end
end
%% PSD-Acc - Loop of correlation scatter plot? (future fig3a+b?)
FlickerColor=[31 125 184; 150 27 27; 219 129 50]/255; %40, Random, Light % blue, red, gold
for iFreqFunc=3%1:length(FunGroupName)
clear MapGroup diffTmap diffMap Tdata;
clear diffTPmap diffRPmap diffTmap
for iTrialType=3%1:length(TrialType)
% iCom=1;
SaveTemp=[SubSavePSD TrialTypeName{iTrialType} '\'];
SaveTemp=[SaveTemp FunGroupName{iFreqFunc} '\' ];
mkdir(SaveTemp)
BOI=[5;15];
BOI=[1 4 8 13 30 39.5 43;4 8 13 30 37 41.5 100];
BName={'Delta','Theta','Alpha','Beta','Gamma-1','Gamma-E','Gamma-2'};
BName2={'1-4 Hz','4-8 Hz','8-13Hz','13-30 Hz','30-37 Hz','39-41 Hz','43-100 Hz'};
PSDBehaviorGroup1 = 1;
PSDBehaviorGroup2 = 3;
PSDBehaviorGroup3 = 6; % 6 = LightRT
allGroupNames = [GroupName{PSDBehaviorGroup1} GroupName{PSDBehaviorGroup2} GroupName{PSDBehaviorGroup3}];
dataName = [TrialTypeName{iTrialType} FreqFuncNames{iFreqFunc} allGroupNames];
IncludedSubj=[SubjG{PSDBehaviorGroup1}(:);SubjG{PSDBehaviorGroup2}(:);SubjG{PSDBehaviorGroup3}(:)]; % 1 3 6 = 40, Random, Light
FlickerID=[zeros(size(SubjG{PSDBehaviorGroup1}(:)))+1;zeros(size(SubjG{PSDBehaviorGroup2}(:)))+2;zeros(size(SubjG{PSDBehaviorGroup3}(:)))+3];
Param.Corr='Spearman'; %%%Type of correlation, see Matlab function corr for more details
Param.Pth=0.05; %%%threshold of Pvalue
Param.EdgeColor=colorMapPN; %%%Color map for correlation link
Param.NodeColor=repmat([0.8 0.8 0.8],6,1); %%%Color of Nodes for correlation link plot
Param.Clim=[-1 1]; %%%Color Limit for Correlation
Param.Title='Pool All Sample'; %%Any title for label the figure
Param.MarkerSize=8; %%%MarkerSize of scatter
Param.SubjIDColor=FlickerColor;
Param.SubjID=FlickerID;
%% Scatterplot of correlation - indiv behavior and PSD
for iBOI=1:size(BOI,2)
NeedI=find(Fplot>=BOI(1,iBOI)&Fplot<=BOI(2,iBOI));
figure;
% for iCh=1:length(ChanEEGLab)
if iFreqFunc==3
PSDtemp=squeeze(FunGroup{iFreqFunc}(LogPSD(IncludedSubj,NeedI,EEGchInd,iTrialType),[],2));
else
PSDtemp=squeeze(FunGroup{iFreqFunc}(LogPSD(IncludedSubj,NeedI,EEGchInd,iTrialType),2));
end
Acctemp=Acc(IncludedSubj);
tempName=EEGch;
tempName{end+1}='Acc';
multiCorr2GroupSubplot(6,6,[PSDtemp Acctemp],size(PSDtemp,2)+1,tempName,Param)
LuFontStandard;
papersizePX=[0 0 6*6 6*6];
set(gcf, 'PaperUnits', 'centimeters');
set(gcf,'PaperPosition',papersizePX,'PaperSize',papersizePX(3:4));
% saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames],'pdf');
saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames],'png');
saveas(gcf,[SaveTemp Param.Corr BName{iBOI} BName2{iBOI} 'PSDAcc_' allGroupNames '.eps'],'epsc');
close all
end
end
%
% cl

S4_ALLSubjects_NoCut_NoNotch_2_100hz.m at commit 8afead0, no license · at the source

Overview

Authors: Matthew K. Attokaren¹, Lu Zhang^1,2, Sindhura Mettupalli¹, Annabelle C. Singer¹

ORCID iDs: Annabelle C. Singer

Institutions: Georgia Institute of Technology (United States); Emory University (United States); The Wallace H. Coulter Department of Biomedical Engineering (United States); National Institutes of Health (United States); National Institute of Mental Health (United States)

Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1229

Dates: received 26 May 2025; accepted 8 April 2026; published online 13 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1162/imag.a.1229 · PMID 42146314 · PMCID PMC13175507 · OpenAlex W4413651284

Open access: diamond, a free copy (OpenAlex)

Preprint: doi:10.1101/2025.08.25.671937

Status: code verified

Categories: EEG (modality), human (organism)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing, Physiology & signal measures

Keywords: gamma sensory stimulation, vigilance, attention, delta oscillations, alpha oscillations, functional connectivity

Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: David and Lucile Packard Foundation (David & Lucile Packard Foundation); NINDS (R01 NS109226, 2RF1NS109226); National Institute on Aging (RF1AG078736); McCamish Foundation; Friends and Alumni of Georgia Tech

Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Gamma oscillations (30–100 Hz) have long been theorized to play a key role in sensory processing and attention by coordinating neural firing across distributed neurons. Gamma oscillations can be generated internally by neural circuits during attention or exogenously by stimuli that turn on and off at gamma frequencies. However, it remains unknown if driving gamma activity via exogenous sensory stimulation affects attention. We tested the hypothesis that non-invasive audiovisual stimulation in the form of flashing lights and sounds (flicker) at 40 Hz improves attention in an attentional vigilance task and affects neural oscillations associated with attention. We recorded scalp EEG activity of healthy adults (n = 62) during 1 hour of either 40 Hz audiovisual flicker, no flicker as control, or randomized flicker as sham stimulation, while subjects performed a psychomotor vigilance task. Participants exposed to 40 Hz flicker stimulation had better accuracy and faster reaction times than participants in the control groups. The 40 Hz group showed increased 40 Hz activity compared to the control groups in agreement with previous studies. Surprisingly, 40 Hz subjects had significantly lower delta power (2–4 Hz), which is associated with arousal, and higher functional connectivity in lower alpha (8–10 Hz), which is associated with attention processes. Furthermore, decreased delta power and increased lower alpha functional connectivity were correlated with better attention task performance. This study reveals how 40 Hz audiovisual stimulation improves attention performance with potential implications for therapeutic interventions for attention disorders and attention improvement.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

singerlabgt/FlickerEEGAttention_HealthyAdults_Manuscript

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 8afead01e123de2e52cdaf8a3ce5694f63251062, 20 February 2026

Languages: MATLAB (851), R (11), Python (6)

Size: 1,025 files, 868 scripts

Software Heritage: not archived

Found in: “Data and Code Availability”

