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Tracing the neural trajectories of evidence accumulation and motor preparation processes during voluntary decisions.

Code ↔ Paper

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The 16 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Method › EEG analyses ↔ EEG Processing/wavelet_convolution.m, lines 1 to 96 · score 0.89 · complex Morlet wavelet, wavelet cycles, baseline corrected, convolution, stimulus onset, dB · Report a mismatch
  2. [2] § Method › EEG data acquisition and processing ↔ EEG Processing/eegca_3_preprocessing_step2.m, lines 130 to 140 · score 0.88 · rank deficiency, high pass filtered, removed channel, bad channels, compensate, stationarity · Report a mismatch
  3. [3] § Method › EEG analyses › Characterising evidence accumulation and motor preparation signatures of voluntary decisions ↔ RT and EEG Analyses /RT and Fixed Window Analyses/eegca_ERP_LMM.Rmd, lines 352 to 395 · score 0.81 · random intercept models, likelihood ratio, random slope models, model comparisons, RT · Report a mismatch
  4. [4] § Method › EEG data acquisition and processing ↔ EEG Processing/eegca_4_preprocessing_step3.m, lines 139 to 150 · score 0.80 · spherical spline interpolation, scalp channel, bad channels, EEGLAB, noisy · Report a mismatch
  5. [5] § Method › EEG analyses › Characterising evidence accumulation and motor preparation signatures of voluntary decisions ↔ RT and EEG Analyses /Mass Univariate Analyses/eegca_massuni_ERP_stimlocked.m, lines 1274 to 1398 · score 0.59 · Bayes Factors, Bayesian, permutation, cluster · Report a mismatch
  6. [6] § Method › EEG analyses › Characterising evidence accumulation and motor preparation signatures of voluntary decisions ↔ RT and EEG Analyses /Mass Univariate Analyses/eegca_massuni_ERP_resplocked.m, lines 1317 to 1433 · score 0.59 · Bayes Factors, Bayesian, permutation, cluster · Report a mismatch
  7. [7] § Results › MB amplitudes exhibit accumulation-to-bound dynamics in voluntary decisions ↔ RT and EEG Analyses /RT and Fixed Window Analyses/eegca_derive_MB_measures.m, lines 11 to 21 · score 0.59 · slope window, amplitude window, fixed window, MB amplitudes, Mu, Beta · Report a mismatch
  8. [8] § Method › EEG analyses › Characterising evidence accumulation and motor preparation signatures of voluntary decisions ↔ RT and EEG Analyses /RT and Fixed Window Analyses/eegca_ERP_LMM.Rmd, lines 8 to 76 · score 0.58 · log transformed RTs, LMMs, scored, slope, amplitude, EEG · Report a mismatch
  9. [9] § Method › EEG analyses › Characterising evidence accumulation and motor preparation signatures of voluntary decisions ↔ RT and EEG Analyses /Mass Univariate Analyses/eegca_multcomp_cluster_permtest.m, lines 1 to 121 · score 0.57 · cluster mass, summed, uncorrected, zero, tailed, iterations · Report a mismatch
  10. [10] § Method › Procedure ↔ Experiment Code/Functions Library/eegca_experiment_parameters.m, the whole file · a weak match · score 0.57 · paced break, practice block, decision phase, EEG · Report a mismatch
  11. [11] § Results › MB amplitudes exhibit accumulation-to-bound dynamics in voluntary decisions ↔ RT and EEG Analyses /Mass Univariate Analyses/eegca_massuni_MB.m, lines 550 to 666 · score 0.57 · 8–30 Hz, slope window, MB amplitudes, response locked, sliding, Mu · Report a mismatch
  12. [12] § Method › Procedure ↔ Experiment Code/RUN_eegca_practice_session.m, lines 666 to 713 · score 0.54 · paced break, practice block, phase · Report a mismatch
  13. [13] § Results ↔ RT and EEG Analyses /Mass Univariate Analyses/eegca_massuni_MB.m, lines 812 to 935 · score 0.53 · 8–30 Hz, shaded standard error, waves, Mu, Beta, maps · Report a mismatch
  14. [14] § Results › The CPP exhibits accumulation-to-bound dynamics in voluntary decisions ↔ RT and EEG Analyses /RT and Fixed Window Analyses/eegca_derive_ERP_measures.m, lines 20 to 59 · score 0.52 · slope window, pre response amplitude, CPP amplitudes, electrode, ERP, RT · Report a mismatch
  15. [15] § Results › The CPP exhibits accumulation-to-bound dynamics in voluntary decisions ↔ RT and EEG Analyses /Mass Univariate Analyses/eegca_massuni_MB.m, lines 1009 to 1147 · score 0.50 · Bayes factors, standard errors, BF, smoothed, Gaussian, forced decisions · Report a mismatch
  16. [16] § Method › EEG analyses › Characterising evidence accumulation and motor preparation signatures of voluntary decisions ↔ RT and EEG Analyses /RT and Fixed Window Analyses/eegca_derive_ERP_measures.m, lines 361 to 431 · score 0.50 · linear regression, pre response amplitudes, model, epoch, slopes · Report a mismatch

