OSCR

Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and Methods › Paradigm and Data Acquisition › EEG Data Acquisition and Preprocessing ↔ eegPreproc.m, lines 375–418 · score 0.68 · pop_eegfiltnew, Independent Component, preprocessing, filtered, event, rejection
  2. [2] § Materials and Methods › Paradigm and Data Acquisition › EEG Data Acquisition and Preprocessing ↔ unfoldERPData.m, lines 39–163 · score 0.63 · voltage thresholds, artifacts, filtered, event, rejection, stimuli

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 898 lines · 37 KB · MIT · 1 match

  1. %
  2. % This script processes EEG data exported from BrainVision in .dat or .eeg format
  3. % to MVPAlab format. It requires corresponding .vhdr, .dat/.eeg, and .vmrk files for.
  4. % The script can handle segmented data and allows the user to specify various
  5. % cleaning and preprocessing steps, including channel removal, resampling,
  6. % filtering, epoching, baseline correction, ICA, interpolation, and more.
  7. % The final data can be saved in .set (EEGLAB format), .mat (MATLAB format), or both.
  8. %
  9. % If channel coordinates are detected to be missing, the user can load them
  10. % from a BrainVision .bvef or .vhdr file, or use layouts present in EEGLAB or
  11. % FieldTrip source files. This requires the additional eegImportChanlocs.m
  12. % script available in the same github repository.
  13. %
  14. % Once the data is saved in either .set or .mat it can be inputed once
  15. % again into the script to perform additional steps if needed.
  16. %
  17. % The script performs the following steps:
  18. % 1. Prompts the user to select cleaning steps.
  19. % 2. Prompts the user for necessary parameters for the selected steps.
  20. % 3. Selects files for processing.
  21. % 4. Processes each selected file according to the specified steps.
  22. % 5. Saves the processed data in the specified format.
  23. %
  24. % Note: If ICA and/or automatic rejection are selected, a checkpoint save
  25. % will be created in the selected format for each one before rejecting
  26. % components/trials.
  27. %
  28. % Ensure you have EEGLAB installed and added to the MATLAB path.
  29. %
  30. % Author: Dino Soldic
  31. % Email: [email hidden]
  32. % Date: 2026-04-24
  33. %
  34. % See also: eegPlotERP
  36. %% Clean Matlab
  37. clear; clc;
  39. %% Ask user for parameters
  40. cleanoptions = {'Resample', 'Single EEG filter', 'Multi EEG filter', 'ERP epoch data', 'RS epoch data', 'Correct baseline', 'Reject with ICA', ...
  41. 'Interpolate', 'Reject voltage outliers', 'Reject abnormal spectra', 'Re-reference', 'Plot ERPs', 'Transform to Fieldtrip', ...
  42. 'Transform to LORETA (RS)', 'Transform to BV'};
  43. [cleanselection, ~] = listdlg('ListString', cleanoptions, 'PromptString', 'Select cleaning steps:', 'SelectionMode', 'multiple');
  45. if isempty(cleanselection), fprintf('Operation canceled. Shutting down\n'); return, end
  47. if any(cleanselection == 1)
  48. % Enter new fsample
  49. resampleValue = inputdlg('Enter new sampling frequency', 'Sampling Frequency', 1, "250");
  50. resampleValue = str2double(resampleValue);
  52. if isempty(resampleValue) || any(isnan(resampleValue))
  53. fprintf('Enter valid numeric value. Shutting down\n');
  54. return
  55. end
  57. end
  59. if any(cleanselection == 2)
  60. % Prompt filter type
  61. filterTypeOptions = {'Low-pass', 'High-pass', 'Pass band', 'Notch'};
  62. [filterType, ~] = listdlg('ListString', filterTypeOptions, 'PromptString', {'Select the filter that you want to', 'apply to your EEG data:'}, 'SelectionMode', 'single');
  64. if isempty(filterType), fprintf('Operation canceled. Shutting down\n'); return, end
  65. doNotchFilter = false;
  67. while true
  68. % Enter filter freq value
  69. switch filterType
  70. case 1
  71. filterVal = inputdlg('Enter frequency cutoff (Hz)', 'Low-Pass Filter', 1)';
  72. filterVal = str2double(filterVal);
  73. filterValues.low = [];
  74. filterValues.high = filterVal;
  75. case 2
  76. filterVal = inputdlg('Enter frequency cutoff (Hz)', 'High-Pass Filter', 1)';
  77. filterVal = str2double(filterVal);
  78. filterValues.low = filterVal;
  79. filterValues.high = [];
  80. case 3
  81. filterVal = inputdlg({'Enter low frequency cutoff (Hz)', 'Enter high frequency cutoff (Hz)'}, 'Pass Band Filter', 1)';
  82. filterVal = str2double(filterVal);
  83. filterValues.low = filterVal(1);
  84. filterValues.high = filterVal(2);