Holds: README, environment (Behavior Analysis/requirements.txt), tests

Not found: license file, CITATION.cff, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (158 files), Signal Processing Toolbox (64 files), EEGLAB (27 files), SPM (14 files), CircStat (10 files), fdr_bh (Benjamini-Hochberg FDR) (9 files), Wavelet Toolbox (9 files), Image Processing Toolbox (8 files), Parallel Computing Toolbox (5 files), NumPy (4 files), pandas (4 files), FieldTrip (3 files), SciPy (3 files), Chronux (2 files), ERPLAB (2 files), ICLabel (2 files), Matplotlib (2 files), seaborn (2 files), Violinplot-Matlab (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

869 files

Behavior Analysis/PlotAllAttentionTaskFigsScript.py, Python, 45 lines
Behavior Analysis/StatsTest_Attention.py, Python, 202 lines
Behavior Analysis/dataframe_creation.py, Python, 151 lines
Behavior Analysis/plot_fig1_graphs.py, Python, 58 lines
Behavior Analysis/plots_for_AttentionTask.py, Python, 434 lines
Behavior Analysis/plots_for_Fig1_illustrator.py, Python, 703 lines
EEG Analysis/Functions/CalculateAvgRTforEaSubjDotsyncTest.m, MATLAB, 8 lines
EEG Analysis/Functions/FieldName2Struct.m, MATLAB, 8 lines
EEG Analysis/Functions/GenMatCode-main/General/FieldName2Struct.m, MATLAB, 8 lines
EEG Analysis/Functions/GenMatCode-main/General/FindKeywords.m, MATLAB, 12 lines
EEG Analysis/Functions/GenMatCode-main/General/GT_FileFoundKeyWord.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/General/ImageInsert.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/General/MapFields1to2.m, MATLAB, 21 lines
EEG Analysis/Functions/GenMatCode-main/General/Merge2Stru.m, MATLAB, 4 lines
EEG Analysis/Functions/GenMatCode-main/General/MergeField.m, MATLAB, 8 lines
EEG Analysis/Functions/GenMatCode-main/General/RunRcode.m, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/General/SearchFile.m, MATLAB, 16 lines
EEG Analysis/Functions/GenMatCode-main/General/SingleStructFiledMerge.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/General/StrFieldTransfer.m, MATLAB, 13 lines
EEG Analysis/Functions/GenMatCode-main/General/StrSubIndex.m, MATLAB, 41 lines
EEG Analysis/Functions/GenMatCode-main/General/StructFiledMerge.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/General/TimeRangeModify.m, MATLAB, 57 lines
EEG Analysis/Functions/GenMatCode-main/General/TimeRangeModifyDemo.m, MATLAB, 18 lines
EEG Analysis/Functions/GenMatCode-main/General/copyFormat.m, MATLAB, 74 lines
EEG Analysis/Functions/GenMatCode-main/General/copyFormatOld.m, MATLAB, 54 lines
EEG Analysis/Functions/GenMatCode-main/General/deleteFile.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/General/deleteFormat.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/General/dirFile.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/General/fieldmerge.m, MATLAB, 9 lines
EEG Analysis/Functions/GenMatCode-main/General/loadMat_Lu.m, MATLAB, 17 lines
EEG Analysis/Functions/GenMatCode-main/General/save_parfor.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/General/uniqueStrCell.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Adj2Cyto.m, MATLAB, 55 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Adj2List.m, MATLAB, 8 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Adj2WeightList.m, MATLAB, 16 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CVAccBNLU.m, MATLAB, 123 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CVModelLearnLU.m, MATLAB, 120 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CVModelStructureLearnLU.m, MATLAB, 80 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CellMatch_str.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Cir_arcPlot.m, MATLAB, 49 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircBundlePlot.m, MATLAB, 158 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircBundlePlotEdge.m, MATLAB, 125 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosAAL.m, MATLAB, 411 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosAALYeo.m, MATLAB, 412 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosAALYeoHist.m, MATLAB, 406 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosGE.m, MATLAB, 226 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosGE1.m, MATLAB, 256 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosGE2.m, MATLAB, 323 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosPlot.m, MATLAB, 209 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CircosPre.m, MATLAB, 78 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Cluster2BrainNet.m, MATLAB, 82 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/ClusterIndividual2BrainNet.m, MATLAB, 93 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CorrMat2Degree.m, MATLAB, 18 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/CytoPrepare.m, MATLAB, 72 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Data2Corr.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Data2IDMatchPhInfo.m, MATLAB, 72 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/DemomanhattanPlot.m, MATLAB, 10 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Edge2Degree.m, MATLAB, 6 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Edge2DegreeFixCon.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/Edge2NodeIndex.m, MATLAB, 34 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/EdgeCorrPlot.m, MATLAB, 58 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/EdgeExtract.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/EdgeNode2EdgeListIndex.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/GT_CirBundleHeatPlot.m, MATLAB, 141 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/GT_CirBundlePlot.m, MATLAB, 103 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/LearnStructureLU.m, MATLAB, 192 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/LoopsFind.m, MATLAB, 445 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/LuPairRegressPlotGroup.m, MATLAB, 259 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/ParcorrfMRI.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/adj2loop.m, MATLAB, 66 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/arcPlot.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/arcPlotDemo.m, MATLAB, 33 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/barplotLu.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/bonf_holm.m, MATLAB, 117 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/colorMapCreat.m, MATLAB, 57 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/compare_loop.m, MATLAB, 30 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/consensusplot.m, MATLAB, 398 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/corr2Fisher.m, MATLAB, 9 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/corrValueRemove.m, MATLAB, 34 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/corrfMRI.m, MATLAB, 28 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/demoAdj2Loop.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/edgeFromMetaResult.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/edge_list2net.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/extract_edges_all.m, MATLAB, 51 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/fast_mo_sgn.m, MATLAB, 267 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/heterogeneity_test_random.m, MATLAB, 59 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/hrgplot.m, MATLAB, 319 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/linerrorbar.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/link_forestplot.m, MATLAB, 111 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/link_test_simple.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/mk_matrix2dag.m, MATLAB, 12 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/mutli_site_analysis.m, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/optimalleaforderLU.m, MATLAB, 386 lines
EEG Analysis/Functions/GenMatCode-main/GraphAnalysis/rose2.m, MATLAB, 109 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/InsertTimeCoor.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/KalmanFilterTrajectory.m, MATLAB, 127 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/RemoveSpeedDownArtifacts.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/RoutinMerge.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/RoutinSimilarity.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/RunningTh.m, MATLAB, 44 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/VelocityPeriStim.m, MATLAB, 142 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/VelocityRatio.m, MATLAB, 17 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Behaviors/speedCal.m, MATLAB, 32 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/Bankformat.m, MATLAB, 10 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/BehaviorEgo_Nex.m, MATLAB, 103 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/BehaviorEgo_NexNormTrace.m, MATLAB, 103 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/CauWaveDemo1.m, MATLAB, 246 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/CauWaveDemo2.m, MATLAB, 98 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/CauWaveDemo3.m, MATLAB, 117 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/ComputeMaps.m, MATLAB, 451 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/ComputeMapsJu.m, MATLAB, 372 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/ComputeMapsJu2.m, MATLAB, 386 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/Compute_placefield_map.m, MATLAB, 154 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/Compute_rate_map.m, MATLAB, 74 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DemoEgoMapCont.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DemoMapCohWavelet.m, MATLAB, 209 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DemoMapCont.m, MATLAB, 78 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DemoMapSpeed.m, MATLAB, 57 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DemoPathTrial.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DetermineXYlimits.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/DistCal.m, MATLAB, 11 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/EgoMapCont.m, MATLAB, 86 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/EgoSpikePosLu.m, MATLAB, 59 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/ExtractTrajectory.m, MATLAB, 58 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/FieldFind2D_Lu.m, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/File2PathTrial.m, MATLAB, 167 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_FieldRate1D.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_MapCont.m, MATLAB, 46 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_PhasePrecessionPlot.m, MATLAB, 79 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_PhasePrecessionPlotEXGroup.m, MATLAB, 183 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_PhasePrecessionPlotGroup.m, MATLAB, 182 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_PlaceField_1DCir.m, MATLAB, 88 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_PlaceField_1DCirPlot.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_RateHist.m, MATLAB, 78 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_rateMap.m, MATLAB, 44 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_rateMapAdaptive.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/GT_rateMapAdaptiveShuffle.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/InfieldCheck.