add EEG data analysis file

EEG4_Step04_ExploratoryERPPermutation.m

@@ -0,0 +1,171 @@
+%% B_analysis 
+% Perform lmeEEG on simulated EEG data 
+clear all;clc
+load('E:\Exp02\EEGCode\Exp02_chanloc_.mat');
+load('E:\Exp02\EEGCode\Exp02_time_.mat');
+load('E:\Exp02\EEGCode\Exp02_sEEG_1.mat');
+DesignM = readtable('E:\Exp02\EEGCode\Exp02_DesignM.csv');
+DesignM.Properties.VariableNames = {'SubjID', 'Markers'};
+DesignM.Properties.VariableNames{'Markers'} = 'Marker3';
+unique_SubjID = unique(DesignM.SubjID);
+Table = readtable('SemCatEStork.csv');
+Table.Properties.VariableNames{'x___Target'} = 'Target';
+Table2 = readtable('FreqDistra.csv');
+Table = innerjoin(Table, Table2,'Keys', {'Distractor'});
+
+repeatedTable = repmat(Table, 36, 1);
+SubjList = [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,...
+    27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, ...
+    45, 46];
+repeatedTable.SubjID = reshape(repelem(SubjList, 102),  3672, 1);
+
+mergedTable = innerjoin(DesignM, repeatedTable,'Keys', {'SubjID', 'Marker3'});
+
+
+
+Order = readtable('Exp02_SeqSI.csv');
+repeatedOrder = innerjoin(mergedTable, Order, 'Keys', {'SubjID', 'Marker3'});
+
+
+rowsToRemove = repeatedOrder.SubjID == 12 | repeatedOrder.SubjID == 44 |...
+    repeatedOrder.ExpTrialList == 1 | repeatedOrder.ExpTrialList == 2 |...
+    repeatedOrder.ExpTrialList == 3 | repeatedOrder.ExpTrialList == 4 |...
+    repeatedOrder.ExpTrialList == 74 | repeatedOrder.ExpTrialList == 75 |...
+    repeatedOrder.ExpTrialList == 76 | repeatedOrder.ExpTrialList == 77;
+
+repeatedOrder(rowsToRemove, :) = [];
+sEEG = sEEG(:, :, ~rowsToRemove);
+
+channelinfo = EEG.chanlocs;
+
+clear DesignM EEG mergedTable Order repeatedTable rowsToRemove ...
+    SubjList Table unique_SubjID
+save('well_preparedData.mat');
+disp('Data is well-organized!!!!!!!!!!');
+%% STEP 1
+% Conduct mixed models on each channel/timepoint combination.
+ID = nominal(repeatedOrder.SubjID); Item=nominal(repeatedOrder.Target); 
+%CON = categorical(repeatedOrder.Condition);
+JSD = categorical(repeatedOrder.JSD);
+Classifier = categorical(repeatedOrder.ClassifierCongruency);
+Freq = log(repeatedOrder.Frequency);
+Stroke = (repeatedOrder.NumbersofStorks - mean(repeatedOrder.NumbersofStorks))/std(repeatedOrder.NumbersofStorks);
+CongruencySemanticCategories = categorical(repeatedOrder.CongruencySemanticCategories);
+mEEG = nan(size(sEEG));
+
+for ch = 1:size(sEEG,1)
+    for tpoint = 1:size(sEEG,2)
+        EEG = double(squeeze(sEEG(ch,tpoint,:)));
+        EEG = table(EEG, CongruencySemanticCategories,Stroke, Freq, JSD, Classifier, ID, Item);
+        m = fitlme(EEG,'EEG~CongruencySemanticCategories + Stroke + Freq + JSD + Classifier + +(1|ID)+(1|Item)');
+
+        mEEG(ch,tpoint,:) = fitted(m,'Conditional',0)+residuals(m);
+        Coeff = fixedEffects(m);
+        Coeff_1 = Coeff(2:4); X = designMatrix(m); X_1 = X(:, 2:4);
+        coeff_CongruencySemanticCategories_1 = Coeff(strcmp(m.CoefficientNames, 'CongruencySemanticCategories_1'));
+        Coeff_Stroke = Coeff(strcmp(m.CoefficientNames, 'Stroke'));
+        coeff_Freq = Coeff(strcmp(m.CoefficientNames, 'Freq'));
+        Coeff_Intercept = Coeff(strcmp(m.CoefficientNames, '(Intercept)'));
+
+%         m = fitlme(EEG,'EEG~Freq+Stroke+Animacy+Classifier+Animacy:Classifier+(1|ID)+(1|Item)');
+        mEEG(ch,tpoint,:) = fitted(m,'Conditional',0)+residuals(m) - X_1 * Coeff_1;
+
+    end
+end
+
+% Extract design matrix X
+EEG = double(squeeze(sEEG(1,1,:)));
