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main_MI_offline_crossSubject.m
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%% Study the effect of transfer learning in 3 different components of offline cross-subject MI classification:
% 1. Data alignment
% 2. Spatial filtering
% 3. Classification
%
%% Compare 27 different algorithms:
% 1. CSP-LDA
% 2. CSP-CLDA
% 3. CSP-wAR
% 4. CCSP-LDA
% 5. CCSP-CLDA
% 6. CCSP-wAR
% 7. RCSP-LDA
% 8. RCSP-CLDA
% 9. RCSP-wAR
% 10. EA-CSP-LDA
% 11. EA-CSP-CLDA
% 12. EA-CSP-wAR
% 13. EA-CCSP-LDA
% 14. EA-CCSP-CLDA
% 15. EA-CCSP-wAR
% 16. EA-RCSP-LDA
% 17. EA-RCSP-CLDA
% 18. EA-RCSP-wAR
% 19. PS-CSP-LDA
% 20. PS-CSP-CLDA
% 21. PS-CSP-wAR
% 22. PS-CCSP-LDA
% 23. PS-CCSP-CLDA
% 24. PS-CCSP-wAR
% 25. PS-RCSP-LDA
% 26. PS-RCSP-CLDA
% 27. PS-RCSP-wAR
%
% We study offline cross-subject transfer, by combining data from all other subjects to form a single source domain
% Experiment 1: on Dataset 1, 7 subjects
% Experiment 2: on Dataset 2a, using training data of the 9 subjects only
% Experiment 3: on Dataset 2a, using evaluation data of the 9 subjects only
%
% Need the covariance toolbox: https://github.com/alexandrebarachant/covariancetoolbox
%
%% Dongrui Wu, drwu@hust.edu.cn
clc; clearvars; close all; warning off all; rng('default');
nRepeat=30; % number of repeats to get statistically meaningful results
minN=0; % min number of target labeled samples
maxN=20; % max number of target labeled samples
nStep=4; % number of target labeled samples to add in each iteration
nAlgs=27; % Number of algorithms
for ds=1:2
switch ds
case 1
files=dir('./Data1/A*.mat');
case 2
files=dir('./Data2/A*T.mat');
case 3
files=dir('./Data2/A*E.mat');
end
XRaw=[]; XallEA=[]; XallPS=[]; yAll=[]; nSubs=length(files);
for s=1:nSubs
s
load([files(s).folder '\' files(s).name]);
XRaw=cat(3,XRaw,X); yAll=cat(1,yAll,y); nTrials=length(y);
%% EA for all subjects
% Need the covariance toolbox: https://github.com/alexandrebarachant/covariancetoolbox
refEA=mean_covariances(covariances(X),'arithmetic');
sqrtRefEA=refEA^(-1/2);
XEA=nan(size(X));
for j=1:length(y)
XEA(:,:,j)=sqrtRefEA*X(:,:,j);
end
XallEA=cat(3,XallEA,XEA);
%% PS for all subjects
% Need the covariance toolbox: https://github.com/alexandrebarachant/covariancetoolbox
refPS=mean_covariances(covariances(X),'riemann');
sqrtRefPS=refPS^(-1/2);
XPS=nan(size(X));
for j=1:length(y)
XPS(:,:,j)=sqrtRefPS*X(:,:,j);
end
XallPS=cat(3,XallPS,XPS);
end
labels=unique(y);
%% Main loop
Accs=cell(1,nSubs);
for t=1:nSubs
idsTarget=(t-1)*nTrials+1:t*nTrials;
idsSource=1:nSubs*nTrials; idsSource(idsTarget)=[];
XtRaw=XRaw(:,:,idsTarget); XtEA=XallEA(:,:,idsTarget);
XtPS=XallPS(:,:,idsTarget); yt=yAll(idsTarget);
XsRaw=XRaw(:,:,idsSource); XsEA=XallEA(:,:,idsSource);
XsPS=XallPS(:,:,idsSource); ys=yAll(idsSource);
ids1=find(ys==labels(1)); ids2=find(ys==labels(2));
acc=zeros(nAlgs,nRepeat,floor((maxN-minN)/nStep)+1);
parfor r=1:nRepeat
[t,r]
tempAcc=nan(nAlgs,floor((maxN-minN)/nStep)+1);
idsTrain0=datasample(1:nTrials,maxN,'replace',false);
while length(unique(yt(idsTrain0(1:nStep))))==1 % make sure the first nStep samples include two classes
idsTrain0=datasample(1:nTrials,maxN,'replace',false);
end
idsTest0=1:nTrials; idsTest0(idsTrain0)=[];
%% Offline calibration
for n=minN:nStep:maxN % select training trials from the training pool
idsTrain=idsTrain0(1:n);
idsTest=cat(2,idsTrain0(n+1:end),idsTest0);
XtTestRaw=XtRaw(:,:,idsTest);
ytTest=yt(idsTest);
XtTrainRaw=XtRaw(:,:,idsTrain);
ytTrain=yt(idsTrain);
yTrain=cat(1,ytTrain,ys);
%% %%% Case 1: CSP filters from the target subject only, no EA, no TLCSP
% Target training data only
if n>0
% 1. CSP-LDA
idxAlg=1;
[fTrain,fTest]=CSPfeature(XtTrainRaw,ytTrain,XtTestRaw);
LDA = fitcdiscr(fTrain,ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 2. CSP-CLDA
idxAlg=2;
[fTrain,fTest]=CSPfeature(XtTrainRaw,ytTrain,cat(3,XsRaw,XtTestRaw));
fTrain=cat(1,fTrain,fTest(1:length(ys),:)); fTest(1:length(ys),:)=[];
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 3. CSP-wAR
idxAlg=3;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR3=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR3=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR3(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR3);
end
%% %%% Case 2: CCSP, use source data in CSP, no EA
% 4. CCSP-LDA
idxAlg=4;
[fTrain,fTest]=CSPfeature(cat(3,XtTrainRaw,XsRaw),yTrain,XtTestRaw);
if n>0
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
end
% 5. CCSP-CLDA
idxAlg=5;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 6. CCSP+wAR