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/KalmanFilterTrajectory.m, MATLAB, 126 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/MapCohWavelet.m, MATLAB, 372 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/MapCont.m, MATLAB, 80 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/MapCorr.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/MapSpeed.m, MATLAB, 319 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/MultiColorbar/test_multicb.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/NormPlaceField2D.m, MATLAB, 125 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/OccupancyVector_map.m, MATLAB, 204 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/Occupancy_map.m, MATLAB, 155 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/OccupyMapAdaptiveSmoothing_Nex.m, MATLAB, 327 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/OccupyMapAdaptiveSmoothing_NexLuOldVersion.m, MATLAB, 369 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/OccupyMapGroup_Nex.m, MATLAB, 39 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/OccupyMapTrial_Nex.m, MATLAB, 310 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/OccupymapAdaptiveSmoothing.m, MATLAB, 178 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/PCAMapGroup_Nex.m, MATLAB, 62 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/PathFindMotion.m, MATLAB, 21 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/PhasePrecesionUPMC.m, MATLAB, 138 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/PlaceCellMutualInfo.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/PlaceFieldAngularShift.m, MATLAB, 64 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/PlaceFieldFind2D.m, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RateMapAdaptiveShuffle_Nex.m, MATLAB, 180 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RateMapAdaptiveSmoothing_Nex.m, MATLAB, 125 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RateMapAdaptive_Nex.m, MATLAB, 192 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RateMap_Nex.m, MATLAB, 233 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RebuildDataWaveMissingPoints.m, MATLAB, 99 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RemoveRearingArtifacts.m, MATLAB, 55 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RemoveSpeedDownArtifacts.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RemoveSpeedDownArtifacts_NAT.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/RunDistDect.m, MATLAB, 42 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/SmoothDec.m, MATLAB, 108 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/Sparsity.m, MATLAB, 41 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/SpatialShuffle_Nex.m, MATLAB, 152 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/SpeedThreshold.m, MATLAB, 80 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/TsDirMotion.m, MATLAB, 69 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/TsHeadMotion_Nex.m, MATLAB, 301 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/TsHeadMotion_NexJulie.m, MATLAB, 87 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/TsTriMotionMap.m, MATLAB, 164 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/TsTriMotionMap_Nex.m, MATLAB, 336 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/VectorContinuity.m, MATLAB, 140 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/VectorSmility.m, MATLAB, 50 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/adjustMap.m, MATLAB, 6 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/autoCorrelationMap.m, MATLAB, 60 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/boxCoverage.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/boxCoverage2.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/boxCoverage3.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/boxcarSmoothing.m, MATLAB, 70 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/cicular_common_UPMC.m, MATLAB, 189 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/correlation.m, MATLAB, 65 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/dataSplit.m, MATLAB, 135 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/demo.m, MATLAB, 121 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/demo1.m, MATLAB, 130 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/fieldMotionCohere.m, MATLAB, 61 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/gridnessScore9_4.m, MATLAB, 5,967 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/mapstat.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/parameters.m, MATLAB, 419 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/pointCorr.m, MATLAB, 59 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/rateMap.m, MATLAB, 295 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/ratemapAdaptiveSmoothing.m, MATLAB, 230 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/rotatePath.m, MATLAB, 5 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/smooth2005.m, MATLAB, 633 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/smooth2a.m, MATLAB, 88 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/spatialInformation.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/spatialInformationRate.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/speed_cluster_script.m, MATLAB, 10 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/spikePos.m, MATLAB, 65 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/spikePosLu.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/stability.m, MATLAB, 157 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/testcorr.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Placecell/zeroLagCorrelation.m, MATLAB, 45 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/Pos2SpaPeriod.m, MATLAB, 51 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_MountainOutput.m, MATLAB, 281 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_MountainResult.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_MountainView.m, MATLAB, 506 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_RHD2Mat.m, MATLAB, 635 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_TsMatch.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_rhd2mdaMultiWrite.m, MATLAB, 327 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_rhd2mda_HighPassFilter.m, MATLAB, 67 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/GT_wavePlot.m, MATLAB, 89 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/Plx/APextractPlot.m, MATLAB, 74 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/Plx/APextractPlot2.m, MATLAB, 91 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/Plx/mergePlxLag.m, MATLAB, 227 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/Plx/wavePlot.m, MATLAB, 45 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/alignWaveForm.m, MATLAB, 42 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/wave_cooledit.m, MATLAB, 50 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/wave_width1.m, MATLAB, 55 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/wave_width2.m, MATLAB, 88 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/wave_width2_plx0608.m, MATLAB, 91 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/wave_width_Peak2Trough.m, MATLAB, 12 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/wave_width_exp.m, MATLAB, 57 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/SpikeSort/waveform_read_plx_linlab.m, MATLAB, 120 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/AAL/Yeo_JNeurophysiol11_MNI152/AAL2matchYeo2011.m, MATLAB, 117 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/AAL/aal2_for_SPM12/aal/aal.m, MATLAB, 60 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/AAL/aal2_for_SPM12/aal/gin_list_dlabels.m, MATLAB, 613 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/AlteredEdgeInfo.m, MATLAB, 71 lines
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EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/CVModelLearnLU.m, MATLAB, 120 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/CVModelStructureLearnLU.m, MATLAB, 80 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/CircosAALYeo.m, MATLAB, 412 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/CircosAALYeoHist.m, MATLAB, 406 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Cluster2BrainNet.m, MATLAB, 82 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/ClusterIndividual2BrainNet.m, MATLAB, 93 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/CorrMat2Degree.m, MATLAB, 18 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/CytoPrepare.m, MATLAB, 72 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Data2Corr.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/DemomanhattanPlot.m, MATLAB, 10 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/LearnStructureLU.m, MATLAB, 192 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/LoopsFind.m, MATLAB, 445 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/LoopsFindAALData.m, MATLAB, 74 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/LoopsFindAALMaxEdgeNum.m, MATLAB, 84 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Lu_3D2ROI.m, MATLAB, 121 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Lu_3D2Vox.m, MATLAB, 92 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Lu_4D2ROI.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Lu_4D2Vox.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/Mat2BrainNet.m, MATLAB, 102 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/MyWatershed.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/ParcorrfMRI.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/SigFC_Behavior_NS.m, MATLAB, 45 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/corrValueRemove.m, MATLAB, 34 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/corrfMRI.m, MATLAB, 28 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/demoAdj2Loop.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/edge_list2net.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/extract_edges_all.m, MATLAB, 51 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/heterogeneity_test_random.m, MATLAB, 59 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/linerrorbar.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/link_forestplot.m, MATLAB, 111 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/link_test_simple.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/mk_matrix2dag.m, MATLAB, 12 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/mutli_site_analysis.m, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/regress_wei.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/rose2.m, MATLAB, 109 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/voxel_ttest_remcov.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/voxel_ttest_remcov_fast.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/fMRI/voxel_ttest_remcov_fastLU.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/generate_lfp.m, MATLAB, 32 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/generate_wave_spike-.m, MATLAB, 72 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/generate_wave_spike-new.m, MATLAB, 41 lines