Paper

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The authors' code

MATLAB · 1,148 lines · 48 KB · no license · 3 matches

  2. %% Housekeeping
  4. % Clear all variables in the workspace, and close all windows
  5. clear all;
  6. %close all;
  8. % Change working directory to that which contains this script
  9. mfile_name = mfilename('fullpath');
  10. [pathstring, nameOfFile, extensionString] = fileparts(mfile_name);
  11. cd(pathstring);
  15. %% Setting Parameters
  17. % IDs to process
  18. participantIDs = [1:29,31:50];
  20. % Choose experiment name
  21. experimentName = 'eegca_p';
  24. %% Analysis Options
  26. % Cluster-based permutation test options
  27. cluster_permtest_criticalAlpha = 0.05; % Critical cluster-level alpha
  28. cluster_permtest_clusteringAlpha = 0.01; % Cluster-forming alpha level
  29. cluster_permtest_iterations = 10000; % Number of permutation samples
  31. % Define sliding window parameters
  32. win_ms = 300; % your chosen window
  33. polyord = 2;
  34. MB_freq = 8:30;
  35. fs = 512;
  37. min_trials_for_reg = 20; % guardrail: need at least this many trials/condition for OLS
  38. min_obs_for_OLS = 10; % guardrail: at each timepoint, need >= this many finite values
  40. % Preallocate β matrices
  41. mb_slope_beta_RT.free = []; % slope β(t) in Free
  42. mb_slope_beta_RT.forced = []; % slope β(t) in Forced
  43. mb_amp_beta_RT.free = []; % amp β(t) in Free
  44. mb_amp_beta_RT.forced = []; % amp β(t) in Forced
  46. group_time_valid = []; % time axis (ms) for the "valid" interior samples
  49. %% Preallocate matrices to store trial by trial RT data across all participants
  51. % Group.DecPhaseRT = [];
  52. % Group.CoMPhaseRT = [];
  53. Group.DecFreeRT = [];
  54. % Group.DecFreeFast = [];
  55. % Group.DecFreeMedium = [];
  56. % Group.DecFreeSlow = [];
  57. Group.DecForcedRT = [];
  58. % Group.DecForcedFast = [];
  59. % Group.DecForcedMedium = [];
  60. % Group.DecForcedMedSlow = [];
  61. % Group.DecForcedSlow = [];
  65. %% EEG Data Processing
  67. for datasetNo = 1:length(participantIDs)
  69. % Announce the ID number we are processing
  70. fprintf(['-----------------------------------------------------', ...
  71. '\n\nProcessing participant ID ' int2str(participantIDs(datasetNo)), ' ****** Averaging ERPs\n\n', ...
  72. '-----------------------------------------------------\n\n']);
  76. %% -- Load EEG + Behavioural Data Files
  78. % Epoched EEG data file
  79. load(['EEG Data/Epoched Data/', experimentName, int2str(participantIDs(datasetNo)), '_MuBeta_CSD.mat']);
  81. % Behavioural data file
  82. %load(['Behavioural Data/Cleaned Behavioural Data/p' int2str(participantIDs(datasetNo)), '_cleaned.mat']);
  83. load(['Channel Locations/channel_information.mat'], 'chanlocs');
  85. %% -- Calculate Mean and SD
  87. % - Decision Phase RTs
  88. % Remove trials with RTs less than 200ms
  89. decBadTrials = [];
  90. for trialNo = 1:numel(TC_Resp_DecPhase.DecPhaseRT)
  91. if TC_Resp_DecPhase.DecPhaseRT(trialNo) < 0.2
  92. decBadTrials = [decBadTrials, trialNo];
  93. end
  94. end
  96. variables_TC_Resp_DecPhase = fieldnames(TC_Resp_DecPhase);
  98. for variable_index = 1:numel(variables_TC_Resp_DecPhase)
  99. TC_Resp_DecPhase.(variables_TC_Resp_DecPhase{variable_index})(:, decBadTrials) = [];
  100. end
  102. MuBeta_Resp{1,1}(:,:,decBadTrials) = [];
  105. % Calculate mean, median, and standard deviation
  106. mean_DecPhaseRTs = mean(TC_Resp_DecPhase.DecPhaseRT);
  107. median_DecPhaseRTs = median(TC_Resp_DecPhase.DecPhaseRT);
  108. std_DecPhaseRTs = std(TC_Resp_DecPhase.DecPhaseRT);
  109. DecPhaseMaxCutoff = 20;
  111. DecPhaseFreeRTs = TC_Resp_DecPhase.DecPhaseRT(TC_Resp_DecPhase.DecPhaseCondition == "free");
  112. DecPhaseFreeRTs = DecPhaseFreeRTs(DecPhaseFreeRTs <= DecPhaseMaxCutoff);
  113. DecPhaseForcedRTs = TC_Resp_DecPhase.DecPhaseRT(TC_Resp_DecPhase.DecPhaseCondition == "forced");
  114. DecPhaseForcedRTs = DecPhaseForcedRTs(DecPhaseForcedRTs < DecPhaseMaxCutoff);
  115. DecPhaseFreeRT_bins = prctile(DecPhaseFreeRTs, [33.33,66.67]);
  116. DecPhaseForcedRT_bins = prctile(DecPhaseForcedRTs, [33.33,66.67]);
  118. % Preallocate tertile matrices
  119. usableDecTrials = length(TC_Resp_DecPhase.DecPhaseRT);
  120. TC_Resp_DecPhase.FreeFast(1:usableDecTrials) = nan;
  121. TC_Resp_DecPhase.FreeMedium(1:usableDecTrials) = nan;
  122. TC_Resp_DecPhase.FreeSlow(1:usableDecTrials) = nan;
  123. TC_Resp_DecPhase.ForcedFast(1:usableDecTrials) = nan;
  124. TC_Resp_DecPhase.ForcedMedium(1:usableDecTrials) = nan;
  125. TC_Resp_DecPhase.ForcedSlow(1:usableDecTrials) = nan;
  127. clear trialNo
  129. for trialNo = 1:length(TC_Resp_DecPhase.DecPhaseRT)
  130. if TC_Resp_DecPhase.DecPhaseCondition{trialNo} == "free"
  131. TC_Resp_DecPhase.FreeRT(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  133. % FREE FASTEST Quartile
  134. if TC_Resp_DecPhase.DecPhaseRT(trialNo) < DecPhaseFreeRT_bins(1)
  135. TC_Resp_DecPhase.FreeFast(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  136. TC_Resp_DecPhase.FreeMedium(trialNo) = nan;
  137. TC_Resp_DecPhase.FreeSlow(trialNo) = nan;
  138. Tertile.DecIndex(datasetNo,trialNo) = 1;
  140. % FREE MEDIUM Quartile
  141. elseif TC_Resp_DecPhase.DecPhaseRT(trialNo) >= DecPhaseFreeRT_bins(1) && TC_Resp_DecPhase.DecPhaseRT(trialNo) < DecPhaseFreeRT_bins(2)
  142. TC_Resp_DecPhase.FreeMedium(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  143. TC_Resp_DecPhase.FreeFast(trialNo) = nan;
  144. TC_Resp_DecPhase.FreeSlow(trialNo) = nan;
  145. Tertile.DecIndex(datasetNo,trialNo) = 2;
  146. % FREE SLOWEST Quartile
  147. else
  148. TC_Resp_DecPhase.FreeSlow(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  149. TC_Resp_DecPhase.FreeFast(trialNo) = nan;
  150. TC_Resp_DecPhase.FreeMedium(trialNo) = nan;
  151. Tertile.DecIndex(datasetNo,trialNo) = 3;
  152. end
  154. elseif TC_Resp_DecPhase.DecPhaseCondition{trialNo} == "forced"
  156. TC_Resp_DecPhase.ForcedRT(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  158. % FORCED FASTEST Quartile
  159. if TC_Resp_DecPhase.DecPhaseRT(trialNo) < DecPhaseForcedRT_bins(1)
  160. TC_Resp_DecPhase.ForcedFast(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  161. TC_Resp_DecPhase.ForcedMedium(trialNo) = nan;
  162. TC_Resp_DecPhase.ForcedSlow(trialNo) = nan;
  163. Tertile.DecIndex(datasetNo, trialNo) = 4;
  165. % FORCED MEDIUM FAST Quartile
  166. elseif TC_Resp_DecPhase.DecPhaseRT(trialNo) >= DecPhaseForcedRT_bins(1) && TC_Resp_DecPhase.DecPhaseRT(trialNo) < DecPhaseForcedRT_bins(2)
  167. TC_Resp_DecPhase.ForcedMedium(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  168. TC_Resp_DecPhase.ForcedFast(trialNo) = nan;
  169. TC_Resp_DecPhase.ForcedSlow(trialNo) = nan;
  170. Tertile.DecIndex(datasetNo, trialNo) = 5;
  172. % FORCED SLOWEST Quartile
  173. else
  174. TC_Resp_DecPhase.ForcedSlow(trialNo) = TC_Resp_DecPhase.DecPhaseRT(trialNo);
  175. TC_Resp_DecPhase.ForcedFast(trialNo) = nan;
  176. TC_Resp_DecPhase.ForcedMedium(trialNo) = nan;
  177. Tertile.DecIndex(datasetNo, trialNo) = 6;
  178. end
  180. end
  181. end
  183. % % - CoM Phase RTs
  184. % % Remove trials with RTs less than 200ms
  185. % comBadTrials = [];
  186. % for trialNo = 1:numel(TC_Resp_CoMPhase.CoMPhaseRT)
  187. % if TC_Resp_CoMPhase.CoMPhaseRT(trialNo) < 0.2
  188. % comBadTrials = [comBadTrials, trialNo];
  189. % end
  190. % end
  191. %
  192. % variables_TC_Resp_CoMPhase = fieldnames(TC_Resp_CoMPhase);
  193. %
  194. % for variable_index = 1:numel(variables_TC_Resp_CoMPhase)
  195. % TC_Resp_CoMPhase.(variables_TC_Resp_CoMPhase{variable_index})(:, comBadTrials) = [];
  196. % end
  197. %
  198. % EEG_Data_Resp_CoMPhase(:,:,comBadTrials) = [];
  199. %
  200. % % Calculate mean, median, and standard deviation
  201. % mean_CoMPhaseRTs = mean(TC_Resp_CoMPhase.CoMPhaseRT);
  202. % median_CoMPhaseRTs = median(TC_Resp_CoMPhase.CoMPhaseRT);
  203. % std_CoMPhaseRTs = std(TC_Resp_CoMPhase.CoMPhaseRT);
  204. % CoMPhaseRTMaxCutoff = 10;
  205. %
  206. % CoMPhaseFreeRTs = TC_Resp_CoMPhase.CoMPhaseRT(TC_Resp_CoMPhase.CoMPhaseCondition == "free");
  207. % CoMPhaseFreeRTs = CoMPhaseFreeRTs(CoMPhaseFreeRTs < CoMPhaseRTMaxCutoff);
  208. % CoMPhaseFreeSwitch = TC_Resp_CoMPhase.CoMPhaseRT(strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, "free") & TC_Resp_CoMPhase.SwitchStay_Numeric == 1);