  85. case 4
  86. filterVal = inputdlg({'Enter low frequency cutoff (Hz)', 'Enter high frequency cutoff (Hz)'}, 'Notch Filter', 1)';
  87. filterVal = str2double(filterVal);
  88. filterValues.low = filterVal(1);
  89. filterValues.high = filterVal(2);
  90. doNotchFilter = true;
  91. end
  93. % Check values
  94. if ~isnumeric(filterVal) || any(isnan(filterVal)) || isempty(filterVal) || any(filterVal < 0)
  95. fprintf('Enter valid values for filtering\n');
  96. else
  97. break
  98. end
  100. end
  102. end
  104. if any(cleanselection == 6)
  105. baselineThreshold = inputdlg({'Enter baseline correction start point in milliseconds (ms)', 'Enter baseline correction end point in milliseconds (ms)'}, 'Baseline Correction', 1, {'-200', '0'});
  106. baselineThreshold = str2double(baselineThreshold);
  108. if isempty(baselineThreshold) || any(isnan(baselineThreshold))
  109. fprintf('Enter valid numeric value. Shutting down\n');
  110. return
  111. end
  113. end
  115. if any(cleanselection == 7)
  116. icaTypeOptions = {'runica', 'SOBI'};
  117. [icaType, ~] = listdlg('ListString', icaTypeOptions, 'PromptString', {'Select the ICA algorithm that you', 'want to apply to your EEG data:'}, 'SelectionMode', 'single');
  118. if isempty(icaType), fprintf('Operation canceled. Shutting down\n'); return, end
  119. end
  121. if any(cleanselection == 9)
  122. amplitudeThreshold = inputdlg('Enter maximum voltage threshold for automatic voltage epoch rejection', 'Voltage Threshold', 1, "75");
  123. amplitudeThreshold = str2double(amplitudeThreshold);
  125. if isempty(amplitudeThreshold) || isnan(amplitudeThreshold)
  126. fprintf('Enter valid numeric value. Shutting down\n');
  127. return
  128. end
  130. end
  132. if any(cleanselection == 10)
  133. rejSpecSettings = cell(1);
  134. spectraIdx = 1;
  136. while true
  138. while true
  139. % Set spectrum settings
  140. spectraOptions = {'Enter power rejection threshold (dB)', ...
  141. 'Enter low frequency limit (Hz)', 'Enter high frequency limit (Hz)'};
  142. rejSpecValues = inputdlg(spectraOptions, 'Spectra Thresholds', 1, {'50', '0', '2'});
  143. rejSpecValues = str2double(rejSpecValues);
  145. % Check values
  146. if isempty(rejSpecValues) || any(isnan(rejSpecValues))
  147. fprintf('Enter valid values for epoching\n');
  148. else
  149. break
  150. end
  152. end
  154. % Save values to cell
  155. rejSpecSettings{spectraIdx} = rejSpecValues;
  156. spectraIdx = spectraIdx + 1;
  158. % Prompt to redo spectra with other options
  159. addSpectra = questdlg('Do you wish to add more spectra reject options?', 'Reject spectra', 'Yes', 'No', 'No');
  160. if strcmpi(addSpectra, 'no'), break, end
  161. end
  163. end
  165. % Select files to load
  166. [loadfiles, loadpath] = uigetfile({'*.vhdr;*.ahdr', 'Brain Vision files (*.vhdr, *.ahdr)'; '*.mat;*.set', 'MATLAB-EEGLAB files (*.mat, *.set)'}, 'Select files with raw EEG data to load', 'MultiSelect', 'on');
  167. if loadpath == 0, fprintf('Operation canceled. Shutting down\n'); return, end
  168. filelist = fullfile(loadpath, loadfiles);
  169. if ~iscell(filelist), filelist = {filelist}; end
  171. % Define savepath
  172. rawSavepath = uigetdir(pwd, 'Select path to save the data');
  173. if rawSavepath == 0, fprintf('Operation canceled. Shutting down\n'); return, end
  175. % make mat ft folder if selected
  176. if any(cleanselection == 13)
  178. % Set save path
  179. savepath_ft = fullfile(rawSavepath, 'ft_mat_files');
  181. % Check folder
  182. if ~exist(savepath_ft, 'dir'), mkdir(savepath_ft); end
  184. end
  186. % Prompt save format
  187. saveformat = questdlg('Do you want to save as .set (EEGLAB dataset), .mat (MATLAB data) file or both?', 'Choose format', 'set', 'mat', 'both', 'mat');
  189. % Check the user's response
  190. if strcmpi(saveformat, 'set')
  192. fprintf('Data will be saved as .set file.\n');
  194. % Set save path
  195. savepath_set = fullfile(rawSavepath, 'set_files');
  196. icaSaveFolderPath = fullfile(savepath_set, 'preIca');
  197. rejSaveFolderPath = fullfile(savepath_set, 'preRej');
  199. % Check folder
  200. if ~exist(savepath_set, 'dir'), mkdir(savepath_set); end
  201. if ~exist(icaSaveFolderPath, 'dir'), mkdir(icaSaveFolderPath); end
  202. if ~exist(rejSaveFolderPath, 'dir'), mkdir(rejSaveFolderPath); end
  204. % Update the savepath
  205. savepath = savepath_set;
  207. elseif strcmpi(saveformat, 'mat')
  209. fprintf('Data will be saved as .mat file.\n');