EEG Analysis/Functions/GenMatCode-main/Neuroscience/selectindex2.m, MATLAB, 138 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/CircStats_1Way_LU.m, MATLAB, 81 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/DirRate.m, MATLAB, 32 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/GT_CalPPC.m, MATLAB, 108 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/GT_CalPPCgpu.m, MATLAB, 141 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/GT_Spike2Phase.m, MATLAB, 203 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/GT_Spike2PhaseMorlet.m, MATLAB, 61 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/GT_cic_linear_regressBuzsaki.m, MATLAB, 52 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/HistPolarData.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/Phase2Trough.m, MATLAB, 21 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/Untitled4.m, MATLAB, 29 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/bottom_climax_change.m, MATLAB, 6 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_interface.m, MATLAB, 348 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_linear_regress.m, MATLAB, 13 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_linear_regressBuzsaki.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_ripple_vs_time.m, MATLAB, 61 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_ripple_vs_time2.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_ripple_vs_time3.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_ripple_vs_time_demo.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cic_vs_time_demo.m, MATLAB, 29 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_ADvsTheta.m, MATLAB, 90 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_cic_vs_time.m, MATLAB, 52 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_common.m, MATLAB, 225 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_common_gamma.m, MATLAB, 208 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_common_gamma_demo.m, MATLAB, 364 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_common_ghy.m, MATLAB, 135 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_common_ripple.m, MATLAB, 137 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_common_ripple_demo.m, MATLAB, 265 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_cutoutside.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_filter.m, MATLAB, 161 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_filter2.m, MATLAB, 157 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_final.m, MATLAB, 89 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_final_wave.m, MATLAB, 117 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_hilbert.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_hilbert_ghy.m, MATLAB, 132 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_human.m, MATLAB, 396 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_nonfilter.m, MATLAB, 186 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_num.m, MATLAB, 103 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_num_gamma.m, MATLAB, 91 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_num_ripple1.m, MATLAB, 86 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_num_ripple2.m, MATLAB, 45 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_plot.m, MATLAB, 17 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_transfer.m, MATLAB, 11 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cicular_write.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/circ_rLU.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/circ_regress_buzsaki.m, MATLAB, 128 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/creat_gamma.m, MATLAB, 522 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/cut_data.m, MATLAB, 7 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/main_phase_interval.m, MATLAB, 47 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mixall.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mixall_AD.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mixall_common.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mixall_ts.m, MATLAB, 12 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mmgca.m, MATLAB, 18 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mmgcf.m, MATLAB, 18 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/mmpolar.m, MATLAB, 515 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/os_score_vs_time.m, MATLAB, 64 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/oscillation_score.m, MATLAB, 47 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/phase_fit.m, MATLAB, 9 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/phase_lock_ave.m, MATLAB, 17 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/phase_lock_comput.m, MATLAB, 30 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/sum_category_theta.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/sum_category_theta_maxbin.m, MATLAB, 18 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/thetadetect.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Cicular data/wave_timerange.m, MATLAB, 130 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/CorrToCommunity.m, MATLAB, 26 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/CrCoCAng.m, MATLAB, 46 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/CrCoCC.m, MATLAB, 58 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Data1InData2Bin.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/FastPeakFind.m, MATLAB, 208 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/FieldName2Struct.m, MATLAB, 8 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/MakeTrialLFP.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/MapFields1to2.m, MATLAB, 21 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/MarkToPeriod.m, MATLAB, 16 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/MergePeriod.m, MATLAB, 31 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/MetaAnalysis.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/NANcorrcoef.m, MATLAB, 315 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/NonZeroRowAve.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Period1InNonPeriod2.m, MATLAB, 42 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Period1InPeriod2.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/Period2LFPIndex.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/PeriodFrom01.m, MATLAB, 17 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/RateHist.m, MATLAB, 75 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/RowAve.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/RowMedian.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/COHwaveletTest.m, MATLAB, 88 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/DemowcoherLU.m, MATLAB, 174 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_CrossFre.m, MATLAB, 341 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_GammaEventsThetaPhase.m, MATLAB, 671 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_LFPCoh_WL.m, MATLAB, 123 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_MultiTsTriSpectrum.m, MATLAB, 255 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_PSD_Period.m, MATLAB, 252 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_PSDthetaOld.m, MATLAB, 214 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_RippleParam.m, MATLAB, 90 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_SpecThetaAlign.m, MATLAB, 651 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_SpecThetaAlignOld.m, MATLAB, 620 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_SpecThetaExtract.m, MATLAB, 285 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_TsTriggerSpectrum.m, MATLAB, 224 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_gammaThetaField.m, MATLAB, 72 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_welchPsdTrial.m, MATLAB, 147 lines, 1 match
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/GT_welchSpecTrial.m, MATLAB, 145 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/LuMOSEwavCFandSD.m, MATLAB, 116 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/PeakPeriodFindCont.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/PowerRep.m, MATLAB, 107 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/SpectrumField2D.m, MATLAB, 76 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspecMorse_EqualTriL.m, MATLAB, 189 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspecMorse_NonEqualTriL.m, MATLAB, 189 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspecMorse_NonEqualTriL_TrialIndex.m, MATLAB, 125 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspecMorse_Trial.m, MATLAB, 65 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspec_EqualTriL.m, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspec_NonEqualTriL.m, MATLAB, 117 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspec_NonEqualTriL_TrialIndex.m, MATLAB, 135 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspec_Trial.m, MATLAB, 74 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/crossspec_TrialIndex.m, MATLAB, 98 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/ArtifactRemoveTest/ArtifactRemove.m, MATLAB, 197 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/FilterBeta.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/FilterDelta.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/FilterGamma.m, MATLAB, 96 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/FilterRipple.m, MATLAB, 183 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/FilterTheta.m, MATLAB, 66 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/Noise50HzRemove.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/ThetaNormalize.m, MATLAB, 191 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/demo.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/demoEEGLABfilter.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/demolyx.m, MATLAB, 89 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/filter_SMR.m, MATLAB, 627 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/filter_SMR_ArtReMove.m, MATLAB, 804 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter/test/demo.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/ArtifactRemoveTest/ArtifactRemove.m, MATLAB, 197 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/FilterBeta.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/FilterDelta.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/FilterGamma.m, MATLAB, 96 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/FilterRipple.m, MATLAB, 183 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/FilterTheta.m, MATLAB, 66 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/Noise50HzRemove.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/ThetaNormalize.m, MATLAB, 191 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/demo.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/demoEEGLABfilter.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/demolyx.m, MATLAB, 89 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/filter_SMR.m, MATLAB, 627 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/filter_SMR_ArtReMove.m, MATLAB, 804 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/filter1/test/demo.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/pmtmLu.m, MATLAB, 152 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/BK_ThetaPeriodDect.m, MATLAB, 105 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/BK_ThetaPeriodDect2.m, MATLAB, 120 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Coh_TrialIndex.