diff --git a/2015-sarasola-sanz-EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies.pdf b/2015-sarasola-sanz-EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies.pdf
deleted file mode 100644
index 077f0c6..0000000
--- a/2015-sarasola-sanz-EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies.pdf
+++ /dev/null
Binary files differ
diff --git a/2015-shiman-Towards Decoding of Functional Movements from the Same Limb using EEG.pdf b/2015-shiman-Towards Decoding of Functional Movements from the Same Limb using EEG.pdf
deleted file mode 100644
index ac26fcf..0000000
--- a/2015-shiman-Towards Decoding of Functional Movements from the Same Limb using EEG.pdf
+++ /dev/null
Binary files differ
diff --git a/2016-spueler-Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients.pdf b/2016-spueler-Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients.pdf
deleted file mode 100644
index 33278f5..0000000
--- a/2016-spueler-Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients.pdf
+++ /dev/null
Binary files differ
diff --git a/analyze.m b/analyze.m
deleted file mode 100644
index dbcc954..0000000
--- a/analyze.m
+++ /dev/null
@@ -1,24 +0,0 @@
-[signal, states, params] = load_bcidat('/home/jph/Uni/Masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R01.dat');
-
-% [ splitSignals, code ] = cutAll( states, signal );
-[ splitSignals, code ] = cutWindows( states, signal, params, 1);
-
-NFFT=2048; % should be 2^n
-
-[ filteredSig, pwMeanMove, pwMeanRest,hz ] = myFilter( splitSignals,code,NFFT );
-
-%plotChannelProzDiff(pwMeanMove,pwMeanRest,hz,params);
-%plotChannels(pwMeanMove,hz,params);
-plotImgScProzDiff( pwMeanMove, pwMeanRest)
-
-% figure
-% subplot(2,1,1)
-% plot(log(pwMeanMove))
-% xlim([0 150])
-% %ylim([-2 6])
-% subplot(2,1,2)
-% plot(log(pwMeanRest))
-% xlim([0 150])
-% %ylim([-2 6])
-
-    
\ No newline at end of file
diff --git a/balance2Classes.m b/balance2Classes.m
deleted file mode 100644
index f4bae25..0000000
--- a/balance2Classes.m
+++ /dev/null
@@ -1,14 +0,0 @@
-function [trainClasses,trainData]=balance2Classes(trainClasses,trainData)
-    %balances two classes (0,1)
-    eps=0.00001;
-    while mean(trainClasses)> 0.5+eps || mean(trainClasses) < 0.5-eps
-        r=floor(0.5*size(trainClasses)*rand(1)+1);
-        if mean(trainClasses) < 0.5
-            more=find(trainClasses==0);
-        else
-            more=find(trainClasses==1);
-        end
-        trainClasses=trainClasses([1:more(r)-1,more(r)+1:end]);
-        trainData=trainData([1:more(r)-1,more(r)+1:end],:);
-    end
-end
\ No newline at end of file
diff --git a/balanceClasses.m b/balanceClasses.m
deleted file mode 100644
index 54e7ba0..0000000
--- a/balanceClasses.m
+++ /dev/null
@@ -1,20 +0,0 @@
-function [trainClasses,trainData]=balanceClasses(trainClasses,trainData,maxPerClass,noClasses)
-%     if maxPerClass > min(histcounts(trainClasses,noClasses))
-%         maxPerClass=min(histcounts(trainClasses));
-%         warning('not enough Data per Class; set maxPerClass to %i', maxPerClass);
-%     end
-    i=1;
-    delPerClass=histcounts(trainClasses,noClasses)-maxPerClass;
-    delIndex=zeros(sum(delPerClass),1);
-    while sum(delPerClass)>0
-        r=fix(size(trainClasses,1)*rand(1)+1);
-        if delPerClass(trainClasses(r)+1)>0 && all(delIndex~=r)
-            delIndex(i)=r;
-            i=i+1;
-            delPerClass(trainClasses(r)+1)=delPerClass(trainClasses(r)+1)-1;
-        end
-    end
-    trainClasses(delIndex)=[];
-    trainData(delIndex,:)=[];
-    assert(size(trainClasses,1)>0)
-end
\ No newline at end of file
diff --git a/callAll.m b/callAll.m
deleted file mode 100644
index aceaf65..0000000
--- a/callAll.m
+++ /dev/null
@@ -1,27 +0,0 @@
-addpath('/home/hohlochj/masterarbeit')
-pathToFile='/nfs/wsi/ti/messor/hohlochj/origData/dirs.txt';
-% pathToFile='/home/jph/Uni/masterarbeit/Block1_ReachingMovements/dirs.txt';
-
-windowEMG=0.2;
-windowEEG=1;
-shift=0.2;
-pburgOrder=50;
-minEEGFreq=0;
-maxEEGFreq=200;
-k=10;
-maxExpC=0;
-maxPerClass=250;
-EEG=true;
-poolObj=parpool(32);
-
-[subjects,numbers]=structForAllDays(pathToFile);
-for i=1:size(subjects,2)
-    subject=subjects{i};
-    for number=numbers{i}
-%         fprintf('%s%i\n',subject,number);
-        readEEGEciton(subject,number,windowEMG,windowEEG,shift,5,7500,pburgOrder,minEEGFreq,maxEEGFreq);
-        svmEciton(subject,number,false,k,maxExpC,maxPerClass);
-        svmEciton(subject,number,EEG,k,maxExpC,maxPerClass);
-    end
-end
-delete(poolObj)
diff --git a/chooseForCode.m b/chooseForCode.m
deleted file mode 100644
index aa15422..0000000
--- a/chooseForCode.m
+++ /dev/null
@@ -1,12 +0,0 @@
-function [ splitSamplesCode ] = chooseForCode(code,codes, splitSamples)
-%chooseForCode chooses the samples for given code
-%   input the code to choose, the list of codes, the number of Samples and
-%   the list of Samples to get the list with the given code
-    splitSamplesCode=[];
-    for i=1:size(codes)
-        if code == codes(i)
-            splitSamplesCode=[splitSamplesCode,splitSamples(:,:,i)];
-        end
-    end
-end
-