+% EEG = table(EEG,Freq,Stroke,Animacy,Classifier,ID,Item);
+EEG = table(EEG,Classifier,ID,Item);
+m = fitlme(EEG,'EEG~Classifier+(1|ID)+(1|Item)');
+% m = fitlme(EEG,'EEG~Freq+Stroke+Animacy+Classifier+Animacy:Classifier+(1|ID)+(1|Item)');
+
+X = designMatrix(m);
+clear EEG EEGall
+save('./Output/marginalResult_Classifier.mat');
+clear EEG repeatedOrder tpoint;
+disp('the marginalization stage is done!!!!!!!!!!');
+%% Step 2
+% Perform mass univariate linear regressions on ???marginal??? EEG data.
+%f_obs = nan(size(mEEG,1),size(mEEG,2));
+
+t_obs = nan(size(mEEG,1),size(mEEG,2),size(X,2));
+beta_obs = nan(size(mEEG,1),size(mEEG,2),size(X,2));
+se_obs = nan(size(mEEG,1),size(mEEG,2),size(X,2));
+for ch = 1:size(mEEG,1)
+    parfor tpoint = 1:size(mEEG,2)
+        EEG = squeeze(mEEG(ch,tpoint,:));
+        [t_obs(ch,tpoint,:), beta_obs(ch,tpoint,:), se_obs(ch,tpoint,:)]=lmeEEG_regress(EEG,X);
+        mdl_summary = anova(fitlm(X, EEG), 'summary');
+        f_obs(ch,tpoint, :)= mdl_summary.F(2);
+    end
+end
+save('./Output/observed_stage_classifier.mat');
+disp('the observation stage is done!!!!!!!!!!');
+%% Step 3 permutations
+% Perform permutation testing ...
+nperms=999;
+num_rows = size(X,1);
+% rperms = randperm(num_rows);
+
+% [rperms] = lmeEEG_permutations4(nperms, Animacy, Classifier, ID, Item);
+f_perms = nan(nperms,size(mEEG,1),size(mEEG,2));
+t_perms = nan(nperms,size(mEEG,1),size(mEEG,2),size(X,2));
+beta_perms = nan(nperms,size(mEEG,1),size(mEEG,2),size(X,2));
+se_perms = nan(nperms,size(mEEG,1),size(mEEG,2),size(X,2));
+perm_t=nan(nperms,size(mEEG,1),size(mEEG,2));
+for p =1:nperms
+    XX = X(randperm(num_rows),:);
+%     XX = X(rperms(:,p),:);
+    for ch = 1:size(mEEG,1)
+        parfor tpoint = 1:size(mEEG,2)
+            EEG = squeeze(mEEG(ch,tpoint,:));
+            tic
+            [t_perms(p,ch,tpoint,:), beta_perms(p,ch,tpoint,:),se_perms(p,ch,tpoint,:)]=lmeEEG_regress(EEG,XX);
+            perm_t(p,ch,tpoint)=toc;
+            mdl2_summary = anova(fitlm(XX, EEG),'summary');
+            f_perms(p,ch,tpoint) = mdl2_summary.F(2);
+        end
+    end
+end
+
+save('./Output/permutate_stage_classifier.mat');
+
+disp('the permutation stage is done!!!!!!!!!!');
+%% ... and apply TFCE.
+X = designMatrix(m);
+for i = 2:size(X,2)
+    if ndims(t_obs) == 3
+        Results.(matlab.lang.makeValidName(m.CoefficientNames{i})) = lmeEEG_TFCE(squeeze(t_obs(:,:,i)),squeeze(t_perms(:,:,:,i)),channelinfo,[0.66 2]);
+    elseif ndims(t_obs) == 4
+        Results.(matlab.lang.makeValidName(m.CoefficientNames{i})) = lmeEEG_TFCE(squeeze(t_obs(:,:,:,i)),squeeze(t_perms(:,:,:,:,i)),channelinfo,[0.66 2]);
+    end
+end
+
+Results2 = lmeEEG_TFCE(squeeze(f_obs(:,:)),squeeze(f_perms(:,:,:)),channelinfo,[0.5 1]);
+save('./Output/tfce_stage_classifierr.mat');
+%%
+mT = Results2.Obs;
+mT2 = mT;
+mT(not(Results2.Mask))=0;
+tick_labels = reshape({channelinfo.labels}, 32, 1);
+
+
+figure,
+imagesc(mT)
+xlim([0 230])
+set(gca,'ytick',1:32,'FontSize',15,'FontName','Arial');
+set(gca,'TickLength',[0 0]);
+set(gca,'XTick',linspace(1,230,10),'XTickLabel',-200:100:700,'FontSize',15,'FontName','Arial');
+yticklabels(tick_labels);
+hc=colorbar;
+xlabel("Time (ms)","FontWeight","bold","FontSize",30, "FontName","Arial");
+ylabel(hc,'f-value','FontWeight','bold','FontSize',30,'FontName','Arial');
+
+cmap2=cmap; cmap2(129,:)=[.8 .8 .8];
+set(gca,'clim',[-20 20],'colormap',cmap2)
+
+set(gca,'color','none')
+
+a = get(hc,'YTickLabel')
+set(hc, 'colormap', cmap,'YTickLabel',a,'FontSize',8,'FontName','Arial');