  209. % CoMPhaseFreeSwitch = CoMPhaseFreeSwitch(CoMPhaseFreeSwitch < CoMPhaseRTMaxCutoff);
  210. % CoMPhaseFreeStayed = TC_Resp_CoMPhase.CoMPhaseRT(strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & TC_Resp_CoMPhase.SwitchStay_Numeric == 2);
  211. % CoMPhaseFreeStayed = CoMPhaseFreeStayed(CoMPhaseFreeStayed < CoMPhaseRTMaxCutoff);
  212. %
  213. % CoMPhaseForcedRTs = TC_Resp_CoMPhase.CoMPhaseRT(TC_Resp_CoMPhase.CoMPhaseCondition == "forced");
  214. % CoMPhaseForcedRTs = CoMPhaseForcedRTs(CoMPhaseForcedRTs < CoMPhaseRTMaxCutoff);
  215. % CoMPhaseForcedSwitch = TC_Resp_CoMPhase.CoMPhaseRT(strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & TC_Resp_CoMPhase.SwitchStay_Numeric == 1);
  216. % CoMPhaseForcedSwitch = CoMPhaseForcedSwitch(CoMPhaseForcedSwitch < CoMPhaseRTMaxCutoff);
  217. % CoMPhaseForcedStayed = TC_Resp_CoMPhase.CoMPhaseRT(strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & TC_Resp_CoMPhase.SwitchStay_Numeric == 2);
  218. % CoMPhaseForcedStayed = CoMPhaseForcedStayed(CoMPhaseForcedStayed < CoMPhaseRTMaxCutoff);
  219. %
  220. % CoMPhaseFreeRT_bins = prctile(CoMPhaseFreeRTs, [33.33,66.67]);
  221. % CoMPhaseForcedRT_bins = prctile(CoMPhaseForcedRTs, [33.33,66.67]);
  222. %
  223. % % Preallocate tertile matrices
  224. % usableCoMTrials = length(TC_Resp_CoMPhase.CoMPhaseRT);
  225. % TC_Resp_CoMPhase.FreeFast(1:usableCoMTrials) = nan;
  226. % TC_Resp_CoMPhase.FreeMedium(1:usableCoMTrials) = nan;
  227. % TC_Resp_CoMPhase.FreeSlow(1:usableCoMTrials) = nan;
  228. % TC_Resp_CoMPhase.ForcedFast(1:usableCoMTrials) = nan;
  229. % TC_Resp_CoMPhase.ForcedMedium(1:usableCoMTrials) = nan;
  230. % TC_Resp_CoMPhase.ForcedSlow(1:usableCoMTrials) = nan;
  231. %
  232. % clear trialNo
  233. %
  234. % for trialNo = 1:length(TC_Resp_CoMPhase.CoMPhaseRT)
  235. % if TC_Resp_CoMPhase.CoMPhaseCondition{trialNo} == "free"
  236. %
  237. % TC_Resp_CoMPhase.FreeRT(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  238. %
  239. % if TC_Resp_CoMPhase.SwitchStay_Numeric(trialNo) == 1
  240. %
  241. % TC_Resp_CoMPhase.FreeSwitch(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  242. %
  243. % elseif TC_Resp_CoMPhase.SwitchStay_Numeric(trialNo) == 2
  244. %
  245. % TC_Resp_CoMPhase.FreeStay(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  246. % end
  247. %
  248. %
  249. % % FREE FAST Tertile
  250. % if TC_Resp_CoMPhase.CoMPhaseRT(trialNo) < CoMPhaseFreeRT_bins(1)
  251. %
  252. % TC_Resp_CoMPhase.FreeFast(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  253. % TC_Resp_CoMPhase.FreeMedium(trialNo) = nan;
  254. % TC_Resp_CoMPhase.FreeSlow(trialNo) = nan;
  255. % Tertile.CoMIndex(datasetNo,trialNo)= 1;
  256. %
  257. % % FREE SLOW Tertile
  258. % elseif TC_Resp_CoMPhase.CoMPhaseRT(trialNo) > CoMPhaseFreeRT_bins(2)
  259. %
  260. % TC_Resp_CoMPhase.FreeSlow(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  261. % TC_Resp_CoMPhase.FreeMedium(trialNo) = nan;
  262. % TC_Resp_CoMPhase.FreeFast(trialNo) = nan;
  263. % Tertile.CoMIndex(datasetNo,trialNo)= 3;
  264. %
  265. % else
  266. % % FREE MEDIUM Tertile
  267. % TC_Resp_CoMPhase.FreeMedium(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  268. % TC_Resp_CoMPhase.FreeFast(trialNo) = nan;
  269. % TC_Resp_CoMPhase.FreeSlow(trialNo) = nan;
  270. % Tertile.CoMIndex(datasetNo,trialNo)= 2;
  271. % end
  272. % elseif TC_Resp_CoMPhase.CoMPhaseCondition{trialNo} == "forced"
  273. %
  274. % TC_Resp_CoMPhase.ForcedRT(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  275. %
  276. % if TC_Resp_CoMPhase.SwitchStay_Numeric(trialNo) == 1
  277. %
  278. % TC_Resp_CoMPhase.ForcedSwitch(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  279. %
  280. % elseif TC_Resp_CoMPhase.SwitchStay_Numeric(trialNo) == 2
  281. %
  282. % TC_Resp_CoMPhase.ForcedStay(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  283. % end
  284. %
  285. %
  286. % % FORCED FAST Tertile
  287. % if TC_Resp_CoMPhase.CoMPhaseRT(trialNo) < CoMPhaseForcedRT_bins(1)
  288. %
  289. % TC_Resp_CoMPhase.ForcedFast(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  290. % TC_Resp_CoMPhase.ForcedMedium(trialNo) = nan;
  291. % TC_Resp_CoMPhase.ForcedSlow(trialNo) = nan;
  292. % Tertile.CoMIndex(datasetNo,trialNo)= 4;
  293. %
  294. % % FORCED SLOW Tertile
  295. % elseif TC_Resp_CoMPhase.CoMPhaseRT(trialNo) > CoMPhaseForcedRT_bins(2)
  296. %
  297. % TC_Resp_CoMPhase.ForcedSlow(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  298. % TC_Resp_CoMPhase.ForcedMedium(trialNo) = nan;
  299. % TC_Resp_CoMPhase.ForcedFast(trialNo) = nan;
  300. % Tertile.CoMIndex(datasetNo,trialNo)= 6;
  301. %
  302. % else
  303. % % FORCED MEDIUM Tertile
  304. % TC_Resp_CoMPhase.ForcedMedium(trialNo) = TC_Resp_CoMPhase.CoMPhaseRT(trialNo);
  305. % TC_Resp_CoMPhase.ForcedFast(trialNo) = nan;
  306. % TC_Resp_CoMPhase.ForcedSlow(trialNo) = nan;
  307. % Tertile.CoMIndex(datasetNo,trialNo)= 5;
  308. % end
  309. %
  310. % end
  311. % end
  312. %
  313. % % - Choice Phase RTs
  314. % ChoicePhaseRTs = Res.Timing.choiceReportRTs(~isnan(Res.Timing.choiceReportRTs));
  315. % ChoicePhaseRTs = ChoicePhaseRTs(:); % Flatten matrix into single vector
  316. %
  317. % % Calculate mean, median, and standard deviation
  318. % mean_ChoicePhaseRTs = mean(ChoicePhaseRTs);
  319. % median_ChoicePhaseRTs = median(ChoicePhaseRTs);
  320. % std_ChoicePhaseRTs = std(ChoicePhaseRTs);
  321. % ChoicePhaseRTsCutoff = median_ChoicePhaseRTs + 3 * std_ChoicePhaseRTs;
  322. %
  323. % CoMReportRTs = Res.Timing.CoMSwitchReportRTs(~isnan(Res.Timing.CoMSwitchReportRTs));
  324. % CoMReportRTs = CoMReportRTs(:); % Flatten matrix into single vector
  325. %
  326. % % Calculate mean, median, and standard deviation
  327. % mean_CoMReportRTs = mean(CoMReportRTs);
  328. % median_CoMReportRTs = median(CoMReportRTs);
  329. % std_CoMReportRTs = std(CoMReportRTs);
  330. % CoMReportRTsCutoff = median_CoMReportRTs + 3 * std_CoMReportRTs;
  333. %% Remove zeros from TC RT structures to calculate means
  335. % Filter out zero elements
  336. TC_Resp_DecPhase.FreeRT = TC_Resp_DecPhase.FreeRT(TC_Resp_DecPhase.FreeRT ~= 0);
  337. TC_Resp_DecPhase.FreeFast = TC_Resp_DecPhase.FreeFast(TC_Resp_DecPhase.FreeFast ~= 0);
  338. TC_Resp_DecPhase.FreeMedium = TC_Resp_DecPhase.FreeMedium(TC_Resp_DecPhase.FreeMedium ~= 0);
  339. TC_Resp_DecPhase.FreeSlow = TC_Resp_DecPhase.FreeSlow(TC_Resp_DecPhase.FreeSlow ~= 0);
  340. TC_Resp_DecPhase.ForcedRT = TC_Resp_DecPhase.ForcedRT(TC_Resp_DecPhase.ForcedRT ~= 0);
  341. TC_Resp_DecPhase.ForcedFast = TC_Resp_DecPhase.ForcedFast(TC_Resp_DecPhase.ForcedFast ~= 0);
  342. TC_Resp_DecPhase.ForcedMedium = TC_Resp_DecPhase.ForcedMedium(TC_Resp_DecPhase.ForcedMedium ~= 0);
  343. TC_Resp_DecPhase.ForcedSlow = TC_Resp_DecPhase.ForcedSlow(TC_Resp_DecPhase.ForcedSlow ~= 0);
  345. % TC_Resp_CoMPhase.FreeRT = TC_Resp_CoMPhase.FreeRT(TC_Resp_CoMPhase.FreeRT ~= 0);
  346. % TC_Resp_CoMPhase.FreeSwitch = TC_Resp_CoMPhase.FreeSwitch(TC_Resp_CoMPhase.FreeSwitch~=0);
  347. % TC_Resp_CoMPhase.FreeStay = TC_Resp_CoMPhase.FreeStay(TC_Resp_CoMPhase.FreeStay ~= 0);
  348. % TC_Resp_CoMPhase.FreeFast = TC_Resp_CoMPhase.FreeFast(TC_Resp_CoMPhase.FreeFast ~= 0);
  349. % TC_Resp_CoMPhase.FreeMedium = TC_Resp_CoMPhase.FreeMedium(TC_Resp_CoMPhase.FreeMedium ~= 0);
  350. % TC_Resp_CoMPhase.FreeSlow = TC_Resp_CoMPhase.FreeSlow(TC_Resp_CoMPhase.FreeSlow ~= 0);
  351. % TC_Resp_CoMPhase.ForcedRT = TC_Resp_CoMPhase.ForcedRT(TC_Resp_CoMPhase.ForcedRT ~= 0);
  352. % TC_Resp_CoMPhase.ForcedSwitch = TC_Resp_CoMPhase.ForcedSwitch(TC_Resp_CoMPhase.ForcedSwitch ~= 0);
  353. % TC_Resp_CoMPhase.ForcedStay = TC_Resp_CoMPhase.ForcedStay(TC_Resp_CoMPhase.ForcedStay ~= 0);
  354. % TC_Resp_CoMPhase.ForcedFast = TC_Resp_CoMPhase.ForcedFast(TC_Resp_CoMPhase.ForcedFast ~= 0);
  355. % TC_Resp_CoMPhase.ForcedMedium = TC_Resp_CoMPhase.ForcedMedium(TC_Resp_CoMPhase.ForcedMedium ~= 0);
  356. % TC_Resp_CoMPhase.ForcedSlow = TC_Resp_CoMPhase.ForcedSlow(TC_Resp_CoMPhase.ForcedSlow ~= 0);
  357. %
  359. Group.DecFreeRT = [Group.DecFreeRT; TC_Resp_DecPhase.FreeRT'];
  360. Group.DecForcedRT = [Group.DecForcedRT; TC_Resp_DecPhase.ForcedRT'];
  363. %% Calculate Individual Level Tertile Means and Medians
  364. clear tertileNo
  366. for tertileNo = 1:length(participantIDs)
  367. Tertile.MeanDecFreeRT(datasetNo) = nanmean(TC_Resp_DecPhase.FreeRT);