diff --git a/classifyAccordingToEMG.m b/classifyAccordingToEMG.m
deleted file mode 100644
index 4937188..0000000
--- a/classifyAccordingToEMG.m
+++ /dev/null
@@ -1,26 +0,0 @@
-function [classification]=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG, classes,shift,frequency,threshold)
-% classifyAccordingToEMG data classified as -1 should be taken out
-    classification=zeros([size(trainingDataEEG,1),1]);
-    oldclass=0;
-    for i=1:size(trainingDataEEG,1)
-        if mean(trainingDataEMG(i,:)) > threshold % is movement
-            class=mode(classes(max(round(shift*frequency*i-frequency),1):min(round(i*shift*frequency-1),end)));
-            if class <= 5 % task is to move
-                if oldclass==0
-                    for j=max(fix(i-1/shift),1):fix(i-0.5/shift) % Pause before beginning of movement
-                        classification(j)=-1; %code for pause
-                    end
-                    for j=max(fix(i-0.5/shift),1):i % half a second before executed movement there should be activity (esp. EEG)
-                        classification(j)=-1; %if -1 there is a pause of 1s befor movement onset
-                    end
-                    oldclass=class;
-                end
-                classification(i)=class;
-            else % task is to rest but movement not finished
-                classification(i)=oldclass;
-            end
-        else
-            oldclass=0;
-        end
-    end
-end
\ No newline at end of file
diff --git a/cutAll.m b/cutAll.m
deleted file mode 100644
index 123d7b6..0000000
--- a/cutAll.m
+++ /dev/null
@@ -1,16 +0,0 @@
-function [ splitSignals, code ] = cutAll( states, signal )
-% cut all in same length by dropping the tail
-
-changeIndices=[find(diff(double(states.StimulusCode)));size(states.StimulusCode,1)];
-codeCount=[diff(changeIndices); size(states.StimulusCode,1)-sum(diff(changeIndices))];
-
-splitSignals=[];
-code =zeros(size(codeCount,1));
-c=codeCount(1);
-
-for i=2:size(codeCount,1)
-    splitSignals=cat(3,splitSignals,signal(c:c+4998,:));
-    code(i)=states.StimulusCode(c);
-    c=c+codeCount(i);
-end
-end
\ No newline at end of file
diff --git a/cutMovements.m b/cutMovements.m
deleted file mode 100644
index bcd0132..0000000
--- a/cutMovements.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function [ splitSignals, code ] = cutMovements( stimulusCodes, signal )
-%cut Trials only, ignore resting phases
-
-noSamplesPerTrial=15000;
-% count how many recorded values belong to one trial
-changeIndices=[find(diff(double(stimulusCodes)));size(stimulusCodes,1)];
-codeCount=[diff(changeIndices); size(stimulusCodes,1)-sum(diff(changeIndices))];
-
-splitSignals=[];
-code=[];
-
-c=1;
-
-for i=1:size(codeCount,1)
-    if(codeCount(i)==noSamplesPerTrial && stimulusCodes(c)~=0)
-        splitSignals=cat(3,splitSignals,signal(c:c+codeCount(i)-1,:));
-        code=cat(1,code,stimulusCodes(c));
-    end
-    c=c+codeCount(i);
-end
-end
-
diff --git a/cutWindows.m b/cutWindows.m
deleted file mode 100644
index 61b5b46..0000000
--- a/cutWindows.m
+++ /dev/null
@@ -1,21 +0,0 @@
-function [ splitSignals, code ] = cutWindows( stimulusCodes, signal, params, t )
-    changeIndices=cat(1,0,find(diff(double(stimulusCodes))));
-    codeCount=diff(changeIndices);
-    interval=t*params.SamplingRate.NumericValue;
-
-    splitSignals=[];
-    code=[];
-
-    c=codeCount(1)+1;
-    %cut windows of 1s
-    for i=2:(size(codeCount,1)) %exclude beginning and end (stimulus 0)
-        tempc=0;
-        while codeCount(i)-tempc >= interval
-            splitSignals=cat(3,splitSignals,signal(c+tempc:c+tempc+interval-1,:));
-            code=cat(1,code,double(stimulusCodes(c+tempc)));
-            tempc=tempc+interval;
-        end
-        c=c+codeCount(i);
-    end
-end
-
diff --git a/fileInfo.txt b/fileInfo.txt
deleted file mode 100644
index a4a7f43..0000000
--- a/fileInfo.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-Stimuli Sequence
-12 3 8 1 10 4 7 2 15 4 12 1 14 2 9 3 11 2 13 4 10 3 7 3 12 1 9 1 15 1 6 3 13 2 8 4 11 1 14 4 6 4 9 1 14 2 7 4 6 1 8 3 10 3 7 2 15 4 12 1 14 2 9 4 11 3 13 4 10 2 7 1 12 3 9 2 15 2 6 3 13
-
-Pre & Post Run
-3s
-
-Stimulus duration
-4s
-
-Sampling Rate
-2500
diff --git a/generateTrainingData.m b/generateTrainingData.m
deleted file mode 100644
index 6cc8601..0000000
--- a/generateTrainingData.m
+++ /dev/null
@@ -1,23 +0,0 @@
-function [trainingDataEEG,trainingDataEMG] = generateTrainingData(signal,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq)
-    frequency=params.SamplingRate.NumericValue;
-    signalWindowEMG=frequency*windowEMG;
-    signalWindowEEG=frequency*windowEEG;
-    tempEEG=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shift+1),maxEEGFreq-minEEGFreq+1]);
-    tempEMG=zeros(fix([size(signal,2)-3-32,fix(size(signal,1)/signalWindowEMG-1)*windowEMG/shift+1]));
-    
-    %Filter around 50Hz and below 2 Hz
-    [A,B]= butter(2,[48 52]/(frequency/2),'stop');
-    [C,D]= butter(2,1/(frequency/2),'high');
-    [E,F]= butter(2,[148 152]/(frequency/2),'stop');
-
-    parfor i=1:32 %filter single channel, w/o HEOG, Synchro and 0s