idxAlg=6;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR6=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR6=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR6(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR6);
%% %%% Case 3: RCSP, use source data in CSP, no EA
% 7. RCSP-LDA
idxAlg=7;
[fTrain,fTest]=RCSPfeature(XsRaw,ys,cat(3,XtTrainRaw,XtTestRaw),ytTrain);
if n>0
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
end
% 8. RCSP-CLDA
idxAlg=8;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 9. RCSP-wAR
idxAlg=9;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR9=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR9=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR9(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR9);
%% %%% Case 4: EA + CSP, no TL in CSP
if n>0
% 10. EA-CSP-LDA
idxAlg=10;
[fTrain,fTest]=CSPfeature(XtEA(:,:,idsTrain),ytTrain,XtEA(:,:,idsTest));
LDA = fitcdiscr(fTrain,ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 11. EA-CSP-CLDA
idxAlg=11;
[fTrain,fTest]=CSPfeature(XtEA(:,:,idsTrain),ytTrain,cat(3,XsEA,XtEA(:,:,idsTest)));
fTrain=cat(1,fTrain,fTest(1:length(ys),:)); fTest(1:length(ys),:)=[];
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 12. EA-CSP-wAR
idxAlg=12;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR12=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR12=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR12(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR12);
end
%% %%% Case 5: EA + CCSP
% 13. EA-CCSP-LDA
idxAlg=13;
[fTrain,fTest]=CSPfeature(cat(3,XtEA(:,:,idsTrain),XsEA),yTrain,XtEA(:,:,idsTest));
if n>0
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
end
% 14. EA-CCSP-CLDA
idxAlg=14;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 15. EA-CCSP-wAR
idxAlg=15;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR15=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR15=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR15(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR15);
%% %%% Case 6: EA + RCSP
% 16. EA-RCSP-LDA
idxAlg=16;
[fTrain,fTest]=RCSPfeature(XsEA,ys,XtEA(:,:,[idsTrain idsTest]),ytTrain);
if n>0
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
end
% 17. EA-RCSP-CLDA
idxAlg=17;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 18. EA-RCSP-wAR
idxAlg=18;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR18=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR18=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR18(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR18);
%% %%% Case 7: PS + CSP, no TL in CSP
if n>0
% 19. PS-CSP-LDA
idxAlg=19;
[fTrain,fTest]=CSPfeature(XtPS(:,:,idsTrain),ytTrain,cat(3,XsPS,XtPS(:,:,idsTest)));
fTrain=cat(1,fTrain,fTest(1:length(ys),:)); fTest(1:length(ys),:)=[];
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 20. PS-CSP-CLDA
idxAlg=20;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 21. PS-CSP-wAR
idxAlg=21;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR21=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR21=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR21(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR21);
end
%% %%% Case 8: PS + CCSP
% 22. PS-CCSP-LDA
idxAlg=22;
[fTrain,fTest]=CSPfeature(cat(3,XtPS(:,:,idsTrain),XsPS),yTrain,XtPS(:,:,idsTest));
if n>0
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
end
% 23. PS-CCSP-CLDA
idxAlg=23;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 24. PS-CCSP-wAR
idxAlg=24;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR24=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR24=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR24(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR24);
%% %%% Case 9: PS + RCSP
% 25. PS-RCSP-LDA
idxAlg=25;
[fTrain,fTest]=RCSPfeature(XsPS,ys,XtPS(:,:,[idsTrain idsTest]),ytTrain);
if n>0
LDA = fitcdiscr(fTrain(1:n,:),ytTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
end
% 26. PS-RCSP-CLDA
idxAlg=26;
LDA = fitcdiscr(fTrain,yTrain); yPred=predict(LDA,fTest);
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPred);
% 27. PS-RCSP-wAR
idxAlg=27;
if n==minN || isnan(tempAcc(idxAlg,(n-minN)/nStep))
yPredWAR27=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,[]);
else
yPredWAR27=wAR(fTrain(n+1:end,:),ys,cat(1,fTrain(1:n,:),fTest),ytTrain,yPredWAR27(nStep+1:end));
end
tempAcc(idxAlg,(n-minN)/nStep+1)=100*mean(ytTest==yPredWAR27);
end
acc(:,r,:)=tempAcc;
end
squeeze(mean(acc,2))
Accs{t}=acc;
end
eval(['Accs' num2str(ds) '=Accs;']);
mmAcc=zeros(nAlgs,(maxN-minN)/nStep+1);
for t=1:nSubs
mmAcc=mmAcc+squeeze(mean(Accs{t},2))/nSubs;
end
mmAcc
save(['OfflineMI_dataset' num2str(ds) '.mat'],'Accs','nStep','minN','maxN','nAlgs','nSubs','mmAcc');
end