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Neural Mechanisms of Self-Generated Action Sequences.

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The 2 matches
  1. [1] § Materials and Methods › EEG analyses › Preprocessing ↔ 01Scripts.zip/01Scripts/Preprocessing.m, lines 304–350 · score 0.81 · horizontal eye movement, Independent component, runica, EEGLAB, preprocessing, electrodes
  2. [2] § Materials and Methods › Behavioral data analysis ↔ 01Scripts.zip/01Scripts/ToL_EEG_BEH_analyses.R, lines 150–230 · score 0.63 · Post hoc, MovementType, transformed, sequence length, stimulus driven, ID

Paper

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

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The authors' code

MATLAB · 718 lines · 29 KB · no license · 1 match

  1. %% EEGlab preprocessing sample script
  2. % UCL, ICN - London
  3. clc
  4. clear
  6. %% Set experimental analysis parameters
  7. % If you are working with your own data, please change this code to match
  8. % your datafiles.
  10. exp.name = 'S3'
  12. % Change to your computer's directories
  13. exp.behpath = ['/Volumes/T7/02UCL/07EEGVolition&ProblemSolving/'];
  14. exp.database = ['/Volumes/T7/02UCL/07EEGVolition&ProblemSolving/00EEG/01ConvertedData']
  15. exp.filepath = ['/Volumes/T7/02UCL/07EEGVolition&ProblemSolving/00EEG/02Preprocessing'];
  17. exp.sub_id = [1];
  18. exp.nsub = length(exp.sub_id);
  20. exp.sr = 200; % sampling rate after downsampling
  22. exp.Chans = [1:3,7,17];
  23. exp.ChanLabels = {'Fz,FCz,Cz,FC1,FC2'};
  25. exp.filter.lowerbound = 0; % high pass
  26. exp.filter.upperbound = 30; % low pass
  27. exp.downsampling_rate = 200; % downsampling rate
  29. exp.Epoch = [-1 0.5]; %s
  30. exp.Baseline = [-5 5]; %ms
  32. exp.artefact = 120
  34. % Indices of channels to include in the ICA.
  35. % Include only one of the VEOG (27), and one of the HEOG (29).
  36. exp.icachanind = [1:27,29]
  38. %% Pre-ICA pipeline, preprocessing
  40. for sub = exp.sub_id(1:end)
  41. % Load file
  42. EEG = pop_loadset([exp.database '/' 'P' num2str(sub) '.set']);
  43. EEG.setname=['P' num2str(sub)];
  44. EEG = pop_chanedit(EEG, 'lookup','/Applications/eeglab2021.0/functions/supportfiles/Standard-10-5-Cap385_witheog.elp');
  45. EEG = eeg_checkset( EEG );
  47. clear_boundary=true;
  50. % Clean triggers
  51. % This is usually not a required step - but we have been having issues
  52. % with the NIDaq box, resulting in extra, non-task related triggers
  53. % being recorded. This function cleans them.
  54. %EEG = O1_cleanTriggers(sub,exp); % remove extra triggers
  55. %O1_checkTriggerNumbers(sub,exp)
  57. % Clean triggers
  58. all_latencies= [EEG.event.latency]';
  59. for index= 1: size(all_latencies,1)
  60. if index < size(all_latencies,1)&& index >1
  61. if all_latencies(index)~= all_latencies(index+1) && all_latencies(index)~= all_latencies(index-1)
  62. wrong_trigger_filter(index)= 1;
  63. else
  64. wrong_trigger_filter(index)= 0;
  65. wrong_trigger_filter(index+1)= 0;
  66. end
  67. elseif index < size(all_latencies,1)&& index ==1
  68. if all_latencies(index)~= all_latencies(index+1)
  69. wrong_trigger_filter(index)= 1;
  70. else
  71. wrong_trigger_filter(index)= 0;
  72. wrong_trigger_filter(index+1)= 0;
  73. end
  74. else
  76. if all_latencies(index)~= all_latencies(index-1)
  77. wrong_trigger_filter(index)= 1;
  78. else
  79. wrong_trigger_filter(index)= 0;
  80. end
  81. end
  82. end
  84. all_latencies(:,2)= wrong_trigger_filter;
  86. EEG.event = EEG.event(wrong_trigger_filter==1);
  88. % Add metadata
  89. clear BEHAVIORAL;
  90. clear b1;
  91. clear b2;
  92. clear b3;
  93. clear b4;
  94. clear b5;
  95. clear b6;
  96. clear p1;
  97. clear p2;
  98. clear p3;
  99. clear p4;
  100. clear p5;
  101. clear p6;
  103. p1= ['00Behavioural/P' num2str(sub),'/ToL_fMRI_LOG_P', num2str(sub),'_Random',num2str(sub), 'Run1.xls'];
  104. p2= ['00Behavioural/P' num2str(sub),'/ToL_fMRI_LOG_P', num2str(sub),'_Random',num2str(sub), 'Run2.xls'];
  105. p3= ['00Behavioural/P' num2str(sub),'/ToL_fMRI_LOG_P', num2str(sub),'_Random',num2str(sub), 'Run3.xls'];
  106. p4= ['00Behavioural/P' num2str(sub),'/ToL_fMRI_LOG_P', num2str(sub),'_Random',num2str(sub), 'Run4.xls'];