-        tempEEG(i,:,:)=shiftingPburg(filtfilt(double(E),double(F),filtfilt(double(C),double(D),filtfilt(double(A),double(B),double(signal(:,i))))),frequency,windowEEG,shift,pburgOrder,minEEGFreq,maxEEGFreq);
-        %filtfilt stat filter
-    end % TODO: 8-30Hz, pburg bei kurzen Fenstern (Ordnung: Je nach SamplingRate, bei 2500: 32-50, Alpha, Beta peaks sollten sichtbar sein)
-    trainingDataEEG=permute(tempEEG,[2 1 3]);
-    
-    parfor i=33:size(signal,2)-3
-        tempEMG(i-32,:,:)=waveformLength(filter(C,D,filter(A,B,signal(:,i))),frequency,windowEMG,shift);
-    end
-    trainingDataEMG=permute(tempEMG,[2 1 3]);
-end
diff --git a/kfoldCV.m b/kfoldCV.m
deleted file mode 100644
index 94f27d8..0000000
--- a/kfoldCV.m
+++ /dev/null
@@ -1,32 +0,0 @@
-function [model,maxC] = kfoldCV(classification,trainingData,k,cExpMax,maxDataPerClass)
-    noClasses=size(unique(classification),1);
-if cExpMax>0 %CV only if necessary
-    cvAccurancy=zeros(2*cExpMax+1,1);
-    parfor cExp=1:2*cExpMax+1
-        c=2^(cExp-cExpMax-1);
-
-        randomMapping=transpose(randperm(size(trainingData,1)));
-        accurancy=zeros(k,3);
-
-        for i=1:k
-            trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
-            testData=trainingData(mod(randomMapping,k)+1==i,:,:);
-            trainClasses=classification(mod(randomMapping,k)+1~=i);
-            testClasses=classification(mod(randomMapping,k)+1==i);
-            %fprintf('i=%i, k=%i, c=%i\n',i,k,c)
-            [trainClasses,trainData]=balanceClasses(trainClasses,trainData,maxDataPerClass,noClasses);
-            model=svmtrain(trainClasses,trainData(:,:),sprintf('-t 0 -c %f -q',c));
-            [~, accurancy(i,:), ~]=svmpredict(testClasses, testData(:,:), model,'-q');
-        end
-
-        cvAccurancy(cExp)=mean(accurancy(:,1));
-    end
-    [~,maxC]=max(cvAccurancy);
-else
-    maxC=1; %no gridsearch since only one C possible
-end
-    bestC=2^(maxC-cExpMax-1);
-    [balancedClasses, balanceData]=balanceClasses(classification,trainingData,maxDataPerClass,noClasses);
-    model=svmtrain(balancedClasses, balanceData(:,:),sprintf('-t 0 -c %f -q',bestC));
-
-end
diff --git a/myFilter.m b/myFilter.m
deleted file mode 100644
index 8f6c13d..0000000
--- a/myFilter.m
+++ /dev/null
@@ -1,26 +0,0 @@
-function [ filteredSig, pwMeanMove, pwMeanRest, hz ] = myFilter( splitSignals,code,NFFT )
-    %Filter around 50Hz and below 2 Hz
-    [A,B]= butter(2,[48 52]/1250,'stop');
-    [C,D]= butter(2,2/1250,'high');
-    
-    
-    filteredSig=zeros(size(splitSignals));
-    pwMeanMove=zeros(NFFT/2+1,size(splitSignals,2));
-    pwMeanRest=zeros(NFFT/2+1,size(splitSignals,2));
-
-    for i=1:size(splitSignals,2) %filter single channel
-        for j=1:size(splitSignals,3) %filter single trial
-            filteredSig(:,i,j)=filter(C,D,filter(A,B,splitSignals(:,i,j)));
-            if code(j)<=5
-                pwMeanMove(:,i)=pwMeanMove(:,i)+pwelch(filteredSig(:,i,j),[],[],NFFT,2500);
-            elseif code(j) > 5
-                pwMeanRest(:,i)=pwMeanRest(:,i)+pwelch(filteredSig(:,i,j),[],[],NFFT,2500);
-            end
-            %s=cat(1,s,size(pwelch(filteredSig(:,i,j),[],[],[],2500)));
-        end
-        pwMeanMove(:,i)=pwMeanMove(:,i) /size(find(code <=5),1);
-        pwMeanRest(:,i)=pwMeanRest(:,i) /size(find(code > 5),1);
-    end
-    [a,hz]=pwelch(filteredSig(:,1,1),[],[],NFFT,2500);
-end
-
diff --git a/oldMcode/analyze.m b/oldMcode/analyze.m
new file mode 100644
index 0000000..dbcc954
--- /dev/null
+++ b/oldMcode/analyze.m
@@ -0,0 +1,24 @@
+[signal, states, params] = load_bcidat('/home/jph/Uni/Masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R01.dat');
+
+% [ splitSignals, code ] = cutAll( states, signal );
+[ splitSignals, code ] = cutWindows( states, signal, params, 1);
+
+NFFT=2048; % should be 2^n
+
+[ filteredSig, pwMeanMove, pwMeanRest,hz ] = myFilter( splitSignals,code,NFFT );
+
+%plotChannelProzDiff(pwMeanMove,pwMeanRest,hz,params);
+%plotChannels(pwMeanMove,hz,params);
+plotImgScProzDiff( pwMeanMove, pwMeanRest)
+
+% figure
+% subplot(2,1,1)
+% plot(log(pwMeanMove))
+% xlim([0 150])
+% %ylim([-2 6])
+% subplot(2,1,2)
+% plot(log(pwMeanRest))
+% xlim([0 150])
+% %ylim([-2 6])
+
+    
\ No newline at end of file
diff --git a/oldMcode/balance2Classes.m b/oldMcode/balance2Classes.m
new file mode 100644
index 0000000..f4bae25
--- /dev/null
+++ b/oldMcode/balance2Classes.m
@@ -0,0 +1,14 @@
+function [trainClasses,trainData]=balance2Classes(trainClasses,trainData)
+    %balances two classes (0,1)
+    eps=0.00001;
+    while mean(trainClasses)> 0.5+eps || mean(trainClasses) < 0.5-eps
+        r=floor(0.5*size(trainClasses)*rand(1)+1);
+        if mean(trainClasses) < 0.5
+            more=find(trainClasses==0);
+        else
+            more=find(trainClasses==1);
+        end
+        trainClasses=trainClasses([1:more(r)-1,more(r)+1:end]);
+        trainData=trainData([1:more(r)-1,more(r)+1:end],:);
+    end
+end
\ No newline at end of file
diff --git a/oldMcode/cutAll.m b/oldMcode/cutAll.m
new file mode 100644