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_CohThetaExtract.m, MATLAB, 230 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_MorseCoh.m, MATLAB, 240 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_MorseSpec.m, MATLAB, 205 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_SWMCMorSpec.m, MATLAB, 325 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_SWMFreTimeExtract.m, MATLAB, 284 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_SWMMorseSpec.m, MATLAB, 360 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_SWMSpecExtract.m, MATLAB, 277 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_SWMcspecExtract.m, MATLAB, 290 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_SpecThetaExtract.m, MATLAB, 229 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_TSTriggerSpec.m, MATLAB, 293 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/LU_WCSThetaExtract.m, MATLAB, 293 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Lag_power_eeglab.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Lag_power_time_SF2paper.m, MATLAB, 85 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Lag_power_time_SF2paper1.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Lag_power_time_SF2paper2.m, MATLAB, 135 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/OrderUC_kal.m, MATLAB, 60 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/PSDwavelet.m, MATLAB, 142 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/PeakTrough_PhaseCal.m, MATLAB, 100 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/PowerAmp_time.m, MATLAB, 47 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/PowerAmp_time_eeglab.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/PowerAmp_time_spec.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Spec_theta.m, MATLAB, 62 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/Spec_theta_demo.m, MATLAB, 132 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/XCF_power.m, MATLAB, 95 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/causality_NaviSmr.m, MATLAB, 126 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/cfc_measure_morse.m, MATLAB, 197 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/cmtmLu.m, MATLAB, 140 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/coh_NaviSmr.m, MATLAB, 90 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/coh_NexTrialData.m, MATLAB, 100 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/coh_TrialData.m, MATLAB, 100 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/computeDFT.m, MATLAB, 111 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/computeperiodogram.m, MATLAB, 115 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/corr_spec.m, MATLAB, 52 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/cwtft_LU.m, MATLAB, 666 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/kal2spec.m, MATLAB, 58 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/kal_demo.m, MATLAB, 92 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/kal_spec_perievent.m, MATLAB, 50 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/mvfreqz_ar.m, MATLAB, 252 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/psd_NaviSmr.m, MATLAB, 85 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/psd_TrialData.m, MATLAB, 133 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/pwelchTrial.m, MATLAB, 82 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/smoothCFSLu.m, MATLAB, 13 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/smoothCFSMorletMatlab.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spec_kal.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spectrograms.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spectrograms_spike.m, MATLAB, 93 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spectrograms_spike_write.m, MATLAB, 143 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spectrograms_spike_write1.m, MATLAB, 256 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spectrograms_spike_write2.m, MATLAB, 154 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/spectrograms_wave_write.m, MATLAB, 227 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/waveletLU.m, MATLAB, 381 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/wcoherLU.m, MATLAB, 381 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/wcoher_newVersion.m, MATLAB, 406 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/wcoher_newVersion2.m, MATLAB, 437 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/power/welchSpecLuTrial.m, MATLAB, 140 lines, 1 match
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/psdMTM_NaviSmr.m, MATLAB, 172 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/psd_TrialDataCovTh.m, MATLAB, 127 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/psd_TrialDataVsCov.m, MATLAB, 131 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/rippleNegPeakDetects.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/rowAddingPower.m, MATLAB, 50 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavelet/sowastest.m, MATLAB, 64 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavelet/sowastest2.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavelet/test.m, MATLAB, 121 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavelet/try.m, MATLAB, 55 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavelet/waveletLu.m, MATLAB, 151 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavelet/wtcr16.m, MATLAB, 57 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wavesigfLU.m, MATLAB, 69 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wcoherSigfLU.m, MATLAB, 132 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/welch.m, MATLAB, 181 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/welchCohLu.m, MATLAB, 158 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/welchCohLuTrial.m, MATLAB, 141 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/welchLu.m, MATLAB, 242 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/welchparse.m, MATLAB, 223 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SignalProcessing/wpli_TrialIndex.m, MATLAB, 59 lines, 2 matches
EEG Analysis/Functions/GenMatCode-main/Numeric/SingleStructFiledMerge.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/SmoothPos1D.m, MATLAB, 78 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/TsInTimerange.m, MATLAB, 9 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/TsOutTimerange.m, MATLAB, 12 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/autoCorrF_TS.m, MATLAB, 23 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/AIC_m.m, MATLAB, 73 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauAnalysisTrialNex.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauCoh_Timelag.m, MATLAB, 87 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauCoh_Timelag_try.m, MATLAB, 93 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauCoh_one2one_shuffle.m, MATLAB, 288 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauCoh_one2one_shuffleISI.m, MATLAB, 171 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauCoh_one2one_shuffle_mix.m, MATLAB, 286 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauMouseTrialNex.m, MATLAB, 240 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauMouseTrialNexTraining.m, MATLAB, 245 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauRep.m, MATLAB, 47 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/CauTrialOrderAveAR.m, MATLAB, 108 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_Condition_one2one.m, MATLAB, 292 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_Condition_one2one_new.m, MATLAB, 304 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one.m, MATLAB, 285 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_del_ts.m, MATLAB, 294 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_pyr.m, MATLAB, 262 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_ripple.m, MATLAB, 273 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_ripple_shuffle.m, MATLAB, 233 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_shuffle.m, MATLAB, 266 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_shuffle1.m, MATLAB, 268 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_shuffle2.m, MATLAB, 268 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_one2one_shuffle_pace.m, MATLAB, 265 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_vs_time.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Causality_vs_time_demo.m, MATLAB, 30 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Coherence_one2one.m, MATLAB, 104 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Condition_try.m, MATLAB, 287 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Condition_try2.m, MATLAB, 427 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/AIC_DiTrial.m, MATLAB, 86 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/CauCoh_Pair_shuffleISI.m, MATLAB, 191 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/CauCoh_Pair_shuffleTrial.m, MATLAB, 194 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/CauCoh_Timelag_Pair.m, MATLAB, 157 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/CauCoh_Timelag_Pair_demo.m, MATLAB, 35 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/CauTrialAveAR.m, MATLAB, 135 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Cau_shuffle_demo.m, MATLAB, 74 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Causality_Condition.m, MATLAB, 238 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Causality_Pair.m, MATLAB, 176 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Causality_Pair_ripple.m, MATLAB, 246 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Causality_Pair_ripple_shuffle.m, MATLAB, 214 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Causality_vs_time_Pair.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Causality_vs_time_demo.m, MATLAB, 28 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/Condition_demo.m, MATLAB, 90 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/aicbic.m, MATLAB, 163 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/armorfTest.m, MATLAB, 105 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/armorf_DiTrial.m, MATLAB, 121 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/causality_1to1.m, MATLAB, 126 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/causality_DiTrial.m, MATLAB, 155 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/causality_pair_demo.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/causality_pair_demo_theta.m, MATLAB, 32 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/resamp.m, MATLAB, 64 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Kernel&Downsample/shuffle.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/MARSpec.m, MATLAB, 46 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/armorf.m, MATLAB, 105 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/armorfNoAR.M, MATLAB, 118 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/avesti.m, MATLAB, 147 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/basdescp.m, MATLAB, 137 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/chninfo.m, MATLAB, 78 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/getliststr.m, MATLAB, 11 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/getstr.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/initdraw.m, MATLAB, 13 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/keepback-DWT xorr.