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Script09_Hypothesis4b.m
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Script09_Hypothesis4b.m
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clc;
clear all;
close all;
%% Hypothesis 4b: subsequent memory effect in power spectrum
for Subject=[1:33]
All_channels=[1:65];
Fs=512; % Sampling freq
file_name=['Z:\projects\Hamid\Projects\EEGManyPipelines\Analyses\Subject',num2str(Subject,'%02.f'),...
'\','Subject',num2str(Subject,'%02.f'),'_preprocessed_data.mat'];
load(file_name,'Data_matrix','events','channels')
% Decoding
tr1=0;
tr2=0;
for trial=1:length(events)
if strcmp(events(trial).subsequent_memory,'subsequent_remembered')
tr1=tr1+1;
ClassA(:,:,tr1)=Data_matrix(All_channels,:,trial);
elseif strcmp(events(trial).subsequent_memory,'subsequent_forgotten')
tr2=tr2+1;
ClassB(:,:,tr2)=Data_matrix(All_channels,:,trial);
end
end
% Extracting time-frequency power (Event-related spectral purturbation
% as defined in EEGLAB): for every trial for decoding
epoch_wind=[-256:512];
erspA=nan(65,7500,size(ClassA,3));
erspB=nan(65,7500,size(ClassB,3));
for ch=1:65
for trial=1:size(ClassA,3)
[tmpA,~,~,times,freqs,~,~]=newtimef(ClassA(ch,:,trial),size(Data_matrix,2),...
[epoch_wind(1) epoch_wind(end)]*1000/Fs,Fs, 0,...
'timesout',[150],'freqs',[0 200],'nfreqs',50,...
'plotersp','off','plotitc','off');
erspA(ch,:,trial)=reshape(tmpA,1,[]);
end
for trial=1:size(ClassB,3)
[tmpB,~,~,times,freqs,~,~]=newtimef(ClassB(ch,:,trial),size(Data_matrix,2),...
[epoch_wind(1) epoch_wind(end)]*1000/Fs,Fs, 0,...
'timesout',[150],'freqs',[0 200],'nfreqs',50,...
'plotersp','off','plotitc','off');
erspB(ch,:,trial)=reshape(tmpB,1,[]);
end
end
% Equalizing number of trials between classes by undersampling the
% dominant class and repeating the procedure untill all data is used:
% this is necessary to avoid bias in decoding
for time_freq=1:size(erspA,2)
ClassA_time=squeeze(erspA(:,time_freq,:));
ClassB_time=squeeze(erspB(:,time_freq,:));
if size(ClassA_time,2)>size(ClassB_time,2)
inds_tmp=1:size(ClassA_time,2);
inds_second=size(ClassB_time,2);
folds=floor(size(ClassA_time,2)./size(ClassB_time,2));
else
inds_tmp=1:size(ClassB_time,2);
inds_second=size(ClassA_time,2);
folds=floor(size(ClassB_time,2)./size(ClassA_time,2));
end
for fld=1:folds
if size(ClassA_time,2)~=size(ClassB_time,2)
inds=randsample(inds_tmp,inds_second);
for j=1:length(inds)
inds_tmp((inds_tmp==inds(j)))=[];
end
else
inds=inds_tmp;
end
if size(ClassA_time,2)>size(ClassB_time,2)
Xready=[ClassA_time(:,inds)';ClassB_time'];
Yready=[ones(size(ClassA_time(:,inds),2),1);zeros(size(ClassB_time,2),1)]';
else
Xready=[ClassB_time(:,inds)';ClassA_time'];
Yready=[ones(size(ClassB_time(:,inds),2),1);zeros(size(ClassA_time,2),1)]';
end
Classifier_Model = fitcdiscr(Xready,Yready);
decoding(time_freq,fld)=1-kfoldLoss(crossval(Classifier_Model));
end
[Subject time_freq]
end
decoding_accuracy=nanmean(decoding,2);
file_name=['Z:\projects\Hamid\Projects\EEGManyPipelines\Analyses\Subject',num2str(Subject,'%02.f'),...
'\','Subject',num2str(Subject,'%02.f'),'_Results_Hypothesis4b.mat'];
save(file_name,'decoding_accuracy','times','freqs');
clearvars -except Subject All_channels
end
%% Plotting and statistical testing
clc;
clear all;
close all;
% loading decoding results from all participants
Accuracies=nan(33,7500);
s=0;
for Subject=[1:33]
s=s+1;
file_name=['Z:\projects\Hamid\Projects\EEGManyPipelines\Analyses\Subject',num2str(Subject,'%02.f'),...
'\','Subject',num2str(Subject,'%02.f'),'_Results_Hypothesis4b.mat'];
load(file_name);
Accuracies(s,:)=decoding_accuracy;
AccuraciesT(:,:,s)=reshape(Accuracies(s,:),[50 150]);
end
% Evaluation of significance of effect against decoding in the baseline
% period
for freq=1:size(AccuraciesT,1)
for time_freq=1:size(AccuraciesT,2)
[p(freq,time_freq),h(freq,time_freq)]=signrank(squeeze(AccuraciesT(freq,time_freq,:)),squeeze(nanmean(AccuraciesT(freq,1:45,:),2)),'tail','right');
end
end
% Corrrecting p values for multiple comparisons using Combined Probability of Fisher
threshold=0.05;
[pCorrected,~, ~, ~] = mt_fisher(reshape(p,1,[]), threshold);
pnew=zeros(size(pCorrected));
pnew(pCorrected<threshold)=1;
time_span_of_clustering= 5; % 5 = 10 ms: only the time and frequency spans
% in which all points are sginificant will be marked as significant
pCorrected=bwareaopen(pnew,time_span_of_clustering);
% plotting decoding curves and indicating time points of significant effect
AccuraciesT=squeeze(nanmean(AccuraciesT,3));
limits_scale=round([min(min(AccuraciesT)) max(max(AccuraciesT))]*100);
imagesc(AccuraciesT);
AccuraciesT=uint8(round(rescale(AccuraciesT).*64));
ERSP=flipud(AccuraciesT);
image(ERSP)
hold on
pCorrected=reshape(pCorrected,50,150);
imcontour(flipud(pCorrected),1,'k')
line([46 46],[1 size(ERSP,1)],'color','k');
yticks=flip(freqs,2);
set(gca,'FontSize',10,'LineWidth',1,'XTick',...
[16 46 75 105 135],'XTickLabel',...
[-250:250:750],'YTick',...
[1:5:size(ERSP,1)],'YTickLabel',{downsample( yticks , 5 )},...
'XMinorTick','on','YMinorTick','off','ycolor','k','tickdir','out','xcolor','k','box','off');
xlim([1 size(ERSP,2)]);
ylim([1 size(ERSP,1)]);
xlabel('Time relative to stimulus onset (ms)');
ylabel('Frequency (Hz)');
title ('H4b: subsequent memory effect in spectral power');
c=colorbar('Ticks',[linspace(0,64,5)],...
'TickLabels',{[linspace(limits_scale(1),limits_scale(end),5)]});
c.Label.String = 'Decoding accuracy (%)';
set(c,'FontSize',10,'LineWidth',1)