EEG_Step01_PreproEEG_Batch.m

@@ -0,0 +1,23 @@
+clear all; clc; close all
+SavePath1 = 'E:\Exp02\DATA\EEG_clean01\';%place of the data
+FilePath='E:\Exp02\DATA\EEG\';
+mkdir(SavePath1);
+sub_name = data_load(FilePath, 'bdf');
+%% STEP01 PREPROCESSING: get the dataset into the memory and run ica
+for sub = 1:length(sub_name)% a loop for all data\
+    filename = [FilePath,sub_name{sub}];
+    EEG = pop_biosig(filename);
+    EEG = pop_chanedit(EEG, 'lookup','E:\\Exp02\\EEGCode\\standard_1020.elc');
+%     EEG = pop_reref( EEG, [37 38] );
+    EEG = pop_eegfiltnew(EEG, 0.1,30,333792,0,[],0);
+    EEG = pop_eegfiltnew(EEG, 48,52,31690,1,[],0);
+    EEG = pop_resample( EEG, 256);
+    EEG = pop_epoch( EEG, {  '11'  '12'  '13'  '14'  '15'  '16'  '17'  '18'  '19'  '20'  '21'  '22'  '23'  '24'  '25'  '26'  '27'  '28'  '29'  '30'  '31'  '32'  '33'  '34'  '35'  '36'  '37'  '38'  '39'  '40'  '41'  '42'  '43'  '44'  '45'  '46'  '47'  '48'  '49'  '50'  '51'  '52'  '53'  '54'  '55'  '56'  '57'  '58'  '59'  '60'  '61'  '62'  '63'  '64'  '65'  '66'  '67'  '68'  '69'  '70'  '71'  '72'  '73'  '74'  '75'  '76'  '77'  '78'  '79'  '80'  '81'  '82'  '83'  '84'  '85'  '86'  '87'  '88'  '89'  '90'  '91'  '92'  '93'  '94'  '95'  '96'  '97'  '98'  '99'  '100'  '101'  '102'  '103'  '104'  '105'  '106'  '107'  '108'  '109'  '110'  '111'  '112'  }, [-0.2  0.7], 'newname', 'BDF file epochs', 'epochinfo', 'yes');
+
+    EEG = pop_rmbase( EEG, [-200     0]);  
+    EEG = pop_select( EEG,'nochannel',{'EXG1' 'EXG2' 'EXG3' 'EXG4' 'EXG7' 'EXG8' 'GSR1' 'GSR2' 'Erg1' 'Erg2' 'Resp' 'Plet' 'Temp'});
+
+%     EEG = pop_runica(EEG, 'extended',1,'interupt','on');
+    EEG = pop_saveset( EEG, 'filename',sub_name{sub},'filepath',SavePath1);
+    fprintf('*****the subject %d has been done',sub);
+end

EEG_Step02_PreproEEG.m

@@ -0,0 +1,14 @@
+clear all; clc; close all;
+FilePath = 'E:\Exp02\DATA\EEG_clean02\';%place of the data
+SavePath1='E:\Exp02\DATA\EEG_clean03\';
+mkdir(SavePath1);
+sub_name = data_load(FilePath, 'set');
+for sub = 1:length(sub_name)% a loop for all data\
+    filename = [FilePath,sub_name{sub}];
+    EEG = pop_loadset(filename);
+    EEG = pop_reref( EEG, [33 34] );
+    EEG = pop_select( EEG,'nochannel',{'EXG5' 'EXG6'});
+    EEG = pop_runica(EEG, 'extended',1,'interupt','on');
+    EEG = pop_saveset( EEG, 'filename',sub_name{sub},'filepath',SavePath1);
+    fprintf('*****the subject %d has been done',sub);
+end