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/keepfftcoher.m, MATLAB, 1,565 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/keepmsdwt.m, MATLAB, 1,516 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/msMARCau.m, MATLAB, 3,375 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/msdrawraw.m, MATLAB, 812 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/msfilter.m, MATLAB, 1,541 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/nspl.m, MATLAB, 2,801 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/plotraw.m, MATLAB, 95 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/pwcausal.m, MATLAB, 50 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/template.m, MATLAB, 944 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/NSPL/whitenv.m, MATLAB, 65 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Power_cross_spectra.m, MATLAB, 39 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/Q_m_basic.m, MATLAB, 64 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/cauOrder_DiTrial.m, MATLAB, 109 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/cau_NexTrialData.m, MATLAB, 125 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/caubekkOrder_DiTrial.m, MATLAB, 107 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality threhold/Cau.m, MATLAB, 62 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality threhold/Cau_kernel.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality threhold/Cau_mix.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality threhold/Cau_ripple.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality_MORDER_range.m, MATLAB, 7 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality_compute.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality_file.m, MATLAB, 76 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality_fre.m, MATLAB, 91 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/causality_fre_demo.m, MATLAB, 235 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/cohere_vs_time.m, MATLAB, 158 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/cohere_vs_time2.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/coherence.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/coherence_data_read.m, MATLAB, 199 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/coherence_demo.m, MATLAB, 73 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/creat.m, MATLAB, 182 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/cross_spectral.m, MATLAB, 52 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/fix_order.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/power_spectral.m, MATLAB, 29 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/PatialCau_try_condition.m, MATLAB, 88 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/causality_try1to1.m, MATLAB, 102 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/causality_try_condition.m, MATLAB, 163 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/condition_causality.m, MATLAB, 149 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/try1.m, MATLAB, 52 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/try2.m, MATLAB, 49 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/try3.m, MATLAB, 62 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/causality/try/try4.m, MATLAB, 163 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/corr_NanMat.m, MATLAB, 28 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/crossCorrF_TS.m, MATLAB, 15 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/cssm.m, MATLAB, 36 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/csum.m, MATLAB, 16 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/fast_corr.m, MATLAB, 60 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/heartbeat/wave_read_mark.m, MATLAB, 46 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/heartbeat/wave_read_mark1.m, MATLAB, 205 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/heartbeat/wave_read_mark2.m, MATLAB, 205 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/heartbeat/wave_read_mark3.m, MATLAB, 210 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/heartbeat/wave_read_mark_nexfile.m, MATLAB, 202 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/heartbeat/wave_read_mark_wavefile.m, MATLAB, 196 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/infmean.m, MATLAB, 33 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/isEven.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/makeKernel.m, MATLAB, 203 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/makeKernel65.m, MATLAB, 203 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/mdscaleLU.m, MATLAB, 818 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/meta_correlation.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/meta_net_analysis.m, MATLAB, 132 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/meta_pval.m, MATLAB, 41 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/mygaussfit.m, MATLAB, 47 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/nanzscore.m, MATLAB, 30 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/optimalleaforderLU.m, MATLAB, 386 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/partial_canoncorrLU.m, MATLAB, 8 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/partialcorrLU.m, MATLAB, 390 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/rate_histogram.m, MATLAB, 138 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/rate_histogram_kernel.m, MATLAB, 33 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/ratehistogram_TS.m, MATLAB, 32 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/ratehistogram_TS_Str.m, MATLAB, 16 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/ste.m, MATLAB, 11 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/sumskipnan.m, MATLAB, 96 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/tridisolve.m, MATLAB, 26 lines
EEG Analysis/Functions/GenMatCode-main/Numeric/uniqueStrCell.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/AdjComImagesc.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Dyn/MarkovState_HeatStrPlot.m, MATLAB, 72 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Dyn/MarkovState_Plot.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Dyn/stateSeqPlot.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/DNAPlot.m, MATLAB, 36 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/EdgeBrainEEGLu.m, MATLAB, 51 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/EdgeBrainLu.m, MATLAB, 31 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/EdgeBrainLuML.m, MATLAB, 45 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/FC_BrainEEGLu.m, MATLAB, 103 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/LeftBrainAdj.m, MATLAB, 34 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/LuAAL120Test.m, MATLAB, 59 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/MultiSliceGraph.m, MATLAB, 128 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/MultiSliceGraphEdgeWeight.m, MATLAB, 128 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/NodeBrainEEGLu.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/NodeBrainLu.m, MATLAB, 33 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/NodeBrainLuML.m, MATLAB, 100 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/SurfaceBrainLu.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/test.m, MATLAB, 49 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuBrainPlot/test1.m, MATLAB, 81 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuPairPartialRegressPlot.m, MATLAB, 64 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/LuPairRegressPlot.m, MATLAB, 60 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Multi2DDensity.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiHist_GroupPlot.m, MATLAB, 80 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiMatPlot/MultiMatrix2DMatPlot.m, MATLAB, 146 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiMatPlot/MultiMatrix2DPlot.m, MATLAB, 103 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiMatPlot/MultiMatrix2DPlotVerti.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiMatPlot/MultiMatrix3DPlot.m, MATLAB, 26 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiPolarDensity.m, MATLAB, 85 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultiTrace1DPlot.m, MATLAB, 57 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/MultibarPlot.m, MATLAB, 5 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PairMixLinearSubjRF.m, MATLAB, 317 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PeriodMarkPlot.m, MATLAB, 9 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PhaseHistPolar.m, MATLAB, 104 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Placecell/InFieldPhasePrecessionPlot.m, MATLAB, 100 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Placecell/PhasePrecessionPlot.m, MATLAB, 171 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/Placecell/PhasePrecessionPlot_MergeTrial.m, MATLAB, 62 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/LuFontStandard.m, MATLAB, 81 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/LuLegend.m, MATLAB, 35 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/MyWatershed.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/SpherePlot.m, MATLAB, 30 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/arcPlot.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/barplot.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/error_area.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/showNum.m, MATLAB, 69 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/showPerc.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/showPvalue.m, MATLAB, 44 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/subplotLU.m, MATLAB, 46 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/subplotLUpage.m, MATLAB, 41 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/PlotFormat/subplotPosLu.m, MATLAB, 44 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/SpherePlot.m, MATLAB, 31 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/arcPlotDemo.m, MATLAB, 33 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/aveY_discretizeX.m, MATLAB, 56 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/DensityDisGroup.m, MATLAB, 81 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/DensityDisGroup_WithHartigansTest.m, MATLAB, 79 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/ErrorBarHierarchy.m, MATLAB, 414 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/ErrorBarPlotLU.m, MATLAB, 512 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/ErrorBarPlotSimplePlot.m, MATLAB, 259 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/ErrorBoxPlotLU.m, MATLAB, 415 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/ErrorViolinHalf.m, MATLAB, 755 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/MultiViolinLu.m, MATLAB, 16 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/PhaseHistPolar_Bin.m, MATLAB, 106 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/PolarDensity.m, MATLAB, 81 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/PthReport.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/RateHist_GroupPlot.m, MATLAB, 578 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/RateHist_Stats.m, MATLAB, 396 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/Violin.m, MATLAB, 346 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/Violin_half.m, MATLAB, 376 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/Violin_halfLeft.m, MATLAB, 376 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/Violin_halfRight.m, MATLAB, 376 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/boxplot_Lu.m, MATLAB, 4,055 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/circ_ErrorBarPlotLU.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/colorCode2rgb.m, MATLAB, 40 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/countEntries.