  368. Tertile.MeanDecFreeFast(datasetNo) = nanmean(TC_Resp_DecPhase.FreeFast);
  369. Tertile.MeanDecFreeMedium(datasetNo) = nanmean(TC_Resp_DecPhase.FreeMedium);
  370. Tertile.MeanDecFreeSlow(datasetNo) = nanmean(TC_Resp_DecPhase.FreeSlow);
  371. Tertile.MeanDecForcedRT(datasetNo) = nanmean(TC_Resp_DecPhase.ForcedRT);
  372. Tertile.MeanDecForcedFast(datasetNo) = nanmean(TC_Resp_DecPhase.ForcedFast);
  373. Tertile.MeanDecForcedMedium(datasetNo) = nanmean(TC_Resp_DecPhase.ForcedMedium);
  374. Tertile.MeanDecForcedSlow(datasetNo) = nanmean(TC_Resp_DecPhase.ForcedSlow);
  376. % Tertile.MeanCoMFreeRT(datasetNo) = nanmean(TC_Resp_CoMPhase.FreeRT);
  377. % Tertile.MeanCoMFreeSwitch(datasetNo) = nanmean(TC_Resp_CoMPhase.FreeSwitch);
  378. % Tertile.MeanCoMFreeStay(datasetNo) = nanmean(TC_Resp_CoMPhase.FreeStay);
  379. % Tertile.MeanCoMFreeFast(datasetNo) = nanmean(TC_Resp_CoMPhase.FreeFast);
  380. % Tertile.MeanCoMFreeMedium(datasetNo) = nanmean(TC_Resp_CoMPhase.FreeMedium);
  381. % Tertile.MeanCoMFreeSlow(datasetNo) = nanmean(TC_Resp_CoMPhase.FreeSlow);
  382. % Tertile.MeanCoMForcedRT(datasetNo) = nanmean(TC_Resp_CoMPhase.ForcedRT);
  383. % Tertile.MeanCoMForcedSwitch(datasetNo) = nanmean(TC_Resp_CoMPhase.ForcedSwitch);
  384. % Tertile.MeanCoMForcedStay(datasetNo) = nanmean(TC_Resp_CoMPhase.ForcedStay);
  385. % Tertile.MeanCoMForcedFast(datasetNo) = nanmean(TC_Resp_CoMPhase.ForcedFast);
  386. % Tertile.MeanCoMForcedMedium(datasetNo) = nanmean(TC_Resp_CoMPhase.ForcedMedium);
  387. % Tertile.MeanCoMForcedSlow(datasetNo) = nanmean(TC_Resp_CoMPhase.ForcedSlow);
  389. end
  393. %% -- Average ERPs by Condition
  395. % Free Trials
  396. Epoch.FreeStandaloneAll(:, :, datasetNo) = ...
  397. nanmean(MuBeta_Resp{1,1}(:, :, ...
  398. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'free') & ...
  399. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  400. TC_Resp_DecPhase.DecPhaseRT < DecPhaseMaxCutoff & ...
  401. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  403. Epoch.FreeStandaloneFast(:, :, datasetNo) = ...
  404. nanmean(MuBeta_Resp{1,1}(:, :, ...
  405. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'free') & ...
  406. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  407. TC_Resp_DecPhase.DecPhaseRT < DecPhaseFreeRT_bins(1) & ...
  408. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  410. Epoch.FreeStandaloneMedium(:, :, datasetNo) = ...
  411. nanmean(MuBeta_Resp{1,1}(:, :, ...
  412. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'free') & ...
  413. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  414. TC_Resp_DecPhase.DecPhaseRT >= DecPhaseFreeRT_bins(1) & ...
  415. TC_Resp_DecPhase.DecPhaseRT < DecPhaseFreeRT_bins(2) & ...
  416. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  418. Epoch.FreeStandaloneSlow(:, :, datasetNo) = ...
  419. nanmean(MuBeta_Resp{1,1}(:, :, ...
  420. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'free') & ...
  421. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  422. TC_Resp_DecPhase.DecPhaseRT >= DecPhaseFreeRT_bins(2) & ...
  423. TC_Resp_DecPhase.DecPhaseRT < DecPhaseMaxCutoff & ...
  424. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  426. % Epoch.FreeSubsequentAll(:, :, datasetNo) = ...
  427. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & ...
  428. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  429. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  430. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  431. %
  432. % Epoch.FreeSubsequentFast(:, :, datasetNo) = ...
  433. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & ...
  434. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  435. % TC_Resp_CoMPhase.CoMPhaseRT <= CoMPhaseFreeRT_bins(1) & ...
  436. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  437. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  438. %
  439. % Epoch.FreeSubsequentMedium(:, :, datasetNo) = ...
  440. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & ...
  441. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  442. % TC_Resp_CoMPhase.CoMPhaseRT > CoMPhaseFreeRT_bins(1) & ...
  443. % TC_Resp_CoMPhase.CoMPhaseRT <= CoMPhaseFreeRT_bins(2) & ...
  444. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  445. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  446. %
  447. % Epoch.FreeSubsequentSlow(:, :, datasetNo) = ...
  448. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & ...
  449. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  450. % TC_Resp_CoMPhase.CoMPhaseRT > CoMPhaseFreeRT_bins(2) & ...
  451. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  452. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  453. %
  454. % Epoch.FreeStayed(:, :, datasetNo) = ...
  455. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & ...
  456. % TC_Resp_CoMPhase.SwitchStay_Numeric == 2 & ...
  457. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  458. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  459. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  460. %
  461. % Epoch.FreeSwitched(:, :, datasetNo) = ...
  462. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'free') & ...
  463. % TC_Resp_CoMPhase.SwitchStay_Numeric == 1 & ...
  464. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  465. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  466. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  468. % Forced Trials
  469. Epoch.ForcedStandaloneAll(:, :, datasetNo) = ... % Forced Decision Phase
  470. nanmean(MuBeta_Resp{1,1}(:, :, strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'forced') & ...
  471. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  472. TC_Resp_DecPhase.DecPhaseRT > 0.2 & ...
  473. TC_Resp_DecPhase.DecPhaseRT < DecPhaseMaxCutoff & ...
  474. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  476. Epoch.ForcedStandaloneFast(:, :, datasetNo) = ...
  477. nanmean(MuBeta_Resp{1,1}(:, :, ...
  478. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'forced') & ...
  479. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  480. TC_Resp_DecPhase.DecPhaseRT > 0.2 & ...
  481. TC_Resp_DecPhase.DecPhaseRT <= DecPhaseForcedRT_bins(1) & ...
  482. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  484. Epoch.ForcedStandaloneMedium(:, :, datasetNo) = ...
  485. nanmean(MuBeta_Resp{1,1}(:, :, ...
  486. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'forced') & ...
  487. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  488. TC_Resp_DecPhase.DecPhaseRT > 0.2 & ...
  489. TC_Resp_DecPhase.DecPhaseRT >= DecPhaseForcedRT_bins(1) & ...
  490. TC_Resp_DecPhase.DecPhaseRT < DecPhaseForcedRT_bins(2) & ...
  491. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  493. Epoch.ForcedStandaloneSlow(:, :, datasetNo) = ...
  494. nanmean(MuBeta_Resp{1,1}(:, :, ...
  495. strcmp(TC_Resp_DecPhase.DecPhaseCondition, 'forced') & ...
  496. TC_Resp_DecPhase.ChoiceMade == 1 & ...
  497. TC_Resp_DecPhase.DecPhaseRT > 0.2 & ...
  498. TC_Resp_DecPhase.DecPhaseRT >= DecPhaseForcedRT_bins(2) & ...
  499. TC_Resp_DecPhase.ChoicePhaseRT < 5), 3);
  501. % Epoch.ForcedSubsequentAll(:, :, datasetNo) = ... % Forced CoM Phase
  502. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & ...
  503. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  504. % TC_Resp_CoMPhase.CoMPhaseRT > 0.2 & ...
  505. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  506. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  507. %
  508. % Epoch.ForcedSubsequentFast(:, :, datasetNo) = ...
  509. % mean(EEG_Data_Resp_CoMPhase(:, :, ...
  510. % strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & ...
  511. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  512. % TC_Resp_CoMPhase.CoMPhaseRT > 0.2 & ...
  513. % TC_Resp_CoMPhase.CoMPhaseRT <= CoMPhaseForcedRT_bins(1) & ...
  514. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  515. %
  516. % Epoch.ForcedSubsequentMedium(:, :, datasetNo) = ...
  517. % mean(EEG_Data_Resp_CoMPhase(:, :, ...
  518. % strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & ...
  519. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  520. % TC_Resp_CoMPhase.CoMPhaseRT > 0.2 & ...
  521. % TC_Resp_CoMPhase.CoMPhaseRT > CoMPhaseForcedRT_bins(1) & ...
  522. % TC_Resp_CoMPhase.CoMPhaseRT <= CoMPhaseForcedRT_bins(2) & ...