EEG_Step03_PreproRemovthreshold.m

@@ -0,0 +1,55 @@
+clear all; clc; close all;
+FilePath = 'E:\Exp02\DATA\EEG_clean04\';%place of the data
+SavePath1='E:\Exp02\DATA\EEG_clean05\';
+mkdir(SavePath1);
+AllDATA = [];
+DesignM = [];
+sub_name = data_load(FilePath, 'set');
+n = 1;
+for sub = 1:length(sub_name)% a loop for all data\
+    filename = [FilePath,sub_name{sub}];
+    EEG = pop_loadset(filename);
+    EEG = pop_rmbase( EEG, [-200     0]); 
+    EEG = pop_eegthresh(EEG,1,[1:32] ,-100,100,-0.19922,0.7,0,1);
+    EEG = pop_epoch( EEG, {  '11'  '12'  '13'  '14'  '15'  '16'  '17'  '18'  '19'  '20'  '21'  '22'  '23'  '24'  '25'  '26'  '27'  '28'  '29'  '30'  '31'  '32'  '33'  '34'  '35'  '36'  '37'  '38'  '39'  '40'  '41'  '42'  '43'  '44'  '45'  '46'  '47'  '48'  '49'  '50'  '51'  '52'  '53'  '54'  '55'  '56'  '57'  '58'  '59'  '60'  '61'  '62'  '63'  '64'  '65'  '66'  '67'  '68'  '69'  '70'  '71'  '72'  '73'  '74'  '75'  '76'  '77'  '78'  '79'  '80'  '81'  '82'  '83'  '84'  '85'  '86'  '87'  '88'  '89'  '90'  '91'  '92'  '93'  '94'  '95'  '96'  '97'  '98'  '99'  '100'  '101'  '102'  '103'  '104'  '105'  '106'  '107'  '108'  '109'  '110'  '111'  '112'  }, [-0.2  0.7], 'newname', 'BDF file epochs', 'epochinfo', 'yes');
+    EEG = pop_saveset( EEG, 'filename',sub_name{sub},'filepath',SavePath1);
+    Markers = [];
+    for i = 1:length(EEG.event)
+        mar = EEG.event(i).type;
+        Markers = [Markers; mar]; 
+    end
+    Markers(find(Markers>178)) = [];
+    Markers = unique(Markers);
+
+    ElecName =[];
+    for i = 1:length(EEG.chanlocs)
+        ele = EEG.chanlocs(i).labels;
+        ElecName = [ElecName; string(ele)]; 
+    end
+    splitedCell = strsplit(sub_name{sub}, '.');
+    splitedCell2 = strsplit(splitedCell{1}, '_');
+    subjID = str2num(splitedCell2{2});
+    Design = [repmat(subjID, length(Markers), 1), Markers];
+    DesignM = [DesignM; Design];
+    j = n + length(Markers)-1;
+    sEEG(:, :, n:j) = EEG.data;
+    n = j+1;
+    for trl = 1:size(EEG.data, 3)
+        Frame = [repmat(subjID, length(ElecName), 1), repmat(Markers(trl),length(ElecName), 1), ElecName,  EEG.data(:, :, trl)];
+        AllDATA =[AllDATA; Frame];
+    end
+
+
+    fprintf('*****the subject %d has been done/n',subjID);
+end
+cell2csv('Exp02_EEG.csv', AllDATA, ',');
+save('Exp02_EEG.mat', 'AllDATA', '-v7.3');
+time = EEG.times;
+csvwrite('Exp02_EEGtime.csv', time)
+
+csvwrite('Exp02_DesignM.csv', DesignM)
+
+save('Exp_02DesignM_.mat', 'DesignM')
+save('Exp_02sEEG_1.mat', 'sEEG')
+save('Exp02_time_.mat', 'time')
+save('Exp02_chanloc_.mat', 'EEG')

LICENSE.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Yufang Wang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

data_load.m

@@ -0,0 +1,38 @@
+function [ rest_eyes ] = data_load( filename,key_words )
+%	自动读取filename路径下的与key_words有关的文件
+% -- input --
+%   filename - 想要读取的文件的路径
+%   key_words - 想要读取的文件关键字
+% -- output --
+%   输出所要得到的文件的名字
+
+rest = dir(filename);
+num = 1;
+num1 = 1;
+while num
+    if(rest(num1).bytes == 0)
+        rest(num1) = [];
+    else
+        num = 0;
+    end
+end
+for i = 1:length(rest)
+    rest_name{i} = rest(i).name;
+end
+if ~isempty(key_words)
+    for i = 1:length(rest_name)
+        ind = strfind(rest_name{i},key_words);
+        if (ind ~= 0)
+            kk(i) = i;
+        end
+    end
+    kk = kk(find(kk~=0));
+    rest_eyes = cell(1,length(kk));
+    for i = 1:length(kk)
+        rest_eyes{i} = rest_name{kk(i)};
+    end
+else
+    rest_eyes = rest_name;
+end
+
+