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/distinguishable_colors.m, MATLAB, 152 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/distributionPlot.m, MATLAB, 967 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/gscatterLU.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/hist2dLU.m, MATLAB, 66 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/histPlotLU.m, MATLAB, 31 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/histPlotLUData.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/histPlotLUPerc.m, MATLAB, 34 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/hrgplot.m, MATLAB, 319 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/manhattanPlot.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/mdscaleLU.m, MATLAB, 818 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/myErrorbar.m, MATLAB, 264 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/myHistogram.m, MATLAB, 212 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/perlLU.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/pie3LU.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/pie3s.m, MATLAB, 370 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/pieLU.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/pieLUPerc.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/plotSpread.m, MATLAB, 632 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/polar_errorArea.m, MATLAB, 111 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/polarmy.m, MATLAB, 178 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/polarmy_new.m, MATLAB, 180 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/regress_Lu.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/repeatEntries.m, MATLAB, 140 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/rose_bar.m, MATLAB, 38 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/violin1.m, MATLAB, 266 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/violinplot.m, MATLAB, 127 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/violinplot_half.m, MATLAB, 217 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/distributionPlot/weightedStats.m, MATLAB, 154 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/imagescGrid.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/linerrorbar.m, MATLAB, 134 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/multiCorr2GroupSubplot.m, MATLAB, 375 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/multiCorrVis.m, MATLAB, 343 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/mygaussfit.m, MATLAB, 47 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/ndhist.m, MATLAB, 1,065 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/plotColormap.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/rose2.m, MATLAB, 109 lines
EEG Analysis/Functions/GenMatCode-main/Plotfun/tryWatershed.m, MATLAB, 27 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/CompareSPSS/LuTest1.m, MATLAB, 26 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/CompareSPSS/LuTest2.R, R, 19 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/CompareSPSS/LuTest4.R, R, 102 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/MixAnova2F1W1B_withR.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/MixAnova3F2W1B_withR.m, MATLAB, 13 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/MixAnova3WF2W1B_withR.m, MATLAB, 19 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/MixAnova3_withR.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/MixAnova4_withR.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/MixAnova5_withR.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/MixAnova2F1W1B.R, R, 12 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/MixAnova3F2W1B.R, R, 13 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/MixAnova3RNonF1.R, R, 12 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/MixAnova4R.R, R, 13 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/MixAnova5R.R, R, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/RepAnova1.R, R, 11 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/RepAnova2.R, R, 10 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/RepAnova3.R, R, 11 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/R/anovaR.R, R, 10 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/RMAOV1.m, MATLAB, 222 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/RepAnova2_withR.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/RepAnova3_withR.m, MATLAB, 14 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/anova_rm.m, MATLAB, 216 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mC2X.m, MATLAB, 75 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mCC2X.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mDemoA.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mDispModels.m, MATLAB, 55 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mF.m, MATLAB, 63 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mFCDF.m, MATLAB, 44 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mFindInteractionTerms.m, MATLAB, 49 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mFindMainTerms.m, MATLAB, 49 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mFindTerms.m, MATLAB, 49 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mG2X.m, MATLAB, 125 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mGC2X.m, MATLAB, 108 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mGG2X.m, MATLAB, 99 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mIsInteractionTerm.m, MATLAB, 42 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mIsMainTerm.m, MATLAB, 42 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mIsMember.m, MATLAB, 57 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mLHT.m, MATLAB, 86 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mNC2.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mSVD.m, MATLAB, 86 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mStepwise.m, MATLAB, 288 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mT.m, MATLAB, 505 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mTCDF.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mTerms.m, MATLAB, 53 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mUnique.m, MATLAB, 55 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mX.m, MATLAB, 88 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mancovan_496/mancovan.m, MATLAB, 426 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mixed_between_within_anova.m, MATLAB, 185 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/mixed_design_anova.m, MATLAB, 66 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/myfriedman.m, MATLAB, 219 lines, 2 matches
EEG Analysis/Functions/GenMatCode-main/Statistics/ANOVAandMixEffect/rm_anova2.m, MATLAB, 145 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/BWAOV2.m, MATLAB, 269 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/FDR.m, MATLAB, 24 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/HartigansDipSignifTest.m, MATLAB, 30 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/HartigansDipTest.m, MATLAB, 305 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/LUbioinfochecknargin.m, MATLAB, 21 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/LUmafdr.m, MATLAB, 392 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/LUoptPartialMatch.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/LUopttf.m, MATLAB, 43 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/Meta/MetaAnalysis.m, MATLAB, 83 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/Meta/meta_correlation.m, MATLAB, 68 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/Meta/meta_net_analysis.m, MATLAB, 132 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/Meta/meta_pval.m, MATLAB, 41 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/PthReport.m, MATLAB, 48 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/Thfdr_Pmatrix.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/bonf_holm.m, MATLAB, 117 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/consensusplot.m, MATLAB, 398 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/demo_hartigans_isi.m, MATLAB, 17 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/fanofactor.m, MATLAB, 10 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/fanofactor_ex.m, MATLAB, 25 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/fdr_bh.m, MATLAB, 180 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/fdr_bky.m, MATLAB, 237 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/fexact.m, MATLAB, 409 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/fishertest.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/forest.m, MATLAB, 77 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/gretna_FDR.m, MATLAB, 37 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/gretna_fishertrans.m, MATLAB, 22 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/mackskill.m, MATLAB, 351 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/mafdr1.m, MATLAB, 425 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/multFDR.m, MATLAB, 66 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/myBinomTest.m, MATLAB, 237 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/mybarnard.m, MATLAB, 306 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/prop_test.m, MATLAB, 62 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/regress_Lu.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/regress_wei.m, MATLAB, 20 lines
EEG Analysis/Functions/GenMatCode-main/Statistics/ttest2_cov.m, MATLAB, 40 lines
EEG Analysis/Functions/MapFields1to2.m, MATLAB, 21 lines
EEG Analysis/Functions/Subfun/RateHist_LME1.m, MATLAB, 39 lines
EEG Analysis/Functions/appendingfiles_SM.m, MATLAB, 4 lines
EEG Analysis/Functions/calculateWPLI.m, MATLAB, 40 lines
EEG Analysis/Functions/fooof_mat-main/examples/fooof_example_multi_spectra.m, MATLAB, 30 lines
EEG Analysis/Functions/fooof_mat-main/examples/fooof_example_one_spectrum.m, MATLAB, 27 lines
EEG Analysis/Functions/fooof_mat-main/examples/fooof_example_plot_model.m, MATLAB, 27 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof.m, MATLAB, 84 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof_check_settings.m, MATLAB, 46 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof_get_model.m, MATLAB, 27 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof_group.m, MATLAB, 45 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof_plot.m, MATLAB, 61 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof_unpack_results.m, MATLAB, 44 lines
EEG Analysis/Functions/fooof_mat-main/fooof_mat/fooof_version.m, MATLAB, 23 lines
EEG Analysis/Functions/getMaxFrequency.m, MATLAB, 9 lines
EEG Analysis/Functions/getNewestFile.m, MATLAB, 9 lines
EEG Analysis/Functions/getNewestFolder.m, MATLAB, 9 lines
EEG Analysis/Functions/mergingEEGSets.m, MATLAB, 13 lines
EEG Analysis/Functions/plotSubsetChPSD.m, MATLAB, 56 lines
EEG Analysis/Functions/plotfooof.m, MATLAB, 48 lines
EEG Analysis/Functions/sync_EEG_to_attention_backup.m, MATLAB, 339 lines
EEG Analysis/S0_ConvertBDFtoSet_and_Preprocess_for_Syncing.m, MATLAB, 862 lines, 1 match
EEG Analysis/S1_Sync_EEGSet_to_AttentionCsv.m, MATLAB, 426 lines
EEG Analysis/S2_LoadEEG_CheckSync.m, MATLAB, 414 lines
EEG Analysis/S3_CompletePreprocessing.m, MATLAB, 862 lines, 1 match
EEG Analysis/S4_ALLSubjects_NoCut_NoNotch_2_100hz.m, MATLAB, 4,365 lines, 5 matches
EEG Analysis/Subfun/RateHist_LME1.m, MATLAB, 39 lines
README.md, Text, 18 lines