  523. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  524. %
  525. % Epoch.ForcedSubsequentSlow(:, :, datasetNo) = ...
  526. % mean(EEG_Data_Resp_CoMPhase(:, :, ...
  527. % strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & ...
  528. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  529. % TC_Resp_CoMPhase.CoMPhaseRT > 0.2 & ...
  530. % TC_Resp_CoMPhase.CoMPhaseRT > CoMPhaseForcedRT_bins(2) & ...
  531. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  532. %
  533. % Epoch.ForcedStayed(:, :, datasetNo) = ... % Forced Stayed
  534. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & ...
  535. % TC_Resp_CoMPhase.SwitchStay_Numeric == 2 & ...
  536. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  537. % TC_Resp_CoMPhase.CoMPhaseRT > 0.2 & ...
  538. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  539. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  540. %
  541. % Epoch.ForcedSwitched(:, :, datasetNo) = ... % Forced Switched
  542. % mean(EEG_Data_Resp_CoMPhase(:, :, strcmp(TC_Resp_CoMPhase.CoMPhaseCondition, 'forced') & ...
  543. % TC_Resp_CoMPhase.SwitchStay_Numeric == 1 & ...
  544. % TC_Resp_CoMPhase.ChoiceMade == 1 & ...
  545. % TC_Resp_CoMPhase.CoMPhaseRT > 0.2 & ...
  546. % TC_Resp_CoMPhase.CoMPhaseRT < CoMPhaseRTMaxCutoff & ...
  547. % TC_Resp_CoMPhase.ChoicePhaseRT < 5), 3);
  550. %% Derive sliding-window slope & amplitude & regress β(t) ~ zlogRT per condition
  552. % ---------------------------------------------------------------------
  553. % 1) Build trial × time waveform from Mu/Beta TF by averaging 8–30 Hz
  554. % Result: mb_trials is [nTrials × nTime], in dB or whatever your TF units are.
  555. % ---------------------------------------------------------------------
  556. mb_trials = squeeze(mean(MuBeta_Resp{1,1}(MB_freq, :, :), 1)).'; % [trials x time]
  557. [nTrials, nTime] = size(mb_trials);
  558. t_ms = timepts_struct.resp(:).'; % time axis (ms), response-locked
  560. % ---------------------------------------------------------------------
  561. % 2) Compute sliding-window slope & window-mean amplitude at each sample
  562. % Using the polyfit version (true OLS slope over each window).
  563. % Returns:
  564. % slope_ps: [nTrials x nTime], NaN at edges
  565. % amp_win : [nTrials x nTime], NaN at edges (mean within window)
  566. % valid_mask: 1 x nTime, logical, where full window is available
  567. % ---------------------------------------------------------------------
  568. [slope_ps, amp_win, valid_mask] = eegca_sliding_slope(mb_trials, fs, win_ms);
  569. t_valid = t_ms(valid_mask);
  571. % ---------------------------------------------------------------------
  572. % 3) Trial-level covariates: RT (z-scored log RT) and condition
  573. % We use log to reduce positive skew, then z-score within participant
  574. % so β reflects change per 1 SD of logRT (participant-normalised).
  575. % ---------------------------------------------------------------------
  576. RT_sec = TC_Resp_DecPhase.DecPhaseRT(:); % seconds
  577. cond_str = string(TC_Resp_DecPhase.DecPhaseCondition(:)); % "free"/"forced"
  579. logRT = log(RT_sec);
  580. % guard against zero/negative RTs (shouldn't happen after 0.2 s cut)
  581. logRT(~isfinite(logRT)) = NaN;
  582. zlogRT = (logRT - nanmean(logRT)) ./ nanstd(logRT);
  584. % ---------------------------------------------------------------------
  585. % 4) OLS per time sample: slope(t) ~ [1 zlogRT], amp(t) ~ [1 zlogRT]
  586. % Done separately for Free and Forced trials within participant.
  587. % We only keep β for zlogRT (2nd column).
  588. % ---------------------------------------------------------------------
  589. conds = ["free","forced"];
  591. beta_slope_free = nan(1,sum(valid_mask));
  592. beta_slope_forced = nan(1,sum(valid_mask));
  593. beta_amp_free = nan(1,sum(valid_mask));
  594. beta_amp_forced = nan(1,sum(valid_mask));
  596. for ci = 1:numel(conds)
  597. c = conds(ci);
  598. keep_tr = cond_str == c & isfinite(zlogRT);
  599. if nnz(keep_tr) < min_trials_for_reg
  600. fprintf(' [%s] too few trials (%d) → skipping β(t) this participant.\n', c, nnz(keep_tr));
  601. continue;
  602. end
  604. X = [ones(nnz(keep_tr),1), zlogRT(keep_tr)]; % design matrix
  606. Y_slope = slope_ps(keep_tr, valid_mask); % slopes, valid time only
  607. Y_amp = amp_win(keep_tr, valid_mask); % window-mean amplitude
  609. b_slope = nan(1,size(Y_slope,2));
  610. b_amp = nan(1,size(Y_amp,2));
  612. for tt = 1:size(Y_slope,2)
  613. ys = Y_slope(:,tt);
  614. ya = Y_amp(:,tt);
  616. oks = isfinite(ys);
  617. oka = isfinite(ya);
  619. if nnz(oks) >= min_obs_for_OLS
  620. betaS = X(oks,:) \ ys(oks); % OLS → β = (X'X)^-1 X'y
  621. b_slope(tt) = betaS(2); % β for zlogRT
  622. end
  623. if nnz(oka) >= min_obs_for_OLS
  624. betaA = X(oka,:) \ ya(oka);
  625. b_amp(tt) = betaA(2); % β for zlogRT
  626. end
  627. end
  629. if c == "free"
  630. beta_slope_free = b_slope;
  631. beta_amp_free = b_amp;
  632. else
  633. beta_slope_forced = b_slope;
  634. beta_amp_forced = b_amp;
  635. end
  636. end
  638. % ---------------------------------------------------------------------
  639. % 5) Stack β(t) rows for this participant (and align time if needed)
  640. % First participant sets the master time vector (t_valid).
  641. % Subsequent participants are truncated to the minimum overlapping
  642. % length to keep alignment perfect for group stats.
  643. % ---------------------------------------------------------------------
  644. if isempty(group_time_valid)
  645. group_time_valid = t_valid;
  646. end
  647. L = min(numel(group_time_valid), numel(t_valid));
  648. if L ~= numel(group_time_valid)
  649. group_time_valid = group_time_valid(1:L);
  650. end
  652. % Truncate participant vectors if their valid time differs
  653. beta_slope_free = beta_slope_free(1:L);
  654. beta_slope_forced = beta_slope_forced(1:L);
  655. beta_amp_free = beta_amp_free(1:L);
  656. beta_amp_forced = beta_amp_forced(1:L);
  658. % Append to group matrices
  659. mb_slope_beta_RT.free = [mb_slope_beta_RT.free; beta_slope_free];
  660. mb_slope_beta_RT.forced = [mb_slope_beta_RT.forced; beta_slope_forced];
  661. mb_amp_beta_RT.free = [mb_amp_beta_RT.free; beta_amp_free];
  662. mb_amp_beta_RT.forced = [mb_amp_beta_RT.forced; beta_amp_forced];
  666. end % of for datasetNo
  669. %% Calculate Group Means and Medians by Tertile
  671. Tertile.GroupMean.DecFreeRT = mean(Tertile.MeanDecFreeRT)*1000;
  672. Tertile.GroupMean.DecFreeFast = mean(Tertile.MeanDecFreeFast)*1000;
  673. Tertile.GroupMean.DecFreeMedium = mean(Tertile.MeanDecFreeMedium)*1000;
  674. Tertile.GroupMean.DecFreeSlow = mean(Tertile.MeanDecFreeSlow)*1000;
  675. Tertile.GroupMean.DecForcedRT = mean(Tertile.MeanDecForcedRT)*1000;
  676. Tertile.GroupMean.DecForcedFast = mean(Tertile.MeanDecForcedFast)*1000;
  677. Tertile.GroupMean.DecForcedMedium = mean(Tertile.MeanDecForcedMedium)*1000;
  678. Tertile.GroupMean.DecForcedSlow = mean(Tertile.MeanDecForcedSlow)*1000;
  680. % Tertile.GroupMean.CoMFreeRT = mean(Tertile.MeanCoMFreeRT)*1000;
  681. % Tertile.GroupMean.CoMFreeSwitch = mean(Tertile.MeanCoMFreeSwitch)*1000;
  682. % Tertile.GroupMean.CoMFreeStay = mean(Tertile.MeanCoMFreeStay)*1000;
  683. % Tertile.GroupMean.CoMFreeFast = mean(Tertile.MeanCoMFreeFast)*1000;
  684. % Tertile.GroupMean.CoMFreeMedium = mean(Tertile.MeanCoMFreeMedium)*1000;
  685. % Tertile.GroupMean.CoMFreeSlow = mean(Tertile.MeanCoMFreeSlow)*1000;
  686. % Tertile.GroupMean.CoMForcedRT = mean(Tertile.MeanCoMForcedRT)*1000;
  687. % Tertile.GroupMean.CoMForcedSwitch = mean(Tertile.MeanCoMForcedSwitch)*1000;
  688. % Tertile.GroupMean.CoMForcedStay = mean(Tertile.MeanCoMForcedStay)*1000;
  689. % Tertile.GroupMean.CoMForcedFast = mean(Tertile.MeanCoMForcedFast)*1000;
  690. % Tertile.GroupMean.CoMForcedMedium = mean(Tertile.MeanCoMForcedMedium)*1000;
  691. % Tertile.GroupMean.CoMForcedSlow = mean(Tertile.MeanCoMForcedSlow)*1000;
  698. %% Save Epochs
  699. %
  700. % save(['EEG Data/Epoched Data/eegca_GROUP_MB'], ...
  701. % 'Epoch', ...
  702. % 'TC_Resp_DecPhase', ...
  703. % 'Tertile', ...
  704. % 'Group', ...
  705. % 'timepts_struct');
  706. % %
  707. % %% Load Epochs
  708. %
  709. % load(['EEG Data/Epoched Data/eegca_GROUP', plottingData]);
  713. %% Create MB Plots
  715. % Define colours for different transparencies of blue
