response_correct_fld0.05.mat, response_fld0.05_correct.mat both of these .mat files contains the index values for the correct responses of size 1470x1,1837x1 respectively. Using these we extract eegdata from S, and nback,ctype targets, along with processed_metadata. All these values are put together in a single mat file called data147.mat/data18…

data1470.m

load('processed_target_response_2s.mat')
load('S1.mat')
load('etc\response_correct_fld0.05.mat')

nback_target = nback_target(idx);
ctype_target = ctype_target(idx);
processed_meta_2s = processed_meta_2s(idx,:);
eegdata = S;

save data1470.mat eegdata nback_target ctype_target processed_meta_2s

data1837.m

load('processed_target_response_2s.mat')
load('S2.mat')
load('etc\response_fld0.05_correct.mat') % gives idx

nback_target = nback_target(idx);
ctype_target = ctype_target(idx);
processed_meta_2s = processed_meta_2s(idx,:);
eegdata = S;

save data1837.mat eegdata nback_target ctype_target processed_meta_2s

eeg_dataprocess.m

clear all;
clc;

% loading Preprocessed_2s, which contains data
load('Preprocessed_response_2s.mat')
% loading correct respose indices 
load('etc\response_fld0.05_correct.mat');%==>S2.mat
% load('etc\response_correct_fld0.05.mat');%==>S1.mat

S=[];
for s = 1:length(Preprocessed_2s)
%     if any(s==[3,4,12,23])
%         continue;
%     end
    % subjects exluded
    if any(s ==[3,4,12,19,22,23,33,34])
            continue;
    end
    % extracting n-back features from the remaing subjects data
    fprintf('Subject %d: Name: %s\n',s,Preprocessed_2s(s).subject) 
    s1 = Preprocessed_2s(s).data;
    s1 = double(s1);
    F2 = [];
    for j = 1:size(s1,3)
        F1 = [];
        %fprintf('[Info]: epoch %d/%d\n',j,size(s1,3));
        for i = 1:size(s1,1)
            x = s1(i,:,j);
            f = nback_features(x);
            F1 = [F1;f];
        end
        F1 = F1';
        F1ravel = F1(:);
        F2 = [F2;F1ravel'];
    end
%NOTE:      
%     F2= (F2-min(F2)) ./ (max(F2)-min(F2)); %This is throwing error for me
%     in 2016a
% Modified: 
    num = F2-min(min(F2));
    den = max(max(F2))-min(min(F2));
    F2 = num./den;
    S = [S;F2];
    disp('#########################')
end
S=S(idx,:);

psvm_classification.m

clear all;
clc;
stype={'char','pos','bar','pie'};
f=0;
for type=1:4
    load('data1837.mat');  %chage file to data1470.mat to load other data set
    %% This is where I modified for version compatibility
    idx = find(strcmp(processed_meta_2s(:,4),stype{type}));
    %% 
    S=eegdata;
    nback_target=nback_target(idx,:);
    fprintf('Type : %s\n',stype{type});
    fprintf('Number of samples for..\n');
    fprintf('0,1,2,3 back respectively :%4d,%4d,%4d,%4d\n',size(find(nback_target==0),1),size(find(nback_target==1),1),size(find(nback_target==2),1),size(find(nback_target==3),1));
    a=size(find(nback_target==0),1);
    b=size(find(nback_target==1),1);
    c=size(find(nback_target==2),1);
    d=size(find(nback_target==3),1);
    e = sum([a b c d]);
    
    fprintf('--------------------------------------------\n');
    fprintf('          Test Acc      F1 score\n');
    
    for j=0:2
        for k=(j+1):3
            
            num1=j;
            num2=k;
    
            idx1=find(nback_target==num1);
            idx2=find(nback_target==num2);
            
            data1=S(idx1,:);
            data2=S(idx2,:);
            
            A=[data1;data2];
            Y=[ones(size(idx1,1),1); -1*ones(size(idx2,1),1)];
            
            ridx=randperm(size(Y,1));
            A=A(ridx,:);
            Y=Y(ridx,:);
            
            [w, gamma, train, test, trainf1, testf1, time, nu]=psvm(A,Y,10,0,false,1);
            
            % [w gamma train test time nu] = n_psvm(A,Y,0.5,5);
            fprintf('%d vs %d %10.2f %10.2f\n',num1,num2,test,testf1);
        end
        
    end
    fprintf('--------------------------------------------\n');
    disp(e)
    f =f+e;
    
end
disp(f)