index 0000000..123d7b6
--- /dev/null
+++ b/oldMcode/cutAll.m
@@ -0,0 +1,16 @@
+function [ splitSignals, code ] = cutAll( states, signal )
+% cut all in same length by dropping the tail
+
+changeIndices=[find(diff(double(states.StimulusCode)));size(states.StimulusCode,1)];
+codeCount=[diff(changeIndices); size(states.StimulusCode,1)-sum(diff(changeIndices))];
+
+splitSignals=[];
+code =zeros(size(codeCount,1));
+c=codeCount(1);
+
+for i=2:size(codeCount,1)
+    splitSignals=cat(3,splitSignals,signal(c:c+4998,:));
+    code(i)=states.StimulusCode(c);
+    c=c+codeCount(i);
+end
+end
\ No newline at end of file
diff --git a/oldMcode/cutMovements.m b/oldMcode/cutMovements.m
new file mode 100644
index 0000000..bcd0132
--- /dev/null
+++ b/oldMcode/cutMovements.m
@@ -0,0 +1,22 @@
+function [ splitSignals, code ] = cutMovements( stimulusCodes, signal )
+%cut Trials only, ignore resting phases
+
+noSamplesPerTrial=15000;
+% count how many recorded values belong to one trial
+changeIndices=[find(diff(double(stimulusCodes)));size(stimulusCodes,1)];
+codeCount=[diff(changeIndices); size(stimulusCodes,1)-sum(diff(changeIndices))];
+
+splitSignals=[];
+code=[];
+
+c=1;
+
+for i=1:size(codeCount,1)
+    if(codeCount(i)==noSamplesPerTrial && stimulusCodes(c)~=0)
+        splitSignals=cat(3,splitSignals,signal(c:c+codeCount(i)-1,:));
+        code=cat(1,code,stimulusCodes(c));
+    end
+    c=c+codeCount(i);
+end
+end
+
diff --git a/oldMcode/cutSingleWindow.m b/oldMcode/cutSingleWindow.m
new file mode 100644
index 0000000..526a8bf
--- /dev/null
+++ b/oldMcode/cutSingleWindow.m
@@ -0,0 +1,18 @@
+function [ splitSignals, code ] = cutSingleWindow( stimulusCodes, signal, params, t )
+    changeIndices=cat(1,0,find(diff(double(stimulusCodes))));
+    codeCount=diff(changeIndices);
+    interval=t*params.SamplingRate.NumericValue;
+
+    splitSignals=[];
+    code=[];
+
+    c=codeCount(1)+1;
+    %cut windows of 1s
+    for i=2:(size(codeCount,1)) %exclude beginning and end (stimulus 0)
+        if codeCount(i) >= interval
+            splitSignals=cat(3,splitSignals,signal(c:c+interval-1,:));
+            code=cat(1,code,double(stimulusCodes(c)));
+        end
+        c=c+codeCount(i);
+    end
+end
diff --git a/oldMcode/cutTrainingData.m b/oldMcode/cutTrainingData.m
new file mode 100644
index 0000000..413658c
--- /dev/null
+++ b/oldMcode/cutTrainingData.m
@@ -0,0 +1,29 @@
+load('/home/jph/Uni/masterarbeit/data/AO1.mat');
+
+NFFT=2048;
+cutWindowEEG=2;
+window=0.2;
+shift=0.05;
+k=10;
+maxExpC=7;
+
+%[ splitSignals, code ] = cutMovements( stimulusCodes, signal );
+[ splitSignals, code ] = cutSingleWindow( stimulusCodes, signal, params, cutWindowEEG );
+
+%trainingData has format trial x ch x powerSpectrum
+[trainingDataEEG,trainingDataEMG]=generateTrainingData(splitSignals,NFFT,window,shift,params,cutWindowEEG);
+
+%%classify for different movements and rest
+%classification=zeros(size(code));
+% for i=1:size(code)
+%     if code(i)<=5
+%         classification(i)=code(i);
+%     else
+%         classification(i)=0;
+%     end
+% end
+
+
+%classify for move/rest
+% can't use cutMovements then
+classification=double(code<=5);
diff --git a/oldMcode/cutWindows.m b/oldMcode/cutWindows.m
new file mode 100644
index 0000000..61b5b46
--- /dev/null
+++ b/oldMcode/cutWindows.m
@@ -0,0 +1,21 @@
+function [ splitSignals, code ] = cutWindows( stimulusCodes, signal, params, t )
+    changeIndices=cat(1,0,find(diff(double(stimulusCodes))));
+    codeCount=diff(changeIndices);
+    interval=t*params.SamplingRate.NumericValue;
+
+    splitSignals=[];
+    code=[];
+
+    c=codeCount(1)+1;
+    %cut windows of 1s
+    for i=2:(size(codeCount,1)) %exclude beginning and end (stimulus 0)
+        tempc=0;
+        while codeCount(i)-tempc >= interval
+            splitSignals=cat(3,splitSignals,signal(c+tempc:c+tempc+interval-1,:));
+            code=cat(1,code,double(stimulusCodes(c+tempc)));
+            tempc=tempc+interval;
+        end
+        c=c+codeCount(i);
+    end
+end
+
diff --git a/oldMcode/myFilter.m b/oldMcode/myFilter.m
new file mode 100644
index 0000000..8f6c13d
--- /dev/null
+++ b/oldMcode/myFilter.m
@@ -0,0 +1,26 @@
+function [ filteredSig, pwMeanMove, pwMeanRest, hz ] = myFilter( splitSignals,code,NFFT )
+    %Filter around 50Hz and below 2 Hz
+    [A,B]= butter(2,[48 52]/1250,'stop');
+    [C,D]= butter(2,2/1250,'high');
+    
+    
+    filteredSig=zeros(size(splitSignals));
+    pwMeanMove=zeros(NFFT/2+1,size(splitSignals,2));
+    pwMeanRest=zeros(NFFT/2+1,size(splitSignals,2));
+
+    for i=1:size(splitSignals,2) %filter single channel
+        for j=1:size(splitSignals,3) %filter single trial
+            filteredSig(:,i,j)=filter(C,D,filter(A,B,splitSignals(:,i,j)));
+            if code(j)<=5
+                pwMeanMove(:,i)=pwMeanMove(:,i)+pwelch(filteredSig(:,i,j),[],[],NFFT,2500);
+            elseif code(j) > 5
+                pwMeanRest(:,i)=pwMeanRest(:,i)+pwelch(filteredSig(:,i,j),[],[],NFFT,2500);
+            end
+            %s=cat(1,s,size(pwelch(filteredSig(:,i,j),[],[],[],2500)));
+        end