  716. t1_opacity = [0, 0.4470, 0.7410, 1]; % Dark blue with high opacity
  717. t2_opacity = [0.576, 0.439, 0.859, 1]; % Medium blue with medium opacity
  718. t3_opacity = [1, 0.773, 0, 1]; % Light blue with low opacity
  720. blue_t1_opacity = [0, 0.4470, 0.7410, 1]; % Dark blue with high opacity
  721. blue_t2_opacity = [0, 0.4470, 0.7410, 0.7]; % Medium blue with medium opacity
  722. blue_t3_opacity = [0, 0.4470, 0.7410, 0.3]; % Light blue with low opacity
  724. blue_q1_opacity = [0, 0.4470, 0.7410, 1]; % Dark blue with high opacity
  725. blue_q2_opacity = [0, 0.4470, 0.7410, 0.8]; % Medium blue with medium opacity
  726. blue_q3_opacity = [0, 0.4470, 0.7410, 0.6]; % Light blue with low opacity
  727. blue_q4_opacity = [0, 0.4470, 0.7410, 0.4]; % Light blue with low opacity
  729. red_t1_opacity = [1, 0.1, 0, 1]; % Dark blue with high opacity
  730. red_t2_opacity = [1, 0.1, 0, 0.7]; % Medium blue with medium opacity
  731. red_t3_opacity = [1, 0.1, 0, 0.3]; % Light blue with low opacity
  733. red_q1_opacity = [1, 0.1, 0, 1]; % Dark blue with high opacity
  734. red_q2_opacity = [1, 0.1, 0, 0.8]; % Medium blue with medium opacity
  735. red_q3_opacity = [1, 0.1, 0, 0.6]; % Light blue with low opacity
  736. red_q4_opacity = [1, 0.1, 0, 0.4]; % Light blue with low opacity
  738. ERP_smoothing_constant = 1;
  743. %% Run Bayesian and Frequentist T Tests on
  745. % Frequency bands to average over within ROI
  746. MuBeta_Freq_ttests = [8:30];
  748. % Alpha level for shading
  749. shadingAlpha = 0.2;
  750. % Default from other projects = 0.3
  753. % Run Bayesian t tests and get SD estimates for plotting
  754. % ---MB ---
  755. for timePoint = 1:size(Epoch.FreeStandaloneAll, 2)
  757. % Use Bayesian paired-samples t test from MATLAB bayesFactor
  758. % toolbox (https://klabhub.github.io/bayesFactor/#1)
  759. % Toolbox by Bart Krekelberg adapted from code by Rouder
  760. [MB.bf10(timePoint), MB.p(timePoint)] = ttest_bayes_df( ...
  761. mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, timePoint, :), 1), ...
  762. mean(Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, timePoint, :), 1));
  764. % Frequentist paired-samples t tests
  765. [~, ~, ~, temp_tstats] = ttest( ...
  766. mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, timePoint, :), 1), ...
  767. mean(Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, timePoint, :), 1));
  769. MB.diff_SD(timePoint) = temp_tstats.sd;
  771. end % of for timePoint
  773. % Copy Standalone Results into ERP_Results structure
  774. MB_Results.Standalone.bf10 = MB.bf10;
  775. MB_Results.Standalone.differenceWave_SD = MB.diff_SD;
  776. MB_Results.Standalone.channelsUsed = MuBeta_Freq_ttests;
  778. % Prepare data for cluster-based permutation test
  779. MB_freestandalone = squeeze(mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, :, :), 1));
  780. MB_freestandalone = MB_freestandalone'; % Transpose
  782. MB_forcedstandalone = squeeze(mean(Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, :, :), 1));
  783. MB_forcedstandalone = MB_forcedstandalone'; % Transpose
  785. % Run cluster-based permutation test for Standalone Decisions using function from DDTBOX
  786. [MB_Results.Standalone.cluster_permtest_outputs] =...
  787. eegca_multcomp_cluster_permtest(...
  788. MB_freestandalone, ...
  789. MB_forcedstandalone, ...
  790. 'alpha', cluster_permtest_criticalAlpha, ...
  791. 'iterations', cluster_permtest_iterations, ...
  792. 'clusteringalpha', cluster_permtest_clusteringAlpha, ...
  793. 'use_yuen', 0, ...
  794. 'tail', 'both');
  796. % Save Standalone Results from cluster-based permutation test
  797. MB_Results.Standalone.cluster_permtest_criticalAlpha = cluster_permtest_criticalAlpha;
  798. MB_Results.Standalone.cluster_permtest_clusteringAlpha = cluster_permtest_clusteringAlpha;
  799. MB_Results.Standalone.cluster_permtest_iterations = cluster_permtest_iterations;
  802. % Add timepoints information and info about EEG data
  803. MB_Results.Standalone.timepoints = timepts_struct.resp;
  804. MB_Results.Standalone.chanlocs = chanlocs;
  806. % Y Offset for statistical significance markers in plot
  807. sigMarkers_Y_Offset = 3; % Add extra one to account for 1/0 h test coding
  808. % E.g., A value of 2 means sig. timepoints will appear at Y value -1
  812. %% Plot MB and Diffs Below
  814. figure('Name', 'Free vs Forced Initial MB');
  816. subplot(6, 1, 1:3);
  818. plot(timepts_struct.resp, ...
  819. mean(mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, :, :), ...
  820. 1), ...
  821. 3), ...
  822. 'linewidth', 2);
  824. hold on;
  826. plot(timepts_struct.resp, ...
  827. mean(mean(Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, :, :), ...
  828. 1), ...
  829. 3), ...
  830. 'linewidth', 2);
  832. % Plot statistical significance markers from cluster-based permutation test
  833. tempSigMarkers = MB_Results.Standalone.cluster_permtest_outputs.corrected_h;
  834. tempSigMarkers(tempSigMarkers == 0) = nan;
  835. plot(timepts_struct.resp, ...
  836. tempSigMarkers - sigMarkers_Y_Offset, ...
  837. 'linewidth', 2);
  839. xlim([-1000, 100]);
  840. ylim([-2.5, -0.5]);
  841. x_patch = [-130, -70, -70, -130];
  842. y_patch = [-14, -14, 6, 6];
  843. fill(x_patch, y_patch, [0.8 0.8 0.8], 'EdgeColor', 'none', 'FaceAlpha', 0.4);
  844. xline(0, 'k', 'LineWidth', 1.5);
  845. line([timepts_struct.resp(1), timepts_struct.resp(end)], [0, 0], 'color', 'k');
  846. ylabel('dB');
  847. %legend({'Free', 'Forced'}, 'location', 'southwest');
  848. title('Motor Preparation Mu/Beta (8-30Hz)');
  850. % Mean Difference and SEMs
  851. subplot(6, 1, 4);
  853. plot(timepts_struct.resp, ...
  854. mean(mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, :, :) - Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, :, :), ...
  855. 1), ...
  856. 3), ...
  857. 'linewidth', 2);
  859. hold on;
  860. xlim([-1000, 100]);
  861. line([timepts_struct.resp(1), timepts_struct.resp(end)], [0, 0], 'color', 'k');
  863. % Make shaded standard error regions (using functions from DDTBOX v1.0.5)
  865. % First, calculate mean values and standard errors
  867. avg_y_vals = mean(mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, :, :) - Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, :, :), 1), 3);
  869. % Calculate standard errors of the mean
  870. standard_error_vals = ...
  871. std( ...
  872. squeeze( ...
  873. mean( ...
  874. Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, :, :) - Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, :, :) ...
  875. , 1) ...
  876. )' ...
  877. ) ...
  878. ./ sqrt(size(Epoch.FreeStandaloneAll, 3));
  880. % Create shaded error regions
  881. [patch_x, patch_y] = dd_make_error_bar_object(timepts_struct.resp, ...
  882. avg_y_vals, ...
  883. standard_error_vals);
  885. SE_shading = patch('xdata', patch_x, 'ydata', patch_y);
  887. SE_shading.FaceAlpha = shadingAlpha; % Transparency
  888. % SE_shading.FaceColor = spectra_plots_colour_map(1, :);
  889. SE_shading.EdgeColor = 'none';
  891. xlim([-1000, 100]);
  892. ylim([-0.5, 0.5]);
  893. ylabel('Diff [µV]');
  896. % Effect Sizes (Cohen's d)
  897. subplot(6, 1, 5);
  898. plot(timepts_struct.resp, ...
  899. mean(mean(Epoch.FreeStandaloneAll(MuBeta_Freq_ttests, :, :) - Epoch.ForcedStandaloneAll(MuBeta_Freq_ttests, :, :), ...
  900. 1), ...
  901. 3) ...
  902. ./ MB_Results.Standalone.differenceWave_SD, ...
  903. 'linewidth', 2);
  905. ylabel('Cohen''s d');
  906. ylim([-1, 0.5]);
  907. xlim([-1000, 100]);
  908. line([timepts_struct.resp(1), timepts_struct.resp(end)], [0, 0], 'color', 'k');
  911. % Bayes Factors BF10
  912. subplot(6, 1, 6);
  913. semilogy(timepts_struct.resp, ...
  914. MB_Results.Standalone.bf10, ...
  915. 'linewidth', 2);
  916. ylim([0, 1500]);
  917. xlim([-1000, 100]);
  919. line([timepts_struct.resp(1), timepts_struct.resp(end)], [1, 1], 'color', 'k');
  920. ylabel('BF10');
  921. yticks([0, 1, 100, 1000]);
  923. % Save plot
  924. %saveas(gcf, 'ERP Plots/no RIDE Mass Uni MB Resp.png');
  928. % subplot(7, 1, 7);
  929. % plot(timepts_struct.resp, ...
  930. % ROI.p, ...
  931. % 'linewidth', 2);
  932. % ylim([0, 1]);
  933. %
  934. % line([timepts_struct.resp(1), timepts_struct.resp(end)], [0.05, 0.05], 'color', 'k');
  935. % ylabel('p');
  939. %% Cluster-based permutation tests for Slope and Amplitude Regressions