  211. % Set save path
  212. savepath_mat = fullfile(rawSavepath, 'mat_files');
  213. icaSaveFolderPath = fullfile(savepath_mat, 'preIca');
  214. rejSaveFolderPath = fullfile(savepath_mat, 'preRej');
  216. % Check folder
  217. if ~exist(savepath_mat, 'dir'), mkdir(savepath_mat); end
  218. if ~exist(icaSaveFolderPath, 'dir'), mkdir(icaSaveFolderPath); end
  219. if ~exist(rejSaveFolderPath, 'dir'), mkdir(rejSaveFolderPath); end
  221. % Update savepath
  222. savepath = savepath_mat;
  224. elseif strcmpi(saveformat, 'both')
  226. fprintf('Data will be saved as both .set and .mat file.\n');
  228. % Set save path
  229. savepath_mat = fullfile(rawSavepath, 'mat_files');
  230. savepath_set = fullfile(rawSavepath, 'set_files');
  232. % Update savepath
  233. savepath = struct();
  234. icaSaveFolderPath = struct();
  235. rejSaveFolderPath = struct();
  236. savepath.mat = savepath_mat;
  237. savepath.set = savepath_set;
  238. icaSaveFolderPath.mat = fullfile(savepath.mat, 'preIca');
  239. icaSaveFolderPath.set = fullfile(savepath.set, 'preIca');
  240. rejSaveFolderPath.mat = fullfile(savepath.mat, 'preRej');
  241. rejSaveFolderPath.set = fullfile(savepath.set, 'preRej');
  243. % Check folder
  244. if ~exist(savepath.mat, 'dir'), mkdir(savepath.mat); end
  245. if ~exist(icaSaveFolderPath.mat, 'dir'), mkdir(icaSaveFolderPath.mat); end
  246. if ~exist(rejSaveFolderPath.mat, 'dir'), mkdir(rejSaveFolderPath.mat); end
  248. if ~exist(savepath.set, 'dir'), mkdir(savepath.set); end
  249. if ~exist(icaSaveFolderPath.set, 'dir'), mkdir(icaSaveFolderPath.set); end
  250. if ~exist(rejSaveFolderPath.set, 'dir'), mkdir(rejSaveFolderPath.set); end
  252. end
  254. % set warning for missing chanlocs
  255. warnMissChan = true;
  257. % disable new eeglab version check to run faster
  258. eeglab('nogui');
  259. pop_editoptions('option_checkversion', false);
  261. %% Process data
  262. while true
  264. for i = 1:numel(filelist)
  266. try
  267. %% Load data
  268. % Get the file
  269. file = filelist{i};
  271. % Get filename to save it later
  272. [ogFolderpath, ogfilename, ogExtension] = fileparts(file);
  273. fileNameSave = ogfilename;
  275. % Run EEGlab in the background and call it again for each iteration
  276. % so it clears previous unnecesary data.
  277. eeglab;
  278. close all
  280. % Load EEG data from .vhdr or .ahdr file or other
  281. if strcmp(ogExtension, '.vhdr') || strcmp(ogExtension, '.ahdr')
  282. EEG = pop_loadbv(ogFolderpath, [ogfilename, ogExtension], [], []);
  283. else
  284. EEG = pop_loadset(file);
  285. end
  287. % Check chanlocs
  288. if warnMissChan
  289. isChanLocsEmpty = isempty([EEG.chanlocs.X]) || isempty([EEG.chanlocs.Y]) || isempty([EEG.chanlocs.Z]) || isempty([EEG.chanlocs.theta]);
  291. if isChanLocsEmpty
  293. doLoadCoords = questdlg('The coordinates for your channels/electrodes are missing. Do you wish to load a file with their coordinates?', 'Missing Coordinates', 'Yes', 'No', 'Yes');
  295. % get file and extension
  296. if strcmp(doLoadCoords, 'Yes')
  297. [chanlocsFile, chanlocsDir] = uigetfile('*.*', 'Select file containing EEG layout', 'MultiSelect', 'off');
  298. chanlocsPath = fullfile(chanlocsDir, chanlocsFile);
  299. [~, ~, chanlocsExt] = fileparts(chanlocsPath);
  300. end
  302. end
  304. warnMissChan = false;
  305. end
  307. % import chanlocs
  308. if exist('doLoadCoords', 'var')
  310. if strcmp(doLoadCoords, 'Yes')
  312. while true
  314. try
  315. fprintf('Loading channel coordinates...\n');
  316. EEG = eegImportChanlocs(EEG, chanlocsPath, chanlocsExt);
  317. fprintf('Channel coordinates loaded successfully.\n');
  318. break
  320. catch
  321. reChanlocsLoad = questdlg('Channels could not be loaded. Try again?', 'Channel Error', 'Yes', 'No', 'Yes');
  322. if ~strcmp(reChanlocsLoad, 'Yes'), fprintf('Proceeding without loading channel coordinates...\n'); break, end
  323. end
  325. end
  327. else
  328. dialToWait = warndlg('The data will be processed without channel locations. Please note that you will not be able to use functions that require channel coordinates, such as "Topoplot".', 'Channel Omission');
  329. uiwait(dialToWait);
  330. end
  332. end
  334. %% Clean EEG data using EEGLAB functions
  336. while true
  337. % Step 1 visualize raw data
  338. pop_eegplot(EEG, 1, 0, 0); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  339. uiwait(gcf);
  340. close all