+        pwMeanMove(:,i)=pwMeanMove(:,i) /size(find(code <=5),1);
+        pwMeanRest(:,i)=pwMeanRest(:,i) /size(find(code > 5),1);
+    end
+    [a,hz]=pwelch(filteredSig(:,1,1),[],[],NFFT,2500);
+end
+
diff --git a/oldMcode/plotChannelDiff.m b/oldMcode/plotChannelDiff.m
new file mode 100644
index 0000000..e3e0e01
--- /dev/null
+++ b/oldMcode/plotChannelDiff.m
@@ -0,0 +1,17 @@
+function [] = plotChannelDiff( pwMeanMove, pwMeanRest,hz, params )
+
+for i=1:size(params.ChannelNames.Value,1)
+    n=mod(i,9);
+    if n==1
+        figure
+    elseif n==0;
+        n=9;
+    end
+    subplot(3,3,n);
+    plot(hz,pwMeanMove(:,i)-pwMeanRest(:,i))
+    xlim([0 150])
+    title(params.ChannelNames.Value(i))
+end
+
+end
+
diff --git a/oldMcode/plotChannelProzDiff.m b/oldMcode/plotChannelProzDiff.m
new file mode 100644
index 0000000..b57bd79
--- /dev/null
+++ b/oldMcode/plotChannelProzDiff.m
@@ -0,0 +1,17 @@
+function [] = plotChannelProzDiff( pwMeanMove, pwMeanRest,hz, params )
+
+for i=1:size(params.ChannelNames.Value,1)
+    n=mod(i,9);
+    if n==1
+        figure
+    elseif n==0;
+        n=9;
+    end
+    subplot(3,3,n);
+    plot(hz,(pwMeanMove(:,i)-pwMeanRest(:,i))./pwMeanRest(:,i))
+    xlim([0 150])
+    title(params.ChannelNames.Value(i))
+end
+
+end
+
diff --git a/oldMcode/plotChannels.m b/oldMcode/plotChannels.m
new file mode 100644
index 0000000..9126ec1
--- /dev/null
+++ b/oldMcode/plotChannels.m
@@ -0,0 +1,17 @@
+function [] = plotChannels( pwMean, hz, params )
+
+for i=1:size(params.ChannelNames.Value,1)
+    n=mod(i,9);
+    if n==1
+        figure
+    elseif n==0;
+        n=9;
+    end
+    subplot(3,3,n);
+    plot(log(hz),log(pwMean(:,i,:)))
+    xlim([0 150])
+    title(params.ChannelNames.Value(i))
+end
+
+end
+
diff --git a/oldMcode/plotImgScProzDiff.m b/oldMcode/plotImgScProzDiff.m
new file mode 100644
index 0000000..a1e6b34
--- /dev/null
+++ b/oldMcode/plotImgScProzDiff.m
@@ -0,0 +1,8 @@
+function [] = plotImgScProzDiff( pwMeanMove, pwMeanRest)
+
+prozDiff=(pwMeanMove-pwMeanRest)./pwMeanRest;
+imagesc(1:1.22:100,1:32,transpose(prozDiff(1:82,1:32)))
+colorbar();
+
+end
+
diff --git a/oldMcode/plotStairs.m b/oldMcode/plotStairs.m
new file mode 100644
index 0000000..ee7577a
--- /dev/null
+++ b/oldMcode/plotStairs.m
@@ -0,0 +1,10 @@
+function [ ] = plotStairs( inVec )
+%plotStairs plots Stairs for Vector
+%   plots sum up to Index i as value for i
+    stair=zeros(size(inVec));
+    for i=1:size(classification,1)
+        stair(i)=sum(inVec(1:i));
+    end
+    plot(stairs)
+end
+
diff --git a/oldMcode/plots/cmEMGAO1200msWindow1sPause.fig b/oldMcode/plots/cmEMGAO1200msWindow1sPause.fig
new file mode 100644
index 0000000..8da1819
--- /dev/null
+++ b/oldMcode/plots/cmEMGAO1200msWindow1sPause.fig
Binary files differ
diff --git a/oldMcode/plots/emgHist.fig b/oldMcode/plots/emgHist.fig
new file mode 100644
index 0000000..e9e770a
--- /dev/null
+++ b/oldMcode/plots/emgHist.fig
Binary files differ
diff --git a/oldMcode/plots/imgSc.jpg b/oldMcode/plots/imgSc.jpg
new file mode 100644
index 0000000..8fa50af
--- /dev/null
+++ b/oldMcode/plots/imgSc.jpg
Binary files differ
diff --git a/oldMcode/plots/mitFilter13.fig b/oldMcode/plots/mitFilter13.fig
new file mode 100644
index 0000000..e12cb52
--- /dev/null
+++ b/oldMcode/plots/mitFilter13.fig
Binary files differ
diff --git a/oldMcode/plots/ohneFilter13.fig b/oldMcode/plots/ohneFilter13.fig
new file mode 100644
index 0000000..45b6eec
--- /dev/null
+++ b/oldMcode/plots/ohneFilter13.fig
Binary files differ
diff --git a/oldMcode/plots/prozDiff1.fig b/oldMcode/plots/prozDiff1.fig
new file mode 100644
index 0000000..f862267
--- /dev/null
+++ b/oldMcode/plots/prozDiff1.fig
Binary files differ
diff --git a/oldMcode/plots/prozDiff2.fig b/oldMcode/plots/prozDiff2.fig
new file mode 100644
index 0000000..8ee7748
--- /dev/null
+++ b/oldMcode/plots/prozDiff2.fig
Binary files differ
diff --git a/oldMcode/plots/prozDiff3.fig b/oldMcode/plots/prozDiff3.fig
new file mode 100644
index 0000000..5394b69
--- /dev/null
+++ b/oldMcode/plots/prozDiff3.fig
Binary files differ
diff --git a/oldMcode/plots/prozDiff4.fig b/oldMcode/plots/prozDiff4.fig
new file mode 100644
index 0000000..3bcf571
--- /dev/null
+++ b/oldMcode/plots/prozDiff4.fig
Binary files differ
diff --git a/oldMcode/plots/prozDiff5.fig b/oldMcode/plots/prozDiff5.fig
new file mode 100644
index 0000000..5635ff3
--- /dev/null
+++ b/oldMcode/plots/prozDiff5.fig
Binary files differ
diff --git a/oldMcode/plots/sig13.fig b/oldMcode/plots/sig13.fig
new file mode 100644
index 0000000..68fe997
--- /dev/null
+++ b/oldMcode/plots/sig13.fig
Binary files differ
diff --git a/oldMcode/plots/wlHist.fig b/oldMcode/plots/wlHist.fig
new file mode 100644
index 0000000..e3bdf1f
--- /dev/null
+++ b/oldMcode/plots/wlHist.fig
Binary files differ
diff --git a/oldMcode/shiftingPwelch.m b/oldMcode/shiftingPwelch.m
new file mode 100644
index 0000000..c0dc716
--- /dev/null
+++ b/oldMcode/shiftingPwelch.m
@@ -0,0 +1,9 @@
+function [EEG] = shiftingPwelch(signal,frequency,window,windowShift,NFFT)
+    signalShift=frequency*windowShift;
+    signalWindow=frequency*window;