  940. % Assumptions:
  941. % - mb_slope_beta_RT.free / .forced: [nSubjects x nTimeValid]
  942. % - mb_amp_beta_RT.free / .forced: [nSubjects x nTimeValid] if you used the SAME window;
  943. % otherwise mask amplitude to the same valid region first.
  944. % - timepts_struct.resp: 1 x nTime (ms) for the full epoch
  945. % - time_valid: 1 x nTimeValid (ms) for the valid interior samples
  946. % - eegca_multcomp_cluster_permtest: your cluster test util; accepts (data, null, ...)
  947. % - ttest_bayes_df: your helper returning (bf10, p)
  949. measures = { ...
  950. struct('name','Slope','data', mb_slope_beta_RT,'time',group_time_valid), ...
  951. struct('name','Amp', 'data', mb_amp_beta_RT, 'time',group_time_valid) ...
  952. };
  953. conds_out = {'Free','Forced'};
  954. MB_RT_Results = struct();
  956. for m = 1:numel(measures)
  957. meas_name = measures{m}.name;
  958. meas_data = measures{m}.data;
  959. meas_time = measures{m}.time;
  961. for ci = 1:numel(conds_out)
  962. cname = lower(conds_out{ci});
  963. if ~isfield(meas_data, cname) || isempty(meas_data.(cname))
  964. warning('%s/%s: beta matrix missing or empty.', meas_name, cname);
  965. continue
  966. end
  968. betas = meas_data.(cname);
  969. keep_subj = any(isfinite(betas),2);
  970. betas = betas(keep_subj,:);
  972. keep_time = any(isfinite(betas),1);
  973. betas = betas(:,keep_time);
  974. time_use = meas_time(keep_time);
  976. if isempty(betas) || size(betas,1) < 5
  977. warning('%s/%s: not enough usable data after NaN filtering.', meas_name, cname);
  978. continue
  979. end
  981. MB_RT_Results.(meas_name).(conds_out{ci}).cluster_permtest_outputs = ...
  982. eegca_multcomp_cluster_permtest( ...
  983. betas, zeros(size(betas)), ...
  984. 'alpha', cluster_permtest_criticalAlpha, ...
  985. 'iterations', cluster_permtest_iterations, ...
  986. 'clusteringalpha', cluster_permtest_clusteringAlpha, ...
  987. 'use_yuen', 0, ...
  988. 'tail', 'both');
  990. MB_RT_Results.(meas_name).(conds_out{ci}).time = time_use;
  991. MB_RT_Results.(meas_name).(conds_out{ci}).nSubjects = size(betas,1);
  993. Tuse = size(betas,2);
  994. bf10 = nan(1,Tuse); pval = nan(1,Tuse); sd_b = nan(1,Tuse);
  995. for tt = 1:Tuse
  996. x = betas(:,tt); x = x(isfinite(x));
  997. if numel(x) < 5, continue, end
  998. [bf10(tt), pval(tt)] = ttest_bayes_df(x, 0);
  999. [~, ~, ~, stats] = ttest(x, 0);
  1000. if isfield(stats,'sd'), sd_b(tt) = stats.sd; end
  1001. end
  1002. MB_RT_Results.(meas_name).(conds_out{ci}).bf10 = bf10;
  1003. MB_RT_Results.(meas_name).(conds_out{ci}).p = pval;
  1004. MB_RT_Results.(meas_name).(conds_out{ci}).diff_SD = sd_b;
  1005. end
  1006. end
  1009. %% -------- TOP: FREE --------
  1010. figure;
  1011. subplot(6,1,1:2);
  1012. hold on;
  1014. % Define colours and labels for different tertiles
  1015. colors = {t1_opacity, t2_opacity, t3_opacity};
  1016. labels = {'Fast', 'Medium', 'Slow'};
  1017. dataFieldsFree = {'FreeStandaloneFast', 'FreeStandaloneMedium', 'FreeStandaloneSlow'};
  1019. for i = 1:3
  1020. % Extract the current tertile data for Free condition
  1021. data = Epoch.(dataFieldsFree{i})(MB_freq, :, :);
  1023. % Calculate mean and SEM for the current tertile
  1024. avg_y_vals = mean(mean(data, 1), 3);
  1025. standard_error_vals = std(squeeze(mean(data, 1))') ./ sqrt(size(data, 3));
  1027. % Plot mean line
  1028. plot(timepts_struct.resp, smoothdata(avg_y_vals, 'gaussian', ERP_smoothing_constant), ...
  1029. 'Color', colors{i}(1:4), 'LineWidth', 2, 'DisplayName', labels{i});
  1031. % Shaded error
  1032. [patch_x, patch_y] = dd_make_error_bar_object(timepts_struct.resp, avg_y_vals, standard_error_vals);
  1033. SE_shading = patch('xdata', patch_x, 'ydata', patch_y);
  1034. SE_shading.FaceAlpha = 0.2;
  1035. SE_shading.FaceColor = colors{i}(1:3);
  1036. SE_shading.EdgeColor = 'none';
  1037. end
  1039. % ----- Cluster permutation markers -----
  1040. % Slope
  1041. sigMaskSlopeFree = MB_RT_Results.Slope.Free.cluster_permtest_outputs.corrected_h;
  1042. sigMaskSlopeFree(sigMaskSlopeFree==0) = NaN;
  1043. y_mark_slope = -2.4;
  1044. plot(MB_RT_Results.Slope.Free.time, sigMaskSlopeFree * y_mark_slope, 'k-', 'LineWidth', 2);
  1046. % Amplitude
  1047. sigMaskAmpFree = MB_RT_Results.Amp.Free.cluster_permtest_outputs.corrected_h;
  1048. sigMaskAmpFree(sigMaskAmpFree==0) = NaN;
  1049. y_mark_amp = -2.2; % offset so lines don't overlap
  1050. plot(MB_RT_Results.Amp.Free.time, sigMaskAmpFree * y_mark_amp, 'r-', 'LineWidth', 2);
  1052. xlim([-1000, 50]);
  1053. ylim([-2.5, 0]);
  1054. x_patch = [-130, -70, -70, -130];
  1055. x2_patch = [-300, -70, -70, -300];
  1056. y_patch = [-2.5, -2.5, 0, 0];
  1057. fill(x_patch, y_patch, [0.8 0.8 0.8], 'EdgeColor', 'none', 'FaceAlpha', 0.4);
  1058. fill(x2_patch, y_patch, [0.8 0.8 0.8], 'EdgeColor', 'none', 'FaceAlpha', 0.2);
  1059. xline(0, 'k', 'LineWidth', 1.5);
  1060. ylabel('µV/m^2');
  1061. title('MB - Voluntary Decisions');
  1063. % BF
  1064. subplot(6,1,3);
  1066. % Slope BF10
  1067. semilogy(MB_RT_Results.Slope.Free.time, MB_RT_Results.Slope.Free.bf10, 'k-', 'LineWidth', 1.8, 'DisplayName', 'Free Slope BF_{10}');
  1068. hold on;
  1070. % Amplitude BF10
  1071. semilogy(MB_RT_Results.Amp.Free.time, MB_RT_Results.Amp.Free.bf10, 'r-', 'LineWidth', 1.8, 'DisplayName', 'Free Amp BF_{10}');
  1073. % Reference lines
  1074. xline(0, 'k', 'LineWidth', 1.5);
  1075. yline(1, 'k', 'LineWidth', 1); % BF=1 reference
  1076. % yline(3, 'k--', 'LineWidth', 1); % optional: "moderate evidence" threshold
  1078. xlim([-1000, 50]);
  1079. ylim([0, 150])
  1080. yticks([0,1, 100]);
  1081. ylabel('BF_{10}');
  1082. xlabel('Time (ms)');
  1083. title('BF (Slope & Amplitude)');
  1085. % -------- MIDDLE: FORCED --------
  1086. subplot(6,1,4:5);
  1087. hold on;
  1089. dataFieldsForced = {'ForcedStandaloneFast', 'ForcedStandaloneMedium', 'ForcedStandaloneSlow'};
  1091. for i = 1:3
  1092. data = Epoch.(dataFieldsForced{i})(MB_freq, :, :);
  1093. avg_y_vals = mean(mean(data, 1), 3);
  1094. standard_error_vals = std(squeeze(mean(data, 1))') ./ sqrt(size(data, 3));
  1096. plot(timepts_struct.resp, smoothdata(avg_y_vals, 'gaussian', ERP_smoothing_constant), ...
  1097. 'Color', colors{i}(1:4), 'LineWidth', 2, 'DisplayName', labels{i});
  1099. [patch_x, patch_y] = dd_make_error_bar_object(timepts_struct.resp, avg_y_vals, standard_error_vals);
  1100. SE_shading = patch('xdata', patch_x, 'ydata', patch_y);
  1101. SE_shading.FaceAlpha = 0.2;
  1102. SE_shading.FaceColor = colors{i}(1:3);
  1103. SE_shading.EdgeColor = 'none';
  1104. end
  1106. % Slope
  1107. sigMaskSlopeForced = MB_RT_Results.Slope.Forced.cluster_permtest_outputs.corrected_h;
  1108. sigMaskSlopeForced(sigMaskSlopeForced==0) = NaN;
  1109. plot(MB_RT_Results.Slope.Forced.time, sigMaskSlopeForced * y_mark_slope, 'k-', 'LineWidth', 2);
  1111. % Amplitude
  1112. sigMaskAmpForced = MB_RT_Results.Amp.Forced.cluster_permtest_outputs.corrected_h;
  1113. sigMaskAmpForced(sigMaskAmpForced==0) = NaN;
  1114. plot(MB_RT_Results.Amp.Forced.time, sigMaskAmpForced * y_mark_amp, 'r-', 'LineWidth', 2);
  1116. xlim([-1000, 50]);
  1117. ylim([-2.5, 0]);
  1118. x_patch = [-130, -70, -70, -130];
  1119. x2_patch = [-300, -70, -70, -300];
  1120. y_patch = [-3, -3, 0, 0];
  1121. fill(x_patch, y_patch, [0.8 0.8 0.8], 'EdgeColor', 'none', 'FaceAlpha', 0.4);
  1122. fill(x2_patch, y_patch, [0.8 0.8 0.8], 'EdgeColor', 'none', 'FaceAlpha', 0.2);
  1123. xline(0, 'k', 'LineWidth', 1.5);
  1124. ylabel('µV/m^2');
  1125. title('MB - Forced Decisions');
  1126. hold off;
  1128. % -------- BOTTOM: BAYES FACTORS (correct .bf10 fields) --------
  1129. subplot(6,1,6);
  1132. % Slope BF10
  1133. semilogy(MB_RT_Results.Slope.Forced.time, MB_RT_Results.Slope.Forced.bf10, 'k-', 'LineWidth', 1.8, 'DisplayName', 'Forced Slope BF_{10}');
  1134. hold on;
  1135. % Amplitude BF10
  1136. semilogy(MB_RT_Results.Amp.Forced.time, MB_RT_Results.Amp.Forced.bf10, 'r-', 'LineWidth', 1.8, 'DisplayName', 'Forced Amp BF_{10}');
  1138. % Reference lines
  1139. xline(0, 'k', 'LineWidth', 1.5);
  1140. yline(1, 'k', 'LineWidth', 1); % BF=1 reference
  1142. xlim([-1000, 50]);
  1143. ylim([0, 110000]);
  1144. yticks([0,1, 150, 10^5]);
  1145. ylabel('BF_{10}');
  1146. xlabel('Time (ms)');
  1147. title('BF (Slope & Amplitude)');