  342. % Step 2 Remove channels before cleaning data
  343. if ~exist('doRemoveChans', 'var')
  344. doRemoveChans = questdlg('Do you wish to remove channels from your data?', 'Remove Channels', 'Yes', 'No', 'Yes');
  345. if strcmpi(doRemoveChans, 'yes'), doRemoveChans = true; else, doRemoveChans = false; end
  346. end
  348. if doRemoveChans
  350. if ~exist('selectChansToRemove', 'var')
  351. [selectChansToRemove, ~] = listdlg('ListString', {EEG.chanlocs.labels}, 'PromptString', 'Select channels to remove:', 'SelectionMode', 'multiple');
  353. % Find chanlabels from indices
  354. chansToRemove = {EEG.chanlocs(selectChansToRemove).labels};
  355. end
  357. % Use EEGLAB function to remove them
  358. EEG = pop_select(EEG, 'nochannel', selectChansToRemove);
  360. % Update EEG
  361. EEG = eeg_checkset(EEG);
  363. % Completion msg
  364. fprintf('Removed channel(s) {%s} from EEG data.\n', strjoin(chansToRemove, ', '));
  365. else
  366. fprintf('No channels removed from EEG data.\n');
  367. end
  369. % Step 3 Resample
  370. if any(cleanselection == 1)
  372. % Resample
  373. EEG = pop_resample(EEG, resampleValue);
  375. % Plot
  376. pop_eegplot(EEG, 1, 0, 0); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  377. uiwait(gcf);
  378. close all
  379. end
  381. % Step 4 filter data and visualize
  382. % Single
  383. if any(cleanselection == 2)
  385. EEG = pop_eegfiltnew(EEG, 'locutoff', filterValues.low, 'hicutoff', filterValues.high, 'revfilt', doNotchFilter);
  387. end
  389. % Multi
  390. if any(cleanselection == 3)
  392. while true
  394. try
  395. EEG = pop_eegfiltnew(EEG);
  396. pop_eegplot(EEG, 1, 0, 0); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  397. uiwait(gcf);
  398. close all
  399. catch
  400. warning('Input at least one value valid numeric value to filter the data.')
  401. end
  403. % Ask to filter again
  404. refilter = questdlg('Do you wish to filter again your data?', 'Filter data', 'Yes', 'No', 'No');
  405. if strcmpi(refilter, 'No'), break, end
  406. end
  408. end
  410. % Step 5.1 Split data in epochs for ERPs
  411. if any(cleanselection == 4)
  413. if ~exist('epochSettings', 'var')
  415. while true
  416. % Select stimulus
  417. selectStimuli = unique({EEG.event.type});
  418. [epochSettings.stimuli, ~] = listdlg('ListString', selectStimuli, 'PromptString', 'Select Stimuli for Epoch:', 'SelectionMode', 'multiple');
  420. % Select time window
  421. epochSettings.time = inputdlg({'Enter epoch start point in seconds (s)', 'Enter epoch end point in seconds (s)'}, 'Split in Epochs', 1)';
  422. epochSettings.time = str2double(epochSettings.time);
  424. % init suffix
  426. % Check values
  427. if ~isnumeric(epochSettings.time) || any(isnan(epochSettings.time)) || isempty(epochSettings.stimuli) || isempty(epochSettings.time)
  428. fprintf('Enter valid values for epoching\n');
  429. else
  430. break
  431. end
  433. end
  435. end
  437. % Epoch with eeglab
  438. EEG = pop_epoch(EEG, selectStimuli(epochSettings.stimuli), epochSettings.time);
  440. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  441. uiwait(gcf);
  442. close all
  444. end
  446. % Step 5.2 Split data in epochs for RS
  447. if any(cleanselection == 5)
  449. if ~exist('epochSettings', 'var')
  451. while true
  452. % Select stimulus
  453. selectStimuli = unique({EEG.event.type});
  454. [epochSettings.stimuli.start, ~] = listdlg('ListString', selectStimuli, 'PromptString', {'Select start mark for epoching:', ''}, 'SelectionMode', 'single');
  455. [epochSettings.stimuli.end, ~] = listdlg('ListString', selectStimuli, 'PromptString', {'Select end mark for epoching:', ''}, 'SelectionMode', 'single');
  457. % Select time window
  458. epochSettings.time = inputdlg('Enter epoch length in seconds (s)', 'RS Epoch length', 1)';
  459. epochSettings.time = str2double(epochSettings.time);
  461. % Check values
  462. if ~isnumeric(epochSettings.time) || isnan(epochSettings.time) || isempty(epochSettings.stimuli.start) || isempty(epochSettings.stimuli.end) || isempty(epochSettings.time)
  463. fprintf('Enter valid values for epoching\n');
  464. else
  465. break
  466. end
  468. end
  470. end
  472. % Find start and end for epochs in timepoints
  473. startEpochTmpt = EEG.event(find(strcmp({EEG.event.type}, selectStimuli(epochSettings.stimuli.start)))).latency;
  474. endEpochTmpt = EEG.event(find(strcmp({EEG.event.type}, selectStimuli(epochSettings.stimuli.end)))).latency;
  476. % First raw epoch