  107. p5= ['00Behavioural/P' num2str(sub),'/ToL_fMRI_LOG_P', num2str(sub),'_Random',num2str(sub), 'Run5.xls'];
  108. p6= ['00Behavioural/P' num2str(sub),'/ToL_fMRI_LOG_P', num2str(sub),'_Random',num2str(sub), 'Run6.xls'];
  110. %initialize BEHAVIORAL file
  111. [~,~,b1] = xlsread(p1);
  112. behnames= b1(1,:);
  113. b1= b1(2:size(b1,1), :); %eliminate first row
  114. b1= b1(1:(size(b1,1)-1), :); %eliminate last row
  115. for IND= 1:size(b1,1) %look for jitters
  116. JF(IND)= (strcmp(b1{IND,11}, 'Choice_Jitter')|strcmp(b1{IND,11}, 'Release_Jitter'))==false;
  117. end
  118. b1=b1(JF,:);
  119. BEHAVIORAL= b1;
  120. clear JF;
  122. if exist(p2)==2
  123. [~,~,b2] = xlsread(p2);
  124. b2= b2(2:size(b2,1), :); %eliminate first row
  125. b2= b2(1:(size(b2,1)-1), :); %eliminate last row
  126. for IND= 1:size(b2,1) %look for jitters
  127. JF(IND)= (strcmp(b2{IND,11}, 'Choice_Jitter')|strcmp(b2{IND,11}, 'Release_Jitter'))==false;
  128. end
  129. b2=b2(JF,:);
  130. BEHAVIORAL= [BEHAVIORAL; b2];
  131. end
  132. clear JF;
  134. if exist(p3)==2
  135. [~,~,b3] = xlsread(p3);
  136. b3= b3(2:size(b3,1), :); %eliminate first row
  137. b3= b3(1:(size(b3,1)-1), :); %eliminate last row
  138. for IND= 1:size(b3,1) %look for jitters
  139. JF(IND)= (strcmp(b3{IND,11}, 'Choice_Jitter')|strcmp(b3{IND,11}, 'Release_Jitter'))==false;
  140. end
  141. b3=b3(JF,:);
  142. BEHAVIORAL= [BEHAVIORAL; b3];
  143. end
  144. clear JF;
  146. if exist(p4)==2
  147. [~,~,b4] = xlsread(p4);
  148. b4= b4(2:size(b4,1), :); %eliminate first row
  149. b4= b4(1:(size(b4,1)-1), :); %eliminate last row
  150. for IND= 1:size(b4,1) %look for jitters
  151. JF(IND)= (strcmp(b4{IND,11}, 'Choice_Jitter')|strcmp(b4{IND,11}, 'Release_Jitter'))==false;
  152. end
  153. b4=b4(JF,:);
  154. BEHAVIORAL= [BEHAVIORAL; b4];
  155. end
  156. clear JF;
  158. if exist(p5)==2
  159. [~,~,b5] = xlsread(p5);
  160. b5= b5(2:size(b5,1), :); %eliminate first row
  161. b5= b5(1:(size(b5,1)-1), :); %eliminate last row
  162. for IND= 1:size(b5,1) %look for jitters
  163. JF(IND)= (strcmp(b5{IND,11}, 'Choice_Jitter')|strcmp(b5{IND,11}, 'Release_Jitter'))==false;
  164. end
  165. b5=b5(JF,:);
  166. BEHAVIORAL= [BEHAVIORAL; b5];
  167. end
  168. clear JF;
  170. if exist(p6)==2
  171. [~,~,b6] = xlsread(p6);
  172. b6= b6(2:size(b6,1), :); %eliminate first row
  173. b6= b6(1:(size(b6,1)-1), :); %eliminate last row
  174. for IND= 1:size(b6,1) %look for jitters
  175. JF(IND)= (strcmp(b6{IND,11}, 'Choice_Jitter')|strcmp(b6{IND,11}, 'Release_Jitter'))==false;
  176. end
  177. b6=b6(JF,:);
  178. BEHAVIORAL= [BEHAVIORAL; b6];
  179. end
  182. %get rid of trigger 7
  183. for index6 =1:size(EEG.event,2)
  184. Filt(index6)= strcmp(EEG.event(index6).type, 'Trigger 7');
  185. end
  186. EEG.event=EEG.event(Filt==0);
  187. clear index6
  188. clear Filt
  190. %get rid of boundary
  191. if clear_boundary==true
  192. for index6 =1:size(EEG.event,2)
  193. Filt(index6)= strcmp(EEG.event(index6).type, 'boundary');
  194. end
  195. EEG.event=EEG.event(Filt==0);
  197. end
  200. counter=1; %use this to index the BEHAVIORAL file
  201. for index7 =1:size(EEG.event,2)
  205. if strcmp(EEG.event(index7).type, 'Trigger 7')
  206. else
  209. EEG.event(index7).ID= num2str(BEHAVIORAL{counter,1});
  210. EEG.event(index7).Random= num2str(BEHAVIORAL{counter,2});
  211. EEG.event(index7).Run= num2str(BEHAVIORAL{counter,3});
  212. EEG.event(index7).Active_Passive= num2str(BEHAVIORAL{counter,4});
  213. EEG.event(index7).Problem_code= num2str(BEHAVIORAL{counter,5});
  214. EEG.event(index7).min_number_of_moves= num2str(BEHAVIORAL{counter,6});
  215. EEG.event(index7).Factor1= num2str(BEHAVIORAL{counter,7});
  216. EEG.event(index7).Factor2= num2str(BEHAVIORAL{counter,8});
  217. EEG.event(index7).Factor3= num2str(BEHAVIORAL{counter,9});
  218. EEG.event(index7).Counter= num2str(BEHAVIORAL{counter,10});
  219. EEG.event(index7).EventName= num2str(BEHAVIORAL{counter,11});
  220. EEG.event(index7).Onset= num2str(BEHAVIORAL{counter,12});
  221. EEG.event(index7).Duration= num2str(BEHAVIORAL{counter,13});
  222. EEG.event(index7).Wrong_Choice= num2str(BEHAVIORAL{counter,14});
  223. EEG.event(index7).Wrong_Release= num2str(BEHAVIORAL{counter,15});
