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crossgen_sensor_prep2prep_20170522.m
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crossgen_sensor_prep2prep_20170522.m
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%% Cross-validation matrix of preparatory attention. Done on sensor space
% written by Tanya Wen
% 2017/01/27
dbstop if error
% addpath(genpath('/imaging/tw05'))
%addpath(genpath('/imaging/local/software/spm_cbu_svn/releases/spm12_latest/'))
addpath(genpath('/imaging/local/software/spm_cbu_svn/releases/spm12_fil_r7219/'))
%addpath(genpath('/imaging/local/software/spm_toolbox/eeglab13_4_3b'))
spm('defaults', 'eeg');
% required software: libSVM & RSAtoolbox
workingdir = '/imaging/tw05/Preparatory_Attention_Study/Version3-FullExp';
addpath(genpath(fullfile(workingdir,'software','rsatoolbox')));
addpath(fullfile('/imaging/tw05/Preparatory_Attention_Study/','software','libsvm-mat-2.87-1'));
% control variables
libSVMsettings='-s 1 -t 0'; % nu-SVM, linear
nRandomisations=1000;
nfolds = 5;
% rmpath('/hpc-software/matlab/r2014a/toolbox/bioinfo/biolearning/'); % to make sure libSVM code is used (not strictly necessary: matlab svmtrain yields exactly same model)
% Define SUBJECT INFORMATION
subs = [1,2,3,4,5,6,7,8,9,10,11,13,15,16,17,18,19,20]; % subject numbers
subjects_dirs = {'meg16_0317/161107','meg16_0319/161110','meg16_0321/161111','meg16_0322/161114','meg16_0325/161115','meg16_0327/161117','meg16_0330/161121','meg16_0332/161122','meg16_0333/161124','meg16_0337/161128','meg16_0339/161129','meg16_0340/161129','meg16_0341/161201','meg16_0343/161202','meg16_0345/161206','meg16_0346/161206','meg16_0348/161208','meg16_0349/161208','meg16_0350/161212','meg16_0352/161213'};
subjnum = [1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,6,2]; % counterbalancing numbers
%% parallelize
nw=32;
scheduler=cbu_scheduler();
scheduler.SubmitArguments='-q compute -l mem=90gb -l walltime=460800';
if isempty(gcp('nocreate')) || ~exist('pool','var') || pool.NumWorkers ~= nw,
if ~isempty(gcp('nocreate'))
delete(gcp('nocreate'))
end
scheduler.NumWorkers=nw;
pool=parpool(scheduler,nw);
end
for sub = [5,11,17,6,18,19]
% go to subject directory
cd(workingdir)
swd = sprintf('sub%02d/%s',sub,subjects_dirs{sub}); % subject working directory
cd(swd)
%% attentional template
% load data
D = spm_eeg_load('caefMspm12_attention_task_block1_raw.mat');
data3D = D.fttimelock.trial;
% mark button press trials
bp_trials = []; % button press trials
devents = D.events;
for trial = 1:numel(D.events)
if ismember(4096,[devents{trial}.value]) == 1
bp_trials = [bp_trials trial];
end
end
D = badtrials(D, bp_trials, 1);
% get good channels
modalities = {'MEGMAG';'MEGPLANAR';'EEG'};
for m = 1:numel(modalities)
chans{m} = indchantype(D,modalities{m},'GOOD');
end
newdata3D = zscore(data3D(:,[chans{:}],:),[],2); % standardize each channel
cond_values = D.fttimelock.trialinfo;
%% define labels
% define subject specific pairings
allperms = perms([1 2 3]);
for i = 1:length(allperms)
if rem(subjnum(sub),6)+1 == i
choosetargets = allperms(i,:);
end
end
% define cue1 & cue2 index vectors
keep = indtrial(D,D.condlist,'GOOD');
cues=cell(1,2);
for i=1:2
cues{i} = intersect(find(cond_values==i),keep);
end
clear D
%% smoothing option
smoothing = {'Raw','Smooth'};
for smooth = 1:numel(smoothing)
%% sliding time window
winsizes = 8;%[8,25,50,125]; %32ms,100ms,200ms,500ms
for winsize = winsizes
% set analysis time windows
twin_train = [1 size(newdata3D,3)]; % time of interest
stpsize = 1; %step size
trainwins = [twin_train(1):stpsize:(twin_train(2)-winsize)]';
trainwins(:,2) = trainwins(:,1) + winsize;
ntrains = size(trainwins,1);
twin_test = [1 size(newdata3D,3)]; % time of interest
testwins = [twin_test(1):stpsize:(twin_test(2)-winsize)]';
testwins(:,2) = testwins(:,1) + winsize;
ntests = size(testwins,1);
accuracy_matrix = nan(nfolds,ntrains,ntests);
predicted_vals = cell(nfolds,ntrains,ntests);
for fold = 1: nfolds
testind=[cues{1}(fold:nfolds:end) cues{2}(fold:nfolds:end)];
labels_test=cond_values(testind)';
trainind=setdiff([cues{:}],testind);
labels_train=cond_values(trainind)';
parfor islide = 1:ntrains
itrain = newdata3D(:,:,trainwins(islide,1):trainwins(islide,2));
patternsTrain=double(itrain(trainind,:,:));
if smooth == 1 % no smoothing
patternsTrain=reshape(patternsTrain,length(trainind),[]);
elseif smooth == 2 % smoothing
patternsTrain = mean(patternsTrain,3);
end
% classification using linear SVM (train the classifier)
model=svmtrain(labels_train,patternsTrain,libSVMsettings);
for jslide = 1:ntests
itest = newdata3D(:,:,testwins(jslide,1):testwins(jslide,2));
patternsTest = double(itest(testind,:,:));
if smooth == 1 % no smoothing
patternsTest=reshape(patternsTest,length(testind),[]);
elseif smooth == 2 % smoothing
patternsTest = mean(patternsTest,3);
end
% classification using linear SVM (test the classifier)
[predicted,accuracy,~]=svmpredict(labels_test,patternsTest,model);
accuracy_matrix(fold,islide,jslide)=accuracy(1);
predicted_vals{fold,islide,jslide} = predicted;
end % nex slide
end % next slide
end % next fold
output_dir = 'crossgen_sensor_prep-prep';
try cd(output_dir)
catch eval(sprintf('!mkdir %s',output_dir)); cd(output_dir);
end
figure(sub)
imagesc(squeeze(mean(accuracy_matrix,1)));
colorbar;
caxis([1,100]);
% djm:
set(gca,'xticklabel',(cellfun(@str2double,get(gca,'xticklabel'))-1)*stpsize*4-100);
set(gca,'yticklabel',(cellfun(@str2double,get(gca,'yticklabel'))-1)*stpsize*4-100);
xlabel('start of test windows (ms)')
ylabel('start of train windows (ms)')
saveas(gcf,sprintf('CrossGenMatrix%03dms%s_sub%s.png',winsize*4,smoothing{smooth},num2str(sub)));
save(sprintf('CrossGenMatrix%03dms%s_sub%s.mat',winsize*4,smoothing{smooth},num2str(sub)));
close;
cd(workingdir)
cd(swd)
end % winsize
end % smooth
end
delete(gcp)