+    shiftProp=window/windowShift;
+    EEG=zeros([fix(floor(size(signal,1)/signalWindow)-1)*shiftProp+1,NFFT/2+1]);
+    for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
+        EEG(j,:)=pwelch(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow),[],[],NFFT,2500);
+    end
+end
\ No newline at end of file
diff --git a/oldMcode/svm.m b/oldMcode/svm.m
new file mode 100644
index 0000000..88e36fa
--- /dev/null
+++ b/oldMcode/svm.m
@@ -0,0 +1,44 @@
+close all
+clear variables
+clc
+
+load('/home/Uni/masterarbeit/data/AO1200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat');
+
+%k=2; 
+%maxExpC=0; % c\in {2^i|i=-maxExpC:1:maxExpC}
+
+%choose to estimate based on EEG or EMG
+trainingData=trainingDataEMG;
+clear trainingDataEEG;
+clear trainingDataEMG;
+
+accurancy=zeros(k,3);
+maxC=zeros(k,1);
+noClasses=size(unique(classification),1);
+cm=zeros(noClasses);
+randMap=randperm(size(trainingData,1));
+disp('startCV')
+for i=1:k
+    leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
+    leaveClasses=classification(mod(randMap,k)==i-1);
+    remaining=trainingData(mod(randMap,k)~=i-1,:,:);
+    remainingClasses=classification(mod(randMap,k)~=i-1);
+    disp(datestr(datetime('now')))
+    fprintf('create %ith model\n',i)
+    [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,1000);
+    disp(datestr(datetime('now')))
+    
+    [predictions,accurancy(i,:),pvalues]=svmpredict(leaveClasses,leaveOut(:,:),model);
+    cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
+    %cf: for each cue what was the prediction (maybe colourCoded)
+end
+meanAccurancy=mean(accurancy(:,1))
+
+fig=figure;
+cmScaled=zeros(size(cm));
+for i=1:size(cm,1)
+    cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
+end
+imagesc(cmScaled)
+colorbar();
+saveas(fig,'/home/Uni/masterarbeit/plots/cmEMG200ms1sPause.fig','fig');
diff --git a/papers/2015-sarasola-sanz-EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies.pdf b/papers/2015-sarasola-sanz-EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies.pdf
new file mode 100644
index 0000000..077f0c6
--- /dev/null
+++ b/papers/2015-sarasola-sanz-EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies.pdf
Binary files differ
diff --git a/papers/2015-shiman-Towards Decoding of Functional Movements from the Same Limb using EEG.pdf b/papers/2015-shiman-Towards Decoding of Functional Movements from the Same Limb using EEG.pdf
new file mode 100644
index 0000000..ac26fcf
--- /dev/null
+++ b/papers/2015-shiman-Towards Decoding of Functional Movements from the Same Limb using EEG.pdf
Binary files differ
diff --git a/papers/2016-spueler-Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients.pdf b/papers/2016-spueler-Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients.pdf
new file mode 100644
index 0000000..33278f5
--- /dev/null
+++ b/papers/2016-spueler-Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients.pdf
Binary files differ
diff --git a/plotChannelDiff.m b/plotChannelDiff.m
deleted file mode 100644
index e3e0e01..0000000
--- a/plotChannelDiff.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function [] = plotChannelDiff( pwMeanMove, pwMeanRest,hz, params )
-
-for i=1:size(params.ChannelNames.Value,1)
-    n=mod(i,9);
-    if n==1
-        figure
-    elseif n==0;
-        n=9;
-    end
-    subplot(3,3,n);
-    plot(hz,pwMeanMove(:,i)-pwMeanRest(:,i))
-    xlim([0 150])
-    title(params.ChannelNames.Value(i))
-end
-
-end
-
diff --git a/plotChannelProzDiff.m b/plotChannelProzDiff.m
deleted file mode 100644
index b57bd79..0000000
--- a/plotChannelProzDiff.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function [] = plotChannelProzDiff( pwMeanMove, pwMeanRest,hz, params )
-
-for i=1:size(params.ChannelNames.Value,1)
-    n=mod(i,9);
-    if n==1
-        figure
-    elseif n==0;
-        n=9;
-    end
-    subplot(3,3,n);
-    plot(hz,(pwMeanMove(:,i)-pwMeanRest(:,i))./pwMeanRest(:,i))
-    xlim([0 150])
-    title(params.ChannelNames.Value(i))
-end
-
-end
-
diff --git a/plotChannels.m b/plotChannels.m
deleted file mode 100644
index 9126ec1..0000000
--- a/plotChannels.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function [] = plotChannels( pwMean, hz, params )
-
-for i=1:size(params.ChannelNames.Value,1)
-    n=mod(i,9);
-    if n==1
-        figure
-    elseif n==0;
-        n=9;
-    end
-    subplot(3,3,n);
-    plot(log(hz),log(pwMean(:,i,:)))
-    xlim([0 150])
-    title(params.ChannelNames.Value(i))
-end
-
-end
-
diff --git a/plotImgScProzDiff.m b/plotImgScProzDiff.m
deleted file mode 100644
index a1e6b34..0000000
--- a/plotImgScProzDiff.m
+++ /dev/null
@@ -1,8 +0,0 @@
-function [] = plotImgScProzDiff( pwMeanMove, pwMeanRest)
-
-prozDiff=(pwMeanMove-pwMeanRest)./pwMeanRest;
-imagesc(1:1.22:100,1:32,transpose(prozDiff(1:82,1:32)))
-colorbar();
-
-end
-
diff --git a/plotStairs.m b/plotStairs.m
deleted file mode 100644
index ee7577a..0000000
--- a/plotStairs.m
+++ /dev/null
@@ -1,10 +0,0 @@
-function [ ] = plotStairs( inVec )
-%plotStairs plots Stairs for Vector
-%   plots sum up to Index i as value for i