  477. EEG.data = EEG.data(:, startEpochTmpt:endEpochTmpt);
  479. % Get tmpts/trial
  480. epochTmpts = EEG.srate * epochSettings.time;
  481. nEpochs = floor(size(EEG.data, 2) / epochTmpts);
  483. % Second epoching
  484. EEG.data = reshape(EEG.data(:, 1:epochTmpts * nEpochs), size(EEG.data, 1), epochTmpts, nEpochs);
  486. % Fix EEG struct
  487. EEG.trials = size(EEG.data, 3);
  488. EEG.pnts = size(EEG.data, 2);
  489. EEG.xmax = epochSettings.time;
  490. EEG.times = EEG.times(1:epochTmpts);
  491. EEG.event = [];
  492. EEG.urevent = [];
  493. EEG.eventdescription = {};
  495. EEG = eeg_checkset(EEG);
  497. % Plot
  498. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  499. uiwait(gcf);
  500. close all
  502. end
  504. if ~exist('stimuliLabel', 'var')
  505. % Enter labels for epoching
  506. stimuliLabel = inputdlg('Enter label for file''s name', 'Epoch Labels');
  507. if isempty(stimuliLabel), stimuliLabel = ''; end
  508. end
  510. % Change filename for saving
  511. if isempty(stimuliLabel)
  512. fileNameSave = ogfilename;
  513. else
  514. fileNameSave = [ogfilename, '_', stimuliLabel{:}];
  515. end
  517. % Step 6 Correct baseline
  518. if any(cleanselection == 6)
  519. EEG = pop_rmbase(EEG, baselineThreshold');
  520. end
  522. % step 7(8) Remove bad channel
  523. if any(cleanselection == 8)
  525. chansToInterpolate = [];
  527. while true
  529. if ~exist('doRemoveBadChans', 'var')
  530. doRemoveBadChans = questdlg('Do you wish to interpolate any channels?', 'Interpolate Channels', 'Yes', 'No', 'Yes');
  531. if strcmpi(doRemoveBadChans, 'yes'), doRemoveBadChans = true; else, doRemoveBadChans = false; end
  532. end
  534. if ~doRemoveBadChans
  535. fprintf('No channels to interpolate.\n');
  536. break;
  537. end
  539. [selectBadChansToRemove, ~] = listdlg('ListString', {EEG.chanlocs.labels}, 'PromptString', 'Select channels to remove:', 'SelectionMode', 'multiple');
  541. % update chanm to interpo idx on each iteration
  542. chansToInterpolate = [chansToInterpolate, selectBadChansToRemove]; %#ok<AGROW>
  544. % Find chanlabels from indices
  545. chansToRemoveLabel = {EEG.chanlocs(selectBadChansToRemove).labels};
  547. % Update EEG
  548. EEG = eeg_checkset(EEG);
  550. % Completion msg
  551. fprintf('Channel(s) {%s} will be interpolated.\n', strjoin(chansToRemoveLabel, ', '));
  553. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  554. uiwait(gcf);
  555. close all
  557. % Ask to interpolate again
  558. interpochan = questdlg('Do you wish to interpolate more channels?', 'Interpolation', 'Yes', 'No', 'No');
  559. if strcmpi(interpochan, 'No'), break, end
  560. end
  562. end
  564. % Step 7(11) Re-reference before ICA following eeglab suggestion
  565. if any(cleanselection == 11)
  567. if ~exist('trialRef', 'var')
  569. while true
  570. trialRef = questdlg('How do you wish to re-reference the data?', 'Re-reference', 'Average', 'Channel', 'Average');
  572. if strcmpi(trialRef, 'channel')
  573. % Select channel for ref
  574. chanlabels = {EEG.chanlocs.labels};
  575. [rerefChan, ~] = listdlg('ListString', chanlabels, 'PromptString', 'Select channel for Re-reference:', 'SelectionMode', 'single');
  577. % Check input
  578. if isempty(rerefChan), fprintf('Select a valid channel.\n'), else, break, end
  579. end
  581. % Check input
  582. if isempty(trialRef), fprintf('Select an option.\n'), else, break, end
  583. end
  585. end
  587. % Re-ref func
  588. if strcmpi(trialRef, 'average')
  589. EEG = pop_reref(EEG, [], 'exclude', chansToInterpolate);
  590. fprintf('Computing average reference of the data.\n');
  591. elseif strcmpi(trialRef, 'channel')
  592. EEG = pop_reref(EEG, rerefChan, 'exclude', chansToInterpolate);
  593. fprintf('Referencing data to channel "%s".\n', EEG.chanlocs(rerefChan).labels);
  594. end
  596. end
  598. % Step 7 run ICA
  599. if any(cleanselection == 7)
  601. while true
  603. try
  605. switch icaType
  606. case 1
  607. EEG = pop_runica(EEG, 'icatype', 'runica', 'extended', 1, 'interrupt', 'off', 'chanind', setdiff(1:EEG.nbchan, chansToInterpolate));
  608. case 2
  609. EEG = pop_runica(EEG, 'icatype', 'sobi', 'chanind', setdiff(1:EEG.nbchan, chansToInterpolate));
  610. end
  612. break
  613. catch
  614. opts = struct('WindowStyle', 'non-modal', 'Interpreter', 'tex');
  615. errordlg('\color{red} \fontsize{13} ICA was interrupted. Running again', 'ICA Error', opts);
  616. end
  618. end
  620. % Review and label ICA components
  621. EEG = iclabel(EEG, 'default');
  622. pop_viewprops(EEG, 0);
  624. if ~isempty(findall(0, 'Type', 'figure'))
  625. uiwait(gcf);
  626. close all
  627. end
  629. % Save checkpoint
  630. EEG.comments = [];
  632. if strcmpi(saveformat, 'mat')
  633. save(fullfile(icaSaveFolderPath, fileNameSave), 'EEG');
  634. elseif strcmpi(saveformat, 'set')
  635. EEG = pop_saveset(EEG, char(fileNameSave), char(icaSaveFolderPath), 'savemode', 'onefile');
  636. elseif strcmpi(saveformat, 'both')
  637. save(fullfile(icaSaveFolderPath.mat, fileNameSave), 'EEG');
  638. EEG = pop_saveset(EEG, char(fileNameSave), char(icaSaveFolderPath.set), 'savemode', 'onefile');
  639. end
  641. % IC Artifact Rejection
  642. while true
  643. EEG = pop_selectcomps(EEG); % Manually inspect and select
  645. if ~isempty(findall(0, 'Type', 'figure'))
  646. uiwait(gcf);
  647. close all
  648. end
  650. EEG = pop_subcomp(EEG, [], 1, 0); % Remove components from data
  652. EEG = eeg_checkset(EEG);
  654. % Plot channel data
  655. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  656. uiwait(gcf);
  657. close all
  659. % Ask to reject componentes again
  660. reICA = questdlg('Do you wish to reject ICA components once more?', 'Reject ICA', 'Yes', 'No', 'No');
  661. if strcmpi(reICA, 'No'), break, end
  662. end
  664. end
  666. % Step 8 Interpolate bad channels if necessary
  667. if any(cleanselection == 8)
  669. EEG = pop_interp(EEG, chansToInterpolate);
  671. end
  673. % Step 9 Auto Reject abnormal voltage epochs
  674. if any(cleanselection == 9)
  676. % Save checkpoint
  677. EEG.comments = [];
  679. if strcmpi(saveformat, 'mat')
  680. save(fullfile(rejSaveFolderPath, fileNameSave), 'EEG');
  681. elseif strcmpi(saveformat, 'set')
  682. EEG = pop_saveset(EEG, char(fileNameSave), char(rejSaveFolderPath), 'savemode', 'onefile');
  683. elseif strcmpi(saveformat, 'both')
  684. save(fullfile(rejSaveFolderPath.mat, fileNameSave), 'EEG');
  685. EEG = pop_saveset(EEG, char(fileNameSave), char(rejSaveFolderPath.set), 'savemode', 'onefile');
  686. end
  688. % Mark trials to reject
  689. EEG = pop_eegthresh(EEG, 1, 1:length(EEG.chanlocs), -abs(amplitudeThreshold), abs(amplitudeThreshold), EEG.times(1) / 1000, EEG.times(end) / 1000, 1, 0);
  691. % Update thresholds
  692. if ~isempty(EEG.reject.rejmanual)
  693. EEG.reject.rejmanual = EEG.reject.rejmanual | EEG.reject.rejthresh;
  694. EEG.reject.rejmanualE = EEG.reject.rejmanualE | EEG.reject.rejthreshE;
  695. else
  696. EEG.reject.rejmanual = EEG.reject.rejthresh;
  697. EEG.reject.rejmanualE = EEG.reject.rejthreshE;
  698. end
  700. % Decide epoch rej
  701. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  702. uiwait(gcf);
  703. close all
  705. end
  707. % Step 10 Auto reject abnormal spectra
  708. if any(cleanselection == 10)
  710. % Save checkpoint
  711. if ~any(cleanselection == 8)
  712. EEG.comments = [];
  714. if strcmpi(saveformat, 'mat')
  715. save(fullfile(rejSaveFolderPath, fileNameSave), 'EEG');
  716. elseif strcmpi(saveformat, 'set')
  717. EEG = pop_saveset(EEG, char(fileNameSave), char(rejSaveFolderPath), 'savemode', 'onefile');
  718. elseif strcmpi(saveformat, 'both')
  719. save(fullfile(rejSaveFolderPath.mat, fileNameSave), 'EEG');
  720. EEG = pop_saveset(EEG, char(fileNameSave), char(rejSaveFolderPath.set), 'savemode', 'onefile');
  721. end
  723. end
  725. % Mark trials to reject
  726. for spectraIdx = 1:numel(rejSpecSettings)
  727. rejSpecSettingsFunc = rejSpecSettings{spectraIdx};
  728. EEG = pop_rejspec(EEG, 1, 'elecrange', 1:length(EEG.chanlocs), 'method', 'fft', ...
  729. 'threshold', [-abs(rejSpecSettingsFunc(1)) abs(rejSpecSettingsFunc(1))], ...
  730. 'freqlimits', [rejSpecSettingsFunc(2), rejSpecSettingsFunc(3)], ...
  731. 'eegplotreject', 0, 'eegplotplotallrej', 1);
  733. % Update thresholds
  734. if ~isempty(EEG.reject.rejmanual)
  735. EEG.reject.rejmanual = EEG.reject.rejmanual | EEG.reject.rejfreq;
  736. EEG.reject.rejmanualE = EEG.reject.rejmanualE | EEG.reject.rejfreqE;
  737. else
  738. EEG.reject.rejmanual = EEG.reject.rejfreq;
  739. EEG.reject.rejmanualE = EEG.reject.rejfreqE;
  740. end
  742. % Decide epoch rej
  743. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  744. uiwait(gcf);
  745. close all
  747. end
  749. end
  751. % Step 11 Re-reference again after ICA
  752. if any(cleanselection == 11)
  754. % Re-ref func
  755. if strcmpi(trialRef, 'average')
  756. EEG = pop_reref(EEG, []);
  757. fprintf('Computing average reference of the data.\n');
  758. elseif strcmpi(trialRef, 'channel')
  759. EEG = pop_reref(EEG, rerefChan);
  760. fprintf('Referencing data to channel "%s".\n', EEG.chanlocs(rerefChan).labels);
  761. end
  763. end
  765. % Step 12 final data inspection
  766. while true
  767. pop_eegplot(EEG, 1, 1, 1); % [1 channel data or 0 independent components], [1 for channel interpolation, 0 to skip interpolation], [1 to allow manual rejection]
  768. uiwait(gcf);
  769. close all
  771. % Ask to inspect again
  772. re_rej = questdlg('Do you wish to inspect the data once again?', 'Data Inspection', 'Yes', 'No', 'No');
  773. if strcmpi(re_rej, 'No'), break, end
  774. end
  776. % Step 13 Plot ERPs
  777. if any(cleanselection == 12)
  778. topoTitle = sprintf('ERPs for %s', fileNameSave);
  779. pop_plottopo(EEG, 1:length(EEG.chanlocs), char(topoTitle), 0);
  780. uiwait(gcf);
  781. close all
  782. end
  784. % Ask to redo all preproc
  785. redoclean = questdlg('Do you wish to redo data processing or continue?', 'Finish cleaning', 'Redo', 'Finish', 'Finish');
  787. if ~strcmpi(redoclean, 'redo')
  788. fprintf('Proceeding to save data ... \n');
  789. break
  790. else
  791. fprintf ('Deleting current dataset and importing raw data\n');
  793. % Run eeglab and reset vars
  794. EEG = [];
  795. ALLEEG = [];
  796. ALLCOM = {};
  797. eeglab;
  798. close all
  800. % Load EEG data from .vhdr or .ahdr file or other
  801. if strcmp(ogExtension, '.vhdr') || strcmp(ogExtension, '.ahdr')
  802. EEG = pop_loadbv(ogFolderpath, [ogfilename, ogExtension], [], []);
  803. else
  804. EEG = pop_loadset(file);
  805. end
  807. end
  809. end
  811. % Step 14 Save data and remove comments
  812. EEG.comments = [];
  814. if strcmpi(saveformat, 'mat')
  815. % Save "EEG" var
  816. save(fullfile(savepath, fileNameSave), 'EEG');
  818. elseif strcmpi(saveformat, 'set')
  819. % Save dataset
  820. EEG = pop_saveset(EEG, char(fileNameSave), char(savepath), 'savemode', 'onefile');
  822. elseif strcmpi(saveformat, 'both')
  823. % Save "EEG" var
  824. save(fullfile(savepath.mat, fileNameSave), 'EEG');
  826. % Save dataset
  827. EEG = pop_saveset(EEG, char(fileNameSave), char(savepath.set), 'savemode', 'onefile');
  828. end
  830. % transform and save to FT
  831. if any(cleanselection == 13)
  833. fprintf('Exporting to FieldTrip...\n');
  834. data = eeglab2fieldtrip(EEG, 'raw', 'none');
  835. save(fullfile(savepath_ft, fileNameSave), 'data');
  837. end
  839. % transform and save to LORETA
  840. if any(cleanselection == 14)
  842. fprintf('Exporting to LORETA...\n');
  843. loretaDir = fullfile(rawSavepath, fileNameSave);
  844. if ~isfolder(loretaDir), mkdir(loretaDir); end
  846. for epIdx = 1:size(EEG.data, 3)
  848. writematrix(EEG.data(:, :, epIdx)', sprintf("%s\\%s_%d.asc", loretaDir, fileNameSave, epIdx), "FileType", "text", "Delimiter", "\t"); % loreta needs text tab delimited
  850. end
  852. end
  854. if any(cleanselection == 15)
  856. fprintf('Exporting to BrainVision...\n');
  857. bvDir = fullfile(rawSavepath, 'BrainVision');
  858. if ~isfolder(bvDir), mkdir(bvDir); end
  860. EEG = pop_writebva(EEG, fullfile(bvDir, fileNameSave), 'DataOrientation', 'MULTIPLEXED');
  862. end
  864. % Display completion
  865. fprintf('\n-----Subject %s finished-----\n\n', fileNameSave);
  867. clear doRemoveBadChans
  869. catch subject_loop_error
  870. % Display error message
  871. warning('Error found in %s.\n%s (line %d): \n %s\n\nSkipping to next subject...', ogfilename, subject_loop_error.stack(end).name, subject_loop_error.stack(end).line, subject_loop_error.message);
  872. % Skip to next subject
  873. continue
  874. end
  876. end
  878. % Display completion
  879. fprintf('\n-------Successfully completed %d files-------\n', numel(filelist));
  881. %% Ask to run script on a different condition
  882. askConditionRerun = questdlg('Do you wish to clean a different condition?', 'Clean new condition', 'Yes', 'No', 'No');
  884. if strcmpi(askConditionRerun, 'yes')
  885. fprintf ('Preparing to run on new condition...\n');
  887. clear epochSettings stimuliLabel
  888. else
  889. break
  890. end
  892. end
  894. % re-enable new eeglab version check
  895. pop_editoptions('option_checkversion', true);
  897. % Display completion
  898. fprintf('\n\t\t /\\_/\\ \t /\\_/\\ \n\t\t ( o.o )\t ( ^.^ )\n\t\t > ^ <\t\t > ^ <\n');