  225. counter= counter+1;
  226. end
  227. end
  228. end
  230. %%
  232. for sub = exp.sub_id(1:end)
  233. %% Re-reference to average of mastoids
  234. % There are many ways to re-reference EEG data. T
  235. EEG = pop_reref( EEG, [27 28] ); % re-reference to average of mastoids *channels
  236. EEG = eeg_checkset( EEG );
  237. filename = ['r' exp.name '_P' num2str(sub)];
  238. EEG = pop_saveset( EEG, filename, exp.filepath);
  240. % Identify bad channels based on kurtosis
  241. % Note - there are other methods.
  242. % Take this only as a hint - always check
  243. % visually!
  244. [iEEG,indelec] = pop_rejchan(EEG,'elec',[1:30],'threshold',5,'norm','on','measure','kurt');
  245. EEG.badChan = indelec;
  247. % Interpolate bad channels
  248. % EEG = eeg_interp(EEG,indelec);
  250. %% Filter
  251. % This is an important step. The parameters chosen vary depending on
  252. % which kind of analyses need to be run. In our case, we will use a
  253. % relatively low high-pass filter because we may want to look at the
  254. % RP, and a low low-pass filter as well, because we are not interested
  255. % in high-frequency EEG activity. To decide which parameters to use,
  256. % check previous papers analysing your signal of interest and follow
  257. % their guidelines.
  259. EEG = pop_basicfilter(EEG, 1:30 , 'Boundary', 'boundary', 'Cutoff', [exp.filter.lowerbound exp.filter.upperbound], 'Design', 'butter', 'Filter', 'bandpass', 'order',8);
  260. EEG = eeg_checkset( EEG );
  261. filename = ['fr' exp.name '_P' num2str(sub)];
  262. EEG = pop_saveset( EEG, filename, exp.filepath);
  264. % Let's see what the filtered data look like...
  265. EEG1 = pop_loadset([exp.filepath '/r' exp.name '_P' num2str(sub) '.set']);
  266. EEG2 = eeg_checkset( EEG );
  267. eegplot([EEG1.data], 'data2', [EEG2.data], 'color','off')
  269. %% Downsample
  270. % This is not strictly necessary, especially if the recording sampling
  271. % rate is already low as in our case (256Hz). I include it for
  272. % completion purposes.
  274. EEG = eeg_checkset( EEG );
  275. EEG = pop_resample( EEG, exp.downsampling_rate);
  276. EEG = eeg_checkset( EEG );
  277. filename = ['df8r' exp.name '_P' num2str(sub)];
  278. EEG = pop_saveset( EEG, filename, exp.filepath);
  280. %% Epoch around events of interest (e.g. Trigger 2)
  281. % In your experiment, you may have different types of events that you want to look at,
  282. % requiring different epoching parameters. In that case, you would need
  283. % to specify the various Triggers of interest and their respective
  284. % epoching times.
  286. EEG = pop_loadset([exp.filepath '/' 'df8r' exp.name '_P' num2str(sub) '.set']);
  287. EEG = pop_epoch( EEG, {'Trigger 2'}, exp.Epoch, 'epochinfo', 'yes');
  288. EEG = eeg_checkset( EEG );
  289. EEG = pop_saveset( EEG, ['e_df8r' exp.name '_P' num2str(sub)], exp.filepath);
  291. %% Baseline correct
  292. % Baselining takes the baseline epoch and moves the whole epoch "up" or
  293. % "down" so that the average baseline signal is 0. This allows
  294. % comparing multiple epochs.
  295. EEG = pop_loadset([exp.filepath '/' '/e_df8r' exp.name '_P' num2str(sub) '.set']);
  296. EEG = pop_rmbase( EEG, exp.Baseline);
  297. EEG = pop_saveset( EEG, ['be_df8r' exp.name '_P' num2str(sub)], exp.filepath);
  299. %% Let's see what the baselined data look like...
  300. EEG1 = pop_loadset([exp.filepath '/' '/e_df8r' exp.name '_P' num2str(sub) '.set']);
  301. EEG2 = eeg_checkset( EEG );
  302. eegplot([EEG1.data], 'data2', [EEG2.data], 'color','off')
  304. %% Run ICA & plot component maps
  305. % We use the Independent Component Analysis to identify eye movements
  306. % and remove them from the signal.
  308. % ICA can also be used to identify physiological / cognitive components
  309. % of interest, and use them for analysis instead of using raw channel
  310. % data. We will not do this here, but it is an approach worth
  311. % investigating.
  313. EEG = eeg_checkset( EEG ); % set right channel names before ICA
  314. EEG = pop_runica(EEG, 'extended',0,'interupt','on', 'chanind', exp.icachanind);
  315. EEG = pop_chanedit(EEG, 'lookup','/Applications/eeglab2021.0/functions/supportfiles/Standard-10-5-Cap385_witheog.elp');