-    stair=zeros(size(inVec));
-    for i=1:size(classification,1)
-        stair(i)=sum(inVec(1:i));
-    end
-    plot(stairs)
-end
-
diff --git a/read.m b/read.m
deleted file mode 100644
index f7d1925..0000000
--- a/read.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function readEEG(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
-
-    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
-
-    trainingDataEEGcell=cell(maxFile,1);
-    trainingDataEMGcell=cell(maxFile,1);
-    classesCell=cell(maxFile,1);
-
-    for i=1:maxFile
-        [sig, stat, params] = load_bcidat(sprintf('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/%s/%s_B100%i/AO_B1S001R0%i.dat',subject,subject,number,i));
-        [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
-        classesCell{i}=stat.StimulusCode;
-        fprintf('%ith file processed\n',i)
-    end
-
-    clear sig
-
-    trainingDataEEG=cell2mat(trainingDataEEGcell);
-    trainingDataEMG=cell2mat(trainingDataEMGcell);
-    classesMat=cell2mat(classesCell);
-    clear trainingDataEEGcell trainingDataEMGcell classesCell
-
-    classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
-    clear classesMat
-
-    smoothClassification=zeros(size(classificationWithPause));
-    for i=1:size(classificationWithPause,1)
-        smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
-    end
-
-    clear classificationWithPause
-
-    trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
-    trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
-    classification=smoothClassification(smoothClassification~=-1);
-
-    clear smoothClassification i
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),trainingDataEEG,trainingDataEMG,classification);
-    
-
-    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
-end
\ No newline at end of file
diff --git a/readEEGEciton.m b/readEEGEciton.m
deleted file mode 100644
index 0a527cb..0000000
--- a/readEEGEciton.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function readEEGEciton(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
-
-    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
-
-    trainingDataEEGcell=cell(maxFile,1);
-    trainingDataEMGcell=cell(maxFile,1);
-    classesCell=cell(maxFile,1);
-
-    for i=1:maxFile
-        [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/origData/%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,i));
-        [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
-        classesCell{i}=stat.StimulusCode;
-        fprintf('%ith file processed\n',i)
-    end
-
-    clear sig
-
-    trainingDataEEG=cell2mat(trainingDataEEGcell);
-    trainingDataEMG=cell2mat(trainingDataEMGcell);
-    classesMat=cell2mat(classesCell);
-    clear trainingDataEEGcell trainingDataEMGcell classesCell
-
-    classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
-    clear classesMat
-
-    smoothClassification=zeros(size(classificationWithPause));
-    for i=1:size(classificationWithPause,1)
-        smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
-    end
-
-    clear classificationWithPause
-
-    trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
-    trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
-    classification=smoothClassification(smoothClassification~=-1);
-
-    clear smoothClassification i
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),'trainingDataEEG','trainingDataEMG','classification','-v7.3');
-    
-
-    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
-end
diff --git a/readEciton.m b/readEciton.m
deleted file mode 100644
index 205a4ca..0000000
--- a/readEciton.m
+++ /dev/null
@@ -1,55 +0,0 @@
-close all
-clear variables
-%params
-disp('start')
-disp(datestr(datetime('now')))
-windowEMG=0.2;
-windowEEG=1;
-shift=0.2;
-k=10;
-maxExpC=5;
-maxFile=5;
-threshold=7500;
-pburgOrder=32;
-minEEGFreq=0; %Hz
-maxEEGFreq=200; %Hz
-
-
-trainingDataEEGcell=cell(maxFile,1);
-trainingDataEMGcell=cell(maxFile,1);
-classesCell=cell(maxFile,1);
-
-parfor i=1:maxFile
-    [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R0%i.dat',i));
-    [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
-    classesCell{i}=stat.StimulusCode;
-    fprintf('%ith file processed\n',i)
-end
-
-clear sig
-
-trainingDataEEG=cell2mat(trainingDataEEGcell);
-trainingDataEMG=cell2mat(trainingDataEMGcell);
-classesMat=cell2mat(classesCell);
-clear trainingDataEEGcell trainingDataEMGcell classesCell
-
-classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
-clear classesMat
-
-smoothClassification=zeros(size(classificationWithPause));
-for i=1:size(classificationWithPause,1)
-    smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
-end
-
-clear classificationWithPause
-
-trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
-trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
-classification=smoothClassification(smoothClassification~=-1);
-
-clear smoothClassification i
-save /nfs/wsi/ti/messor/hohlochj/matlabData/AO1200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat
-
-
-disp('end')
-disp(datestr(datetime('now')))