eegPreproc.m at commit 9697559, under MIT · at the source

Overview

Authors: Dino Soldic1,2, María Carmen Martín‐Buro1,2, David López‐García3, Ana Belén del Pino1,2, Roberto Fernandes‐Magalhaes1,2, David Ferrera1,2, Irene Peláez1,2, Luis Carretié4, Francisco Mercado1,2
ORCID iDs: Dino Soldic, María Carmen Martín‐Buro, David López‐García, Roberto Fernandes‐Magalhaes, Irene Peláez
  1. Department of Psychology, Faculty of Health Sciences, Rey Juan Carlos University, Madrid, Spain
  2. Research group in Cognitive Neuroscience, Pain and Rehabilitation (NECODOR), Rey Juan Carlos University, Madrid, Spain
  3. Mind, Brain and Behavior Research Center, University of Granada, Granada, Andalucía, Spain
  4. Faculty of Psychology, Autonomous University of Madrid, Madrid, Spain
Institutions: Universidad Rey Juan Carlos (Spain); Universidad de Granada (Spain); Centro de Investigación Mente, Cerebro y Comportamiento (Spain); Universidad Autónoma de Madrid (Spain)
Journal: The European journal of neuroscience, volume 63, issue 9, article e70521
Dates: received 22 January 2026; accepted 16 April 2026; published online 27 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/ejn.70521 · PMID 42045133 · PMCID PMC13121099 · OpenAlex W7156076361
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), pain (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: chronic pain, decoding, discriminant analysis, multivariate analysis, neural markers, support vector machine
MeSH: Brain*, Electroencephalography*, Fibromyalgia*, Adult, Aged, Anxiety, Biomarkers, Female, Humans, Middle Aged, Multivariate Analysis, Rest (* major topic)
Topic: Fibromyalgia and Chronic Fatigue Syndrome Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Ministerio de Ciencia e Innovación of Spain (PID2020-115463RB-I00)
Citations: cited by 2 papers (Europe PMC); 78 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

dinosoldic/eeg-preproc-erp

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9697559bb6e9537941528cb45e3a96d183d0aded, 28 May 2026
Languages: MATLAB (7)
Size: 10 files, 7 scripts
Software Heritage: not archived
Found in: the text, “EEG Data Acquisition and Preprocessing”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: EEGLAB (4 files), ICLabel (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/dinosoldic/EEG-Preproc-ERP/blob/main/eegPreproc.m

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1111/ejn.70521.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 12 MeSH terms, 1 funder, 76 references.

Cite

This paper

Soldic, D., Martín‐Buro, M. C., López‐García, D., del Pino, A. B., Fernandes‐Magalhaes, R., Ferrera, D., Peláez, I., Carretié, L., & Mercado, F. (2026). Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia. The European journal of neuroscience, 63(9), e70521. https://doi.org/10.1111/ejn.70521

BibTeX

@article{soldic2026multivariate,
author = {Soldic, Dino and Martín‐Buro, María Carmen and López‐García, David and del Pino, Ana Belén and Fernandes‐Magalhaes, Roberto and Ferrera, David and Peláez, Irene and Carretié, Luis and Mercado, Francisco},
title = {{Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia}},
journal = {The European journal of neuroscience},
year = {2026},
month = may,
volume = {63},
number = {9},
pages = {e70521},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70521},
url = {https://doi.org/10.1111/ejn.70521},
pmid = {42045133},
pmcid = {PMC13121099}
}

RIS

TY - JOUR
AU - Soldic, Dino
AU - Martín‐Buro, María Carmen
AU - López‐García, David
AU - del Pino, Ana Belén
AU - Fernandes‐Magalhaes, Roberto
AU - Ferrera, David
AU - Peláez, Irene
AU - Carretié, Luis
AU - Mercado, Francisco
TI - Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/05/01
VL - 63
IS - 9
SP - e70521
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70521
UR - https://doi.org/10.1111/ejn.70521
LA - en
ER -

CSL-JSON

{
"id": "10.1111/ejn.70521",
"type": "article-journal",
"title": "Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia",
"container-title": "The European journal of neuroscience",
"author": [
{
"family": "Soldic",
"given": "Dino"
},
{
"family": "Martín‐Buro",
"given": "María Carmen"
},
{
"family": "López‐García",
"given": "David"
},
{
"family": "del Pino",
"given": "Ana Belén"
},
{
"family": "Fernandes‐Magalhaes",
"given": "Roberto"
},
{
"family": "Ferrera",
"given": "David"
},
{
"family": "Peláez",
"given": "Irene"
},
{
"family": "Carretié",
"given": "Luis"
},
{
"family": "Mercado",
"given": "Francisco"
}
],
"container-title-short": "Eur J Neurosci",
"volume": "63",
"issue": "9",
"page": "e70521",
"DOI": "10.1111/ejn.70521",
"PMID": "42045133",
"PMCID": "PMC13121099",
"ISSN": "0953-816X",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/ejn.70521",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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