eegca_massuni_MB.m, no license · at the source

Overview

Authors: Lauren C. Fong1, Paul M. Garrett1, Philip L. Smith1, Robert Hester1, Stefan Bode2, Daniel Feuerriegel1
ORCID iDs: Lauren C. Fong, Paul M. Garrett, Philip L. Smith, Robert Hester, Stefan Bode, Daniel Feuerriegel
  1. Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
  2. Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
Institutions: The University of Melbourne (Australia); New York University Abu Dhabi (United Arab Emirates)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1184
Dates: received 3 November 2025; accepted 27 February 2026; published online 30 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1184 · PMID 41924032 · PMCID PMC13037662 · OpenAlex W7134814849
Open access: diamond, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Spectral & time-frequency, Evoked potentials, Physiology & signal measures, Machine learning
Keywords: CPP, RP, mu/beta, evidence accumulation, motor preparation, voluntary decisions
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Department of Education and Training | Australian Research Council (ARC) (DE220101508); Melbourne Research Scholarship
Citations: cited by 2 papers (Europe PMC); 97 references in the paper

Abstract

Voluntary decisions have previously been described by where they arise in the brain and how actions corresponding to one’s choice are prepared. However, the processes by which these internally guided decisions are formed and translated into action remain unclear. We tested whether neural signatures of evidence accumulation and motor preparation—established in perceptual decision-making—are also observed during voluntary decisions. Forty-nine adults made voluntary (free choice between two options) and forced (single specified choice) decisions while electroencephalography was recorded. To isolate decision formation from action selection, all responses were made with a right-handed keypress. After applying signal deconvolution and current source density transformations, we examined three signals: the centro-parietal positivity (CPP), Mu/Beta (8–30 Hz) amplitudes, and a left-hemisphere readiness potential contralateral to the response hand. For both voluntary and forced decisions, CPP and Mu/Beta amplitudes showed hallmark accumulation-to-bound dynamics—faster responses had steeper ramping signals that converged to stereotyped pre-response amplitude levels. The left-hemisphere readiness potential likewise showed convergence to a pre-response level but with less reliable evidence for response time (RT)-dependent slope scaling, resembling a late, effector-specific motor gate rather than an evolving decision variable. Waveform amplitudes across the time course of these signals also did not differ reliably between voluntary and forced decisions. Our results indicate that, like perceptual decisions, voluntary choices are also formed by a graded accumulation process that culminates in a motor action, with the CPP indexing an evolving decision variable and Mu/Beta indexing effector-specific motor readiness.

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 16 matches between paragraphs and lines of code.

OSF t7z4h

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (53), R (1)
Size: 129 files, 54 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (17 files), EEGLAB (11 files), Statistics and Machine Learning Toolbox (7 files), afex (1 file), broom (1 file), emmeans (1 file), lme4 (1 file), lmerTest (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
54 files
At the source: osf.io/t7z4h

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

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;
  • 54 scripts, each with its path and the digest of its content;
  • 16 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

No dataset and no data link were found in the paper.

Data and Code Availability

All code and data used for the experiment and analyses described in this manuscript are available at http://osf.io/t7z4h.

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

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 6 keywords, 2 funders, 96 references.

Cite

This paper

Fong, L. C., Garrett, P. M., Smith, P. L., Hester, R., Bode, S., & Feuerriegel, D. (2026). Tracing the neural trajectories of evidence accumulation and motor preparation processes during voluntary decisions. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1184. https://doi.org/10.1162/imag.a.1184

BibTeX

@article{fong2026tracing,
author = {Fong, Lauren C. and Garrett, Paul M. and Smith, Philip L. and Hester, Robert and Bode, Stefan and Feuerriegel, Daniel},
title = {{Tracing the neural trajectories of evidence accumulation and motor preparation processes during voluntary decisions}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1184},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1184},
url = {https://doi.org/10.1162/imag.a.1184},
pmid = {41924032},
pmcid = {PMC13037662}
}

RIS

TY - JOUR
AU - Fong, Lauren C.
AU - Garrett, Paul M.
AU - Smith, Philip L.
AU - Hester, Robert
AU - Bode, Stefan
AU - Feuerriegel, Daniel
TI - Tracing the neural trajectories of evidence accumulation and motor preparation processes during voluntary decisions
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/03/30
VL - 4
SP - IMAG.a.1184
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1184
UR - https://doi.org/10.1162/imag.a.1184
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1184",
"type": "article-journal",
"title": "Tracing the neural trajectories of evidence accumulation and motor preparation processes during voluntary decisions",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Fong",
"given": "Lauren C."
},
{
"family": "Garrett",
"given": "Paul M."
},
{
"family": "Smith",
"given": "Philip L."
},
{
"family": "Hester",
"given": "Robert"
},
{
"family": "Bode",
"given": "Stefan"
},
{
"family": "Feuerriegel",
"given": "Daniel"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1184",
"DOI": "10.1162/imag.a.1184",
"PMID": "41924032",
"PMCID": "PMC13037662",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1184",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
30
]
]
}
}

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