  316. EEG = eeg_checkset( EEG );
  317. EEG = pop_saveset( EEG, ['ICAbe_df8r' exp.name '_P' num2str(sub)], exp.filepath);
  320. % Plot ICA result
  321. pop_topoplot(EEG,0, [1:28] ,'O1_P0 resampled epochs',[5 6] ,0,'electrodes','on');
  322. % % If you want to use the EEGlab GUI:
  323. % % Start the GUI by typing eeglab
  324. % % Plot -> component maps -> 2D
  325. % % Tools -> remove components from data -> type component to remove ... -> plot single trials ->
  326. % % acccept -> save
  328. % In this example dataset, components 1 and 2 are clearly eye movement
  329. % components. Component 1 corresponds to eye blinks, and component 4 to
  330. % horizontal eye movements. Let's see what happens when we remove them.
  331. % Go to the GUI and run
  332. % Tools -> remove components from data -> type "1 2" in component to remove ... -> plot single trials ->
  334. % In this dataset, Channel 27 corresponds to VEOG, and 28 to HEOG. You
  335. % can look this up in the EEG.chanlocs field.
  336. % Red lines are after correction, blue lines before correction.
  338. % Notice how in epochs containing eyeblinks (e.g. epoch 6) channel 27 (VEOG) is completely flat after
  339. % correction. Notice how EEG channels with eyeblink artefacts are also
  340. % fully corrected.
  342. % Similarly, in epochs containin horizontal eye movements (e.g. epoch
  343. % 62) channel 28 (HEOG) is completely flat after correction. EEG
  344. % channels should be corrected too.
  346. % Once you've checked that the components you identified as eye
  347. % movements are correct, you can accept removing them and save the new
  348. % dataset.
  350. end
  352. %% Post-ICA correction pipeline
  353. for sub = exp.sub_id(1:end)
  354. %% Artefact rejection
  355. % This is one additional artefact rejection step to ensure that no
  356. % epochs contain excessive fluctuations. We use a thresholding
  357. % procedure, and reject any epochs with voltages spanning +/- 120uV
  358. % from baseline. This value is reasonable, but arbitrary - many papers
  359. % use a 80uV threshold, but given that we use a very low high pass
  360. % filter, our epochs contain a greater degree of drift. Thus, we allow
  361. % for wider fluctuations.
  363. % Note: there are many other approaches to artefact rejection - you may
  364. % wish to pick your own.
  365. EEG = pop_loadset([exp.filepath '/' 'cICAbe_df8r' exp.name '_P' num2str(sub) '.set']);
  366. [EEG, iart] = pop_eegthresh(EEG,1,exp.Chans ,-120,120, -2.5,0.995,0,1);
  367. EEG.iart = iart; % We need to save the artefact indices so we can remove them from the behavioural data too.
  368. EEG = pop_saveset( EEG, ['acICAbe_df8r' exp.name '_P' num2str(sub)], exp.filepath);
  369. end
  371. %% Plot
  372. for sub = exp.sub_id(1:end)i = 0
  373. figure
  375. for sub = exp.sub_id(1:end)
  376. EEG = pop_loadset(['/Volumes/T7/02UCL/07EEGVolition&ProblemSolving/00EEG/02Preprocessing/acICAbe_df8rS3_P' num2str(sub) '.set']);
  378. i = i+1
  379. %subplot(7,4,i)
  380. plot(mean(EEG.data(exp.Chans,:,:),3)') % Plot channel of interest
  381. xline(201)
  382. end
  384. end
  387. %% Extract figure data
  389. for sub = exp.sub_id(1:end)
  390. EEG = pop_loadset(['/Volumes/S/02UCL/07EEGVolition&ProblemSolving/00EEG/02Preprocessing/acICAbe_df8rS3_P' num2str(sub) '.set']);
  393. %check how many epochs contain more than one event of interest
  394. for indx= 1: size(EEG.epoch,2)
  395. EOI(indx)=sum(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2'));
  396. EOI_f(indx)=sum(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2'))==1;
  397. end
  398. clear indx
  400. %keep only channels we need
  401. eeg=EEG.data(exp.Chans,:,:);
  404. %initialize filters
  405. active_2_1_f=zeros(1,size(EEG.epoch,2));
  406. active_2_other_f=zeros(1,size(EEG.epoch,2));
  407. active_4_1_f=zeros(1,size(EEG.epoch,2));
  408. active_4_other_f=zeros(1,size(EEG.epoch,2));
  409. passive_2_1_f=zeros(1,size(EEG.epoch,2));
  410. passive_2_other_f=zeros(1,size(EEG.epoch,2));
  411. passive_4_1_f=zeros(1,size(EEG.epoch,2));
  412. passive_4_other_f=zeros(1,size(EEG.epoch,2));
  414. %we need four datasets: (1) active; (2) passive; (3) 1 active; (4) no1 active
  415. for indx= 1: size(EEG.epoch,2)
  416. if EOI(indx)==1 && EOI_f(indx)==1 && strcmp(EEG.epoch(indx).eventActive_Passive(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), 'active'); %create active
  417. if strcmp(EEG.epoch(indx).eventCounter(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '1')
  418. if strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '2')
  419. active_2_1_f(indx)=1;
  420. elseif strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '4')
  421. active_4_1_f(indx)=1;
  422. end
  423. else
  424. if strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '2')
  425. active_2_other_f(indx)=1;
  426. elseif strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '4')
  427. active_4_other_f(indx)=1;
  428. end
  429. end
  432. elseif EOI(indx)==1 && EOI_f(indx)==1 && strcmp(EEG.epoch(indx).eventActive_Passive(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), 'passive'); %create passive
  433. if strcmp(EEG.epoch(indx).eventCounter(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '1')
  434. if strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '2')
  435. passive_2_1_f(indx)=1;
  436. elseif strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '4')
  437. passive_4_1_f(indx)=1;
  438. end
  439. else
  440. if strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '2')
  441. passive_2_other_f(indx)=1;
  442. elseif strcmp(EEG.epoch(indx).eventmin_number_of_moves(strcmp(EEG.epoch(indx).eventtype, 'Trigger 2')), '4')
  443. passive_4_other_f(indx)=1;
  444. end
  445. end
  449. end
  450. end
  453. ACTIVE_2_1=eeg(:,:,active_2_1_f==1);
  454. ACTIVE_2_1=mean(ACTIVE_2_1,1);
  455. ACTIVE_2_1=mean(ACTIVE_2_1,3);
  456. ACTIVE_2_1=squeeze(ACTIVE_2_1);
  458. ACTIVE_4_1=eeg(:,:,active_4_1_f==1);
  459. ACTIVE_4_1=mean(ACTIVE_4_1,1);
  460. ACTIVE_4_1=mean(ACTIVE_4_1,3);
  461. ACTIVE_4_1=squeeze(ACTIVE_4_1);
  463. ACTIVE_2_other=eeg(:,:,active_2_other_f==1);
  464. ACTIVE_2_other=mean(ACTIVE_2_other,1);
  465. ACTIVE_2_other=mean(ACTIVE_2_other,3);
  466. ACTIVE_2_other=squeeze(ACTIVE_2_other);
  468. ACTIVE_4_other=eeg(:,:,active_4_other_f==1);
  469. ACTIVE_4_other=mean(ACTIVE_4_other,1);
  470. ACTIVE_4_other=mean(ACTIVE_4_other,3);
  471. ACTIVE_4_other=squeeze(ACTIVE_4_other);
  473. PASSIVE_2_1=eeg(:,:,passive_2_1_f==1);
  474. PASSIVE_2_1=mean(PASSIVE_2_1,1);
  475. PASSIVE_2_1=mean(PASSIVE_2_1,3);
  476. PASSIVE_2_1=squeeze(PASSIVE_2_1);
  478. PASSIVE_4_1=eeg(:,:,passive_4_1_f==1);
  479. PASSIVE_4_1=mean(PASSIVE_4_1,1);
  480. PASSIVE_4_1=mean(PASSIVE_4_1,3);
  481. PASSIVE_4_1=squeeze(PASSIVE_4_1);
  483. PASSIVE_2_other=eeg(:,:,passive_2_other_f==1);
  484. PASSIVE_2_other=mean(PASSIVE_2_other,1);
  485. PASSIVE_2_other=mean(PASSIVE_2_other,3);
  486. PASSIVE_2_other=squeeze(PASSIVE_2_other);
  488. PASSIVE_4_other=eeg(:,:,passive_4_other_f==1);
  489. PASSIVE_4_other=mean(PASSIVE_4_other,1);
  490. PASSIVE_4_other=mean(PASSIVE_4_other,3);
  491. PASSIVE_4_other=squeeze(PASSIVE_4_other);
  493. time= -1000:5:495;
  496. A_2_1= cat(2, num2cell(time'), num2cell(ACTIVE_2_1'), repmat({'Intention'}, length(ACTIVE_2_1),1), repmat({'2'}, length(ACTIVE_2_1),1), repmat({'First'}, length(ACTIVE_2_1),1));
  497. A_2_other= cat(2,num2cell(time'),num2cell(ACTIVE_2_other'), repmat({'Intention'}, length(ACTIVE_2_other),1), repmat({'2'}, length(ACTIVE_2_other),1), repmat({'Other'}, length(ACTIVE_2_other),1));
  498. A_4_1= cat(2,num2cell(time'),num2cell(ACTIVE_4_1'), repmat({'Intention'}, length(ACTIVE_4_1),1), repmat({'4'}, length(ACTIVE_4_1),1), repmat({'First'}, length(ACTIVE_4_1),1));
  499. A_4_other= cat(2,num2cell(time'),num2cell(ACTIVE_4_other'), repmat({'Intention'}, length(ACTIVE_4_other),1), repmat({'4'}, length(ACTIVE_4_other),1), repmat({'Other'}, length(ACTIVE_4_other),1));
  500. P_2_1= cat(2,num2cell(time'),num2cell(PASSIVE_2_1'), repmat({'Stimulus'}, length(PASSIVE_2_1),1), repmat({'2'}, length(PASSIVE_2_1),1), repmat({'First'}, length(PASSIVE_2_1),1));
  501. P_2_other= cat(2,num2cell(time'),num2cell(PASSIVE_2_other'), repmat({'Stimulus'}, length(PASSIVE_2_other),1), repmat({'2'}, length(PASSIVE_2_other),1), repmat({'Other'}, length(PASSIVE_2_other),1));
  502. P_4_1= cat(2,num2cell(time'),num2cell(PASSIVE_4_1'), repmat({'Stimulus'}, length(PASSIVE_4_1),1), repmat({'4'}, length(PASSIVE_4_1),1), repmat({'First'}, length(PASSIVE_4_1),1));
  503. P_4_other= cat(2,num2cell(time'),num2cell(PASSIVE_4_other'), repmat({'Stimulus'}, length(PASSIVE_4_other),1), repmat({'4'}, length(PASSIVE_4_other),1), repmat({'Other'}, length(PASSIVE_4_other),1));
  507. out= cat(1,A_2_1, A_2_other, A_4_1, A_4_other, P_2_1, P_2_other, P_4_1, P_4_other);
  508. out= cat(2, num2cell(repmat(sub, size(out,1),1)), out);
  510. T=table(out(:,1),out(:,2), out(:,3), out(:,4), out(:,5), out(:,6));
  511. T.Properties.VariableNames={'ID', 'Time', 'Amplitude', 'Condition', 'Moves','Trial'};
  514. % % % writetable(t_OUT,strcat('Proccessed_FiveChannelsAverage_S', num2str(sub), '.xlsx')); %write output
  515. writetable(T,strcat('Tol4Plot_S', num2str(sub), '.xlsx')); %write output
  517. clear TL;
  518. clear TL_f;
  519. clear OUT;
  520. clear t_OUT;
  521. clear index8;
  522. clear index9;
  523. clear average1;
  524. clear average2;
  525. clear average3;
  526. clear average4;
  527. clear ch_average500;
  528. clear ch_average1000;
  529. clear ch_std500;
  530. clear ch_std1000;
  531. clear ch_std;
  532. clear filter_event_3;
  534. end
  535. %% Extract action data
  536. for sub = exp.sub_id(1:end)
  537. EEG = pop_loadset(['/Volumes/S/02UCL/07EEGVolition&ProblemSolving/00EEG/02Preprocessing/acICAbe_df8rS3_P' num2str(sub) '.set']);
  539. %mean Amplitude 50ms%
  540. average1 = mean(EEG.data(:,191:200,:),2); %here
  541. ch_average50 = squeeze(mean(average1(exp.Chans,:,:),1)); %hered
  543. %mean Amplitude 100ms%
  544. average2 = mean(EEG.data(:,181:200,:),2); %here
  545. ch_average100 = squeeze(mean(average2(exp.Chans,:,:),1)); %hered
  547. %mean Amplitude 500ms%
  548. average3 = mean(EEG.data(:,101:200,:),2); %here
  549. ch_average500 = squeeze(mean(average3(exp.Chans,:,:),1)); %hered
  551. %mean Amplitude 1000ms%
  552. average4 = mean(EEG.data(:,1:200,:),2); %here
  553. ch_average1000 = squeeze(mean(average4(exp.Chans,:,:),1)); %hered
  556. %STD 50ms%
  558. average5 = mean(EEG.data(exp.Chans,191:200,:),1); %here
  559. ch_std50 = squeeze(std(average5(:,:,:),0,2)); %hered
  561. %STD 100ms%
  563. average6 = mean(EEG.data(exp.Chans,181:200,:),1); %here
  564. ch_std100 = squeeze(std(average6(:,:,:),0,2)); %hered
  566. %STD 500ms%
  568. average7 = mean(EEG.data(exp.Chans,101:200,:),1); %here
  569. ch_std500 = squeeze(std(average7(:,:,:),0,2)); %hered
  571. %STD 1000ms%
  573. average8 = mean(EEG.data(exp.Chans,1:200,:),1); %here
  574. ch_std1000 = squeeze(std(average8(:,:,:),0,2)); %hered
  576. % %mean and sd for 20 bins of 50 (49) ms (10 samples) each%
  578. avg_mtx= zeros(size(EEG.data,3),21) ; %initialize matrix of Average Amplitudes in 20 bins + 1 vector for slope
  579. sd_mtx= zeros(size(EEG.data,3),21) ; %initialize matrix of sd Amplitudes in 20 bins + 1 vector for slope
  581. for bin= 1:20
  582. sample_ini=1+(10*bin) -10;
  583. sample_end= 0+(10*bin);
  585. %average for each bin
  586. average_bin = mean(EEG.data(:,sample_ini:sample_end,:),2); %
  587. ch_average_bin = squeeze(mean(average_bin(exp.Chans,:,:),1)); %
  588. avg_mtx(:,bin)= ch_average_bin;
  590. clear average_bin;
  591. clear ch_average_bin;
  594. %sd for each bin
  595. sd_bin = std(EEG.data(:,sample_ini:sample_end,:),0,2); %
  596. ch_sd_bin = squeeze(mean(sd_bin(exp.Chans,:,:),1)); %
  597. sd_mtx(:,bin)= ch_sd_bin;
  599. clear average_bin;
  600. clear ch_average_bin;
  602. end
  605. %slope for 20 bins of 10 samples each
  606. for TRIAL = 1:size(avg_mtx,1)
  607. %get slope and write it in colum 21
  608. mod = fitlm([1:20],avg_mtx(TRIAL,1:size(avg_mtx,2)-1));
  609. avg_mtx(TRIAL,21)= mod.Coefficients.Estimate(2);
  611. mod_sd = fitlm([1:20],sd_mtx(TRIAL,1:size(sd_mtx,2)-1));
  612. sd_mtx(TRIAL,21)= mod_sd.Coefficients.Estimate(2);
  614. clear mod
  615. clear mod_sd
  616. end
  618. %slope for all samples
  619. for TRIAL = 1:size(avg_mtx,1)
  620. %get slope and write it in colum 21
  621. AMP=squeeze(mean(EEG.data(exp.Chans,1:200,TRIAL),1));
  622. mod_all = fitlm([1:200],AMP);
  623. ALL(TRIAL,1)= mod_all.Coefficients.Estimate(2);
  625. clear AMP
  626. clear mod_all
  627. end
  629. %get trial info
  631. % for index9= 1: size(EEG.event,2)
  632. % TL {index9,1}= EEG.event(index9).ID;
  633. % TL {index9,2}= EEG.event(index9).Random;
  634. % TL {index9,3}= EEG.event(index9).Run;
  635. % TL {index9,4}= EEG.event(index9).Active_Passive;
  636. % TL {index9,5}= EEG.event(index9).Problem_code;
  637. % TL {index9,6}= EEG.event(index9).min_number_of_moves;
  638. % TL {index9,7}= EEG.event(index9).Factor1;
  639. % TL {index9,8}= EEG.event(index9).Factor2;
  640. % TL {index9,9}= EEG.event(index9).Factor3;
  641. % TL {index9,10}= EEG.event(index9).Counter;
  642. % TL {index9,11}= EEG.event(index9).EventName;
  643. % TL {index9,12}= EEG.event(index9).Onset;
  644. % TL {index9,13}= EEG.event(index9).Duration;
  645. % TL {index9,14}= EEG.event(index9).Wrong_Choice;
  646. % TL {index9,15}= EEG.event(index9).Wrong_Release;
  647. % TL {index9,16}= EEG.event(index9).epoch;
  648. %
  649. % TL {index9,17}= EEG.event(index9).type(9); %n trigger
  650. % end
  652. for index9= 1: size(EEG.epoch,2)
  653. TL {index9,1}= EEG.epoch(index9).eventID{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  654. TL {index9,2}= EEG.epoch(index9).eventRandom{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  655. TL {index9,3}= EEG.epoch(index9).eventRun{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  656. TL {index9,4}= EEG.epoch(index9).eventActive_Passive{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  657. TL {index9,5}= EEG.epoch(index9).eventProblem_code{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  658. TL {index9,6}= EEG.epoch(index9).eventmin_number_of_moves{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  659. TL {index9,7}= EEG.epoch(index9).eventFactor1{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  660. TL {index9,8}= EEG.epoch(index9).eventFactor2{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  661. TL {index9,9}= EEG.epoch(index9).eventFactor3{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  662. TL {index9,10}= EEG.epoch(index9).eventCounter{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  663. TL {index9,11}= EEG.epoch(index9).eventEventName{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  664. TL {index9,12}= EEG.epoch(index9).eventOnset{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  665. TL {index9,13}= EEG.epoch(index9).eventDuration{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  666. TL {index9,14}= EEG.epoch(index9).eventWrong_Choice{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  667. TL {index9,15}= EEG.epoch(index9).eventWrong_Release{strcmp(EEG.epoch(index9).eventtype, 'Trigger 2')};
  668. end
  671. % for index8 = 1:size(TL,1)
  672. % filter_event_3(index8)= TL{index8,17}=='2'; %create filter that selects only rows with event = 2 (Action Choice)
  673. % end
  675. for label_number = 1:20
  676. if label_number==1
  677. avg_labels={['avg_Amplitude_bin' num2str(label_number)]};
  678. sd_labels={['sd_Amplitude_bin' num2str(label_number)]};
  680. end
  681. avg_labels{label_number}= ['avg_Amplitude_bin' num2str(label_number)];
  682. sd_labels{label_number}= ['sd_Amplitude_bin' num2str(label_number)];
  683. end
  685. avg_labels{21}= 'avg_Amplitude_SLOPE';
  686. sd_labels{21}= 'sd_Amplitude_SLOPE';
  689. %TL_f = TL(filter_event_3,:); %filtered trial info
  691. OUT = cat(2, TL, num2cell(ch_average50), num2cell(ch_average100), num2cell(ch_average500), num2cell(ch_average1000),num2cell(ch_std50),num2cell(ch_std100),num2cell(ch_std500),num2cell(ch_std1000), num2cell(avg_mtx), num2cell(sd_mtx), num2cell(ALL)); %
  692. t_OUT= cell2table(OUT);%convert into table
  694. t_OUT.Properties.VariableNames = cat(2, behnames, {'Average50', 'Average100', 'Average500', 'Average1000','SD50' , 'SD100', 'SD500', 'SD1000'}, avg_labels, sd_labels, {'SlopeAllSamples'} );%
  696. writetable(t_OUT,strcat('ToL_Proccessed_S', num2str(sub), '.xlsx')); %write output
  698. clear TL;
  699. clear TL_f;
  700. clear OUT;
  701. clear t_OUT;
  702. clear index8;
  703. clear index9;
  704. clear average1;
  705. clear average2;
  706. clear average3;
  707. clear average4;
  708. clear ch_average500;
  709. clear ch_average1000;
  710. clear ch_std500;
  711. clear ch_std1000;
  712. clear ch_std;
  713. clear filter_event_3;
  715. end

Preprocessing.m, no license · at the source

Overview

Authors: Silvia Seghezzi1,2, Patrick Haggard1
ORCID iDs: Silvia Seghezzi, Patrick Haggard
  1. University College London, London WC1 3AZ, United Kingdom
  2. Birkbeck, University of London, London WC1E 7HX, United Kingdom
Institutions: University College London (United Kingdom); Birkbeck, University of London (United Kingdom)
Journal: eNeuro, volume 13, issue 5, pages ENEURO.0316-25.2026
Dates: received 25 August 2025; accepted 17 April 2026; published online 22 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0316-25.2026 · PMID 42120201 · PMCID PMC13197169 · OpenAlex W7160927533
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: EEG, executive functions, problem-solving, readiness potential, volition
MeSH: Brain*, Executive Function*, Motor Activity*, Problem Solving*, Psychomotor Performance*, Contingent Negative Variation, Electroencephalography, Female, Humans, Male, Neuropsychological Tests, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Fetzer Institute (4189); John Templeton Fondation (61283)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Complex problems often allow multiple paths to a solution. Choosing and taking the best path is an important part of the executive cognition that underpins intelligent problem-solving behavior. However, once a path is chosen, the motor system must be activated for executing it. This interface between problem-solving and self-generated action has rarely been studied. We recorded EEG movement-related potentials while 25 participants (7 males, 18 females) performed the “Tower of London” problem-solving task. In a control condition, participants merely followed instructed steps without planning for any goal and thus without any sense that their movements solved a problem. Readiness potentials (RPs) preceding actions showed a more sustained preparatory negativity for self-generated than stimulus-driven movements. Critically, this effect was most pronounced at the first move of a sequence and diminished at later stages, indicating that preparatory activity is closely linked to the planning demands of sequence initiation. Consistent with this, contralateral motor β-band suppression was stronger for self-generated actions, particularly at sequence onset, but remained present across all moves, indicating that it is not selectively modulated by sequence position in the same way as the RP. Multivariate pattern analysis further showed that self-generated and stimulus-driven actions could be reliably distinguished throughout the entire preparatory period. Taken together, these results show a deep interaction between executive function and self-generated actions and draw attention to the fact that, if a problem can be solved, then actually solving it generally requires executive cognition to trigger self-generated actions, based on a plan.

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

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OSF c9yek

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State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: the text, “Behavioral data analysis”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (6 files), FieldTrip (5 files), EEGLAB (4 files), brms (1 file), emmeans (1 file), ERPLAB (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), reshape2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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10 files
At the source: osf.io/c9yek/?view_only=4d853c7815024afc8db10f20579e7d73

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Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 12 MeSH terms, 2 funders, 51 references.

Cite

This paper

Seghezzi, S., & Haggard, P. (2026). Neural Mechanisms of Self-Generated Action Sequences. eNeuro, 13(5), ENEURO.0316-25.2026. https://doi.org/10.1523/eneuro.0316-25.2026

BibTeX

@article{seghezzi2026neural,
author = {Seghezzi, Silvia and Haggard, Patrick},
title = {{Neural Mechanisms of Self-Generated Action Sequences}},
journal = {eNeuro},
year = {2026},
month = may,
volume = {13},
number = {5},
pages = {ENEURO.0316--25.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0316-25.2026},
url = {https://doi.org/10.1523/eneuro.0316-25.2026},
pmid = {42120201},
pmcid = {PMC13197169}
}

RIS

TY - JOUR
AU - Seghezzi, Silvia
AU - Haggard, Patrick
TI - Neural Mechanisms of Self-Generated Action Sequences
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/05/22
VL - 13
IS - 5
SP - ENEURO.0316
EP - 25.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0316-25.2026
UR - https://doi.org/10.1523/eneuro.0316-25.2026
LA - en
ER -

CSL-JSON

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"id": "10.1523/eneuro.0316-25.2026",
"type": "article-journal",
"title": "Neural Mechanisms of Self-Generated Action Sequences",
"container-title": "eNeuro",
"author": [
{
"family": "Seghezzi",
"given": "Silvia"
},
{
"family": "Haggard",
"given": "Patrick"
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "5",
"page": "ENEURO.0316-25.2026",
"DOI": "10.1523/eneuro.0316-25.2026",
"PMID": "42120201",
"PMCID": "PMC13197169",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0316-25.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
22
]
]
}
}

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