At the source: github.com/singerlabgt/FlickerEEGAttention_HealthyAdults_Manuscript

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

doi:10.18112/openneuro.ds006222.v1.0.0, at OpenNeuro; found in “Data and Code Availability”

Data and Code Availability

Data from this study is available on OpenNeuro https://doi.org/10.18112/openneuro.ds006222.v1.0.0. Code is available on GitHub: https://github.com/singerlabgt/FlickerEEGAttention_HealthyAdults_Manuscript.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 5 funders, 60 references.

Cite

This paper

Attokaren, M. K., Zhang, L., Mettupalli, S., & Singer, A. C. (2026). 40 Hz audiovisual stimulation improves sustained attention and related brain oscillations. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1229. https://doi.org/10.1162/imag.a.1229

BibTeX

@article{attokaren202640, author = {Attokaren, Matthew K. and Zhang, Lu and Mettupalli, Sindhura and Singer, Annabelle C.}, title = {{40 Hz audiovisual stimulation improves sustained attention and related brain oscillations}}, journal = {Imaging neuroscience (Cambridge, Mass.)}, year = {2026}, month = may, volume = {4}, pages = {IMAG.a.1229}, publisher = {MIT Press}, issn = {2837-6056}, doi = {10.1162/imag.a.1229}, url = {https://doi.org/10.1162/imag.a.1229}, pmid = {42146314}, pmcid = {PMC13175507} }

RIS

TY - JOUR AU - Attokaren, Matthew K. AU - Zhang, Lu AU - Mettupalli, Sindhura AU - Singer, Annabelle C. TI - 40 Hz audiovisual stimulation improves sustained attention and related brain oscillations T2 - Imaging neuroscience (Cambridge, Mass.) J2 - Imaging Neurosci (Camb) PY - 2026 DA - 2026/05/13 VL - 4 SP - IMAG.a.1229 SN - 2837-6056 PB - MIT Press DO - 10.1162/imag.a.1229 UR - https://doi.org/10.1162/imag.a.1229 LA - en ER -

CSL-JSON

{ "id": "10.1162/imag.a.1229", "type": "article-journal", "title": "40 Hz audiovisual stimulation improves sustained attention and related brain oscillations", "container-title": "Imaging neuroscience (Cambridge, Mass.)", "author": [ { "family": "Attokaren", "given": "Matthew K." }, { "family": "Zhang", "given": "Lu" }, { "family": "Mettupalli", "given": "Sindhura" }, { "family": "Singer", "given": "Annabelle C." } ], "container-title-short": "Imaging Neurosci (Camb)", "volume": "4", "page": "IMAG.a.1229", "DOI": "10.1162/imag.a.1229", "PMID": "42146314", "PMCID": "PMC13175507", "ISSN": "2837-6056", "publisher": "MIT Press", "URL": "https://doi.org/10.1162/imag.a.1229", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 13 ] ] } }

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