diff --git a/runSvm.bash b/runSvm.bash
deleted file mode 100755
index e28cb28..0000000
--- a/runSvm.bash
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-git pull
-
-DATE=$(date +%y-%m-%d--%T)
-matlab -nosplash -nodisplay -nodesktop < svm.m > log/$DATE.log 
-
-git add log/$DATE.log
-git commit -m "add log for $DATE" && git pull && git push
diff --git a/shiftingPburg.m b/shiftingPburg.m
deleted file mode 100644
index e43fbaa..0000000
--- a/shiftingPburg.m
+++ /dev/null
@@ -1,12 +0,0 @@
-function [ EEG ] = shiftingPburg( signal,frequency,windowEEG,shift,order,minEEGFreq,maxEEGFreq)
-%shiftingPburg uses pburg to calculate the power spectrum
-%   Detailed explanation goes here
-    signalShift=frequency*shift;
-    signalWindow=frequency*windowEEG;
-    shiftProp=windowEEG/shift;
-    EEG=zeros([fix(floor(size(signal,1)/signalWindow)-1)*shiftProp+1,fix(maxEEGFreq-minEEGFreq+1)]);
-    for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
-        [EEG(j,:),~]=pburg(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow),order,minEEGFreq:maxEEGFreq,frequency);
-    end
-end
-
diff --git a/shiftingPwelch.m b/shiftingPwelch.m
deleted file mode 100644
index c0dc716..0000000
--- a/shiftingPwelch.m
+++ /dev/null
@@ -1,9 +0,0 @@
-function [EEG] = shiftingPwelch(signal,frequency,window,windowShift,NFFT)
-    signalShift=frequency*windowShift;
-    signalWindow=frequency*window;
-    shiftProp=window/windowShift;
-    EEG=zeros([fix(floor(size(signal,1)/signalWindow)-1)*shiftProp+1,NFFT/2+1]);
-    for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
-        EEG(j,:)=pwelch(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow),[],[],NFFT,2500);
-    end
-end
\ No newline at end of file
diff --git a/stimuliMatrix.txt b/stimuliMatrix.txt
deleted file mode 100644
index 091d456..0000000
--- a/stimuliMatrix.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-BlueGo	RedGo	GreenGo	BrownGo	PinkGo	REST	REST	REST	REST	REST	REST	REST	REST	REST	REST	%
-%	%	%	%	%	%	%	%	%	%	%	%	%	%	%	%
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diff --git a/structForAllDays.m b/structForAllDays.m
deleted file mode 100644
index d8c5f3a..0000000
--- a/structForAllDays.m
+++ /dev/null
@@ -1,21 +0,0 @@
-function [names,numbers]=structForAllDays(dirs)
-    fileID=fopen(dirs);
-    text=textscan(fileID,'%s','Delimiter','\n');
-    text=text{1};
-    j=0;
-    k=0;
-    names=cell(1);
-    for i=1:size(text)
-        tmp=char(text(i));
-        name=strcat(tmp(1:2));
-        if (j~=0 && all(squeeze(names{j}==name)))
-            k=k+1;
-            numbers{j}(k)=str2double(tmp(end));
-        else
-            j=j+1;
-            names{j}=name;
-            k=1;
-            numbers{j}(k)=str2double(tmp(end));
-        end
-    end
-end
\ No newline at end of file
diff --git a/svm.m b/svm.m
deleted file mode 100644
index 88e36fa..0000000
--- a/svm.m
+++ /dev/null
@@ -1,44 +0,0 @@
-close all
-clear variables
-clc
-
-load('/home/Uni/masterarbeit/data/AO1200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat');
-
-%k=2; 
-%maxExpC=0; % c\in {2^i|i=-maxExpC:1:maxExpC}
-
-%choose to estimate based on EEG or EMG
-trainingData=trainingDataEMG;
-clear trainingDataEEG;
-clear trainingDataEMG;
-
-accurancy=zeros(k,3);
-maxC=zeros(k,1);
-noClasses=size(unique(classification),1);
-cm=zeros(noClasses);
-randMap=randperm(size(trainingData,1));
-disp('startCV')
-for i=1:k
-    leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
-    leaveClasses=classification(mod(randMap,k)==i-1);
-    remaining=trainingData(mod(randMap,k)~=i-1,:,:);
-    remainingClasses=classification(mod(randMap,k)~=i-1);
-    disp(datestr(datetime('now')))
-    fprintf('create %ith model\n',i)
-    [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,1000);
-    disp(datestr(datetime('now')))
-    
-    [predictions,accurancy(i,:),pvalues]=svmpredict(leaveClasses,leaveOut(:,:),model);
-    cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
-    %cf: for each cue what was the prediction (maybe colourCoded)
-end
-meanAccurancy=mean(accurancy(:,1))
-
-fig=figure;
-cmScaled=zeros(size(cm));
-for i=1:size(cm,1)
-    cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
-end
-imagesc(cmScaled)
-colorbar();
-saveas(fig,'/home/Uni/masterarbeit/plots/cmEMG200ms1sPause.fig','fig');
diff --git a/svmEciton.m b/svmEciton.m
deleted file mode 100644
index bfb1733..0000000
--- a/svmEciton.m
+++ /dev/null
@@ -1,52 +0,0 @@
-function svmEciton(subject,number,EEG,k,maxExpC,maxPerClass)
-    load(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number));
-%    fprintf('%i,%i,%i',size(trainingDataEMG,1),size(trainingDataEEG,1),size(classification,1))
-    addpath('/nfs/wsi/ti/messor/hohlochj/libsvm/matlab');
-    %k=2;
-    %maxExpC=0; % c\in {2^i|i=-maxExpC:1:maxExpC}
-
-    %choose to estimate based on EEG or EMG
-    if EEG
-        trainingData=trainingDataEEG;
-    else
-        trainingData=trainingDataEMG;
-    end
-    clear trainingDataEEG;
-    clear trainingDataEMG;
-%    addAttachedFiles(poolObj,'classification');
-%    addAttachedFiles(poolObj,'trