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Features_variations (2).m
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clc;
clear all;
close all;
%% Commentsh
% Different subvariations,
% feature/channel selection,
% features across time
% rankfeatures
%% Things to report
% contribution of different brain areas by doing single-channel decoding,
% per subject
% Decoding of each category, DIfferent frequ variations, number of features in
% features with more than one, combining them and their contribution,
% overlap of features calculated using some measeare, selecting features, categorizing features based on type, COnfusion matrices,
% discriminability of features on channels, average feature values
% (distribution) for each category, plotting erps of best features,
% Features across time, comparison of the three datasets,
%% These features provide 1 number for each trial and each channel
for variation=1:4
for Subject=1:10 % after pose make subjects strt from 1 and variations from 3
Fs=1000;
Dataset=1;
signal = DatasetLoading_variations(Dataset,Subject,variation); % channel, time, cat, trial
if variation==1
load(['Decoding_Accuracy_ALL_Subject_',num2str(Subject),'_Lighting.mat'],'accuracy');
elseif variation==2
load(['Decoding_Accuracy_ALL_Subject_',num2str(Subject),'_Pose.mat'],'accuracy');
elseif variation==3
load(['Decoding_Accuracy_ALL_Subject_',num2str(Subject),'_Size.mat'],'accuracy');
elseif variation==4
load(['Decoding_Accuracy_ALL_Subject_',num2str(Subject),'_Position.mat'],'accuracy');
end
for feature=[31 21:26]
if feature==33
net= load ('imagenet-caffe-alex.mat');
end
for channel = 1:size(signal,1)
for category = 1:size(signal,3)
for trial = 1:size(signal,4)
if feature==1
trial_data=signal(channel,1:200,category,trial);
else
trial_data=signal(channel,201:end,category,trial);
end
if feature<28 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4));
elseif feature==28 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),1013);
elseif (feature==29 || feature==30) && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),1000);
elseif feature==31 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),30);
elseif feature==32 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),size(signal,1));
elseif feature==33 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),1000);
elseif feature==34 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),1000);
elseif feature==35 && channel==1 && category==1 && trial==1
feature_chosen=nan.*ones(size(signal,1),size(signal,3),size(signal,4),30);
end
if sum(isnan(trial_data))>0
feature_chosen(channel,category,trial)=nan;
else
if feature==1
%% Baseline
feature_chosen(channel,category,trial) = mean(trial_data);
elseif feature==2
%% Time features: Sigal mean
feature_chosen(channel,category,trial) = mean(trial_data);
elseif feature==3
%% Signal median
feature_chosen(channel,category,trial) = median(trial_data);
elseif feature==4
%% Signal variance
feature_chosen(channel,category,trial) = var(trial_data);
elseif feature==5
%% Signal skewness
feature_chosen(channel,category,trial) = skewness(trial_data);
elseif feature==6
%% Signal Kurtosis
feature_chosen(channel,category,trial) = kurtosis(trial_data);
elseif feature==7
%% LZ complexity
threshold = median(trial_data);
% threshold = mean(data);
trial_data(trial_data>= threshold)=1;
trial_data(trial_data< threshold)=0;
[feature_chosen(channel,category,trial),~] = calc_lz_complexity(trial_data, 'exhaustive', 1);
elseif feature==8
%% Higuchi fractal dimension
maxtime = length(trial_data);
% Kmax = floor(maxtime./2);
Kmax = 10;
feature_chosen(channel,category,trial) = Higuchi_FD(trial_data,Kmax);
% % second implementation
% [HFD2(channel,category,trial),~,~,~] = HFD_LCALC(data);
% % thrid implementation
% HFD3(channel,category,trial) = hfd(data,Kmax);
elseif feature==9
%% Katz fractal dimensions
feature_chosen(channel,category,trial) = Katz_FD(trial_data);
elseif feature==10
%% Lyapunov exponent (largest LLE)
[feature_chosen(channel,category,trial),~] = lyaprosen(trial_data,0,0);
elseif feature==11
%% Hurst Exponent
feature_chosen(channel,category,trial) = estimate_hurst_exponent(trial_data);
% HE2(channel,category,trial) = genhurst(data);
% HE3(channel,category,trial) = hurstCC(data);
elseif feature==12
%% Sample entropy
feature_chosen(channel,category,trial) = entropy (trial_data);
% Ent2(channel,category,trial) = SampEn (2,0.2.* std(data),data,1);
elseif feature==13
%% Approximate Entropy
feature_chosen(channel,category,trial) = ApEn (2,0.2.* std(trial_data),trial_data,1);
% Ent4(channel,category,trial) = approx_entropy(2,0.2.* std(data),data);
elseif feature==14
%% ERP components P1/C1/P100, N1/N170, P200/P2a and P2b, chosen by visual inspection of PO3 channel, windows of components are 40, 60, 70 and 80 ms respectively.
% each trial can have 1 to 4 components
% P1
feature_chosen(channel,category,trial)=nanmean(trial_data([80:120]));
elseif feature==15
%% N1
feature_chosen(channel,category,trial)=nanmean(trial_data([120:200]));
elseif feature==16
%% P2a
feature_chosen(channel,category,trial)=nanmean(trial_data([150:220]));
elseif feature==17
%% P2b
feature_chosen(channel,category,trial)=nanmean(trial_data([200:275]));
elseif feature==18
%% Within-trial correlation
numLags=20;
[acf,lags,~] =autocorr(trial_data,numLags);
feature_chosen(channel,category,trial)= mean(acf);
elseif feature==19
%% Hjorth complexity
% this finds spread of the spectrum and represents the change in frequency
% Hcomplexity
step_size=1./Fs;
data_prime=(diff(trial_data)./step_size);
data_second=(diff(data_prime)./step_size);
feature_chosen(channel,category,trial)=(std(data_second).*std(trial_data))./(std(trial_data)).^2;
elseif feature==20
%% Hmobility
step_size=1./Fs;
data_prime=(diff(trial_data)./step_size);
data_second=(diff(data_prime)./step_size);
feature_chosen(channel,category,trial)=std(data_prime)./std(trial_data);
elseif feature==21
%% Mean Freq
feature_chosen(channel,category,trial) = meanfreq(trial_data,Fs);
elseif feature==22
%% Median Freq
feature_chosen(channel,category,trial) = medfreq(trial_data,Fs);
elseif feature==23
%% Average Freq
zeroscount=0;
for i=2:length(trial_data)
if (trial_data(i)>0 && trial_data(i-1)<0) || (trial_data(i)<0 && trial_data(i-1)>0)
zeroscount=zeroscount+1;
end
end
feature_chosen(channel,category,trial) =zeroscount.*(length(trial_data)./Fs);
elseif feature==24
%% Spectral edge frequency 95%
Fourier = fft(trial_data)/length(trial_data);
Fouriers = (abs(Fourier)); % Spectrum
Fv = linspace(0, 1, fix(length(trial_data)/2)+1)*Fs/2; % Frequency Vector
Iv = 1:length(Fv); % Index Vector
IntSpectrum = cumtrapz(Fv, Fouriers(Iv)); % Numeric Integration
feature_chosen(channel,category,trial) = interp1(IntSpectrum, Fv, 0.95*IntSpectrum(end), 'linear'); % Interploate To Find SEF
elseif feature==25
%% Power at Median Freq
amp = 2*abs(fft(trial_data))/length(trial_data);
phs = angle(fft(trial_data));
Fv = linspace(0, 1, fix(length(trial_data)/2)+1)*Fs/2; % Frequency Vector
Iv = 1:length(Fv); % Index Vector
fr_des = medfreq(trial_data,Fs); % Desired Frequency
ampv = amp(Iv); % Trim To Length Of Fv
phsv = phs(Iv); % Trim To Length Of Fv
ap = [ampv(:) phsv(:)]; % Amplitude & Phase Matrix
ap_des = interp1(Fv(:), ap, fr_des, 'linear');
feature_chosen(channel,category,trial) =ap_des(1);
elseif feature==26
%% Phase at Median Freq
amp = 2*abs(fft(trial_data))/length(trial_data);
phs = angle(fft(trial_data));
Fv = linspace(0, 1, fix(length(trial_data)/2)+1)*Fs/2; % Frequency Vector
Iv = 1:length(Fv); % Index Vector
fr_des = medfreq(trial_data,Fs); % Desired Frequency
ampv = amp(Iv); % Trim To Length Of Fv
phsv = phs(Iv); % Trim To Length Of Fv
ap = [ampv(:) phsv(:)]; % Amplitude & Phase Matrix
ap_des = interp1(Fv(:), ap, fr_des, 'linear');
feature_chosen(channel,category,trial) =ap_des(2);
elseif feature==27
%% Signal power
feature_chosen(channel,category,trial)=bandpower(trial_data);
elseif feature==28
%% Wavelet transform: 811 features per trial
[c,l] = wavedec(trial_data,5,'sym2');
[ca5] = appcoef(c,l,'sym2',5);
[cd1,cd2,cd3,cd4,cd5] = detcoef(c,l,[1 2 3 4 5]);
feature_chosen(channel,category,trial,:)=[ca5,cd1,cd2,cd3,cd4,cd5];
elseif feature==29
%% Hilbert transform amplitude: 800 features per trial
Hilb = hilbert(trial_data);
feature_chosen(channel,category,trial,:)=abs(Hilb);
elseif feature==30
%% Hilbert transform phase: 800 features per trial
Hilb = hilbert(trial_data);
feature_chosen(channel,category,trial,:)=angle(Hilb);
elseif feature==31
%% Phase-Amplitude Coupling: 792 features per trial
data.trial{1,1}= trial_data;
data.time{1,1}= [0.001:0.001:1.0];
data.trialinfo=[100]';
data.label{1,1}='SampleData';
toi=[0.001 1.0]; % time of interest
phase=[0.5 12]; % phase(1):0.4:phase(2)
ampl=[24 120]; % amp(1):2.8:amp(2)
diag = 'no'; %'yes' or 'no' to turn on or off diagrams during computation
surrogates = 'no'; %'yes' or 'no' to turn on or off surrogates during computation
approach = 'tort';%,'ozkort','canolty','PLV';
[MI_matrix_raw,~] = calc_MI(data,toi,phase,ampl,diag,surrogates,approach);
feature_chosen(channel,category,trial,:)=reshape(MI_matrix_raw(1:6,1:5),[30 1]);
elseif feature==32
%% Inter-channel correlation 31 feature per trial
ICC=zeros(size(signal,1),1);
for ch2=1:size(signal,1)
trial_data2=signal(ch2,201:end,category,trial);
ICC(ch2,1)=corr(trial_data',trial_data2');
end
feature_chosen(channel,category,trial,:)=ICC;
elseif feature==33
%% CNN
trial_data=(trial_data+abs(min(trial_data)));
trial_data=(trial_data).*(255./max(trial_data));
im_ = single(uint8(trial_data));
im_ = repmat(im_,[1 ceil(227*227./length(im_))]);
im_=im_(1,1:227*227);
im_=reshape(im_,[227 227]);
im_ = imresize(im_,net.meta.normalization.imageSize(1:2));
im_ = repmat(im_,[1 1 3]) - net.meta.normalization.averageImage;
res = vl_simplenn(net, im_);
feature_chosen(channel,category,trial,:)=squeeze(res.x);
elseif feature==34
%% Signal samples
feature_chosen(channel,category,trial,:)=trial_data;
elseif feature==35
%% Within-trial correlation 2,30 values
numLags=30;
[acf,lags,~] =autocorr(trial_data,numLags);
feature_chosen(channel,category,trial,:)= acf(2:end);
end
end
end
end
end
%% Classification
for ch=1:size(signal,1)
Xt=1;
Y=1;
for cat=1:size(signal,3)
isnotnan=~isnan(feature_chosen(:,cat,:,:));
tmp=squeeze(feature_chosen(ch,cat,isnotnan(1,1,:,1),:));
Xt=vertcat(Xt,reshape(tmp,[size(tmp,1)*size(tmp,2),1]));
Y=vertcat(Y,cat.*ones(size(tmp,1)*size(tmp,2),1));
end
X(1:length(Xt),ch)=Xt;
end
clearvars -except variation Fs True_Predicted_labels accuracy Subject signal feature X Y net
X=X(2:end,:);
Y=Y(2:end,:);
folds=10;
combinations=nchoosek(unique(Y),2);
for combination=1:size(combinations,1)
c=0;
for classes=combinations(combination,:)
for counter=1:length(Y)
if Y(counter)==classes
c=c+1;
YY(c)=Y(counter);
XX(c,:)=X(counter,:);
end
end
end
Xready=XX;
Yready=YY;
clearvars YY XX
inds_tmp=1:length(Yready);
for fld=1:folds
inds=randsample(inds_tmp,fix(length(Yready)./folds));
for j=1:length(inds)
inds_tmp((inds_tmp==inds(j)))=[];
end
inds_tr=1:length(Yready);
for j=1:length(inds)
inds_tr((inds_tr==inds(j)))=[];
end
Xtrain=Xready(inds_tr,:);
Ytrain=Yready(inds_tr);
Xtest=Xready(inds,:);
Ytest=Yready(inds);
% making every class pairs 1 vs 2
labs=unique(Ytrain);
for k=1:length(labs)
Ytrain(Ytrain==labs(k))=k;
Ytest(Ytest==labs(k))=k;
end
indx=Ytrain;
% Classifier_Model= fitcecoc(Xtrain,indx,'Learners','naivebayes'); % 'discriminant', 'knn', 'linear', 'naivebayes', 'svm', 'tree'
% % Linear 1.5 days for 10 subjects
% SVM classifier % 30 days for 10 subjects
% Classifier_Model = fitcsvm(Xtrain,indx,'Standardize',true,'KernelFunction','rbf','BoxConstraint',1);
% LDA classifier % 1.5 days for 10 subjects
Classifier_Model = fitcdiscr(Xtrain,indx,'DiscrimType','pseudoquadratic');
% KNN classifier % More than 1.5 days, I did not wait to calculate it
% Classifier_Model = fitcknn(Xtrain,indx,'NumNeighbors',5,'Standardize',1);
[~,score] = predict(Classifier_Model,Xtest);
[~,PredictedLabels]=max(score');
accuracy(feature,combination,fld)=sum(PredictedLabels==Ytest)./length(Ytest);
% True_Predicted_labels{feature,combination,fld,1}=Ytest;
% True_Predicted_labels{feature,combination,fld,2}=PredictedLabels;
end
end
[variation Subject feature]
if variation==1
save(['Decoding_Accuracy_ALL2_Subject_',num2str(Subject),'_Lighting.mat'],'accuracy');
elseif variation==2
save(['Decoding_Accuracy_ALL2_Subject_',num2str(Subject),'_Pose.mat'],'accuracy');
elseif variation==3
save(['Decoding_Accuracy_ALL2_Subject_',num2str(Subject),'_Size.mat'],'accuracy');
elseif variation==4
save(['Decoding_Accuracy_ALL2_Subject_',num2str(Subject),'_Position.mat'],'accuracy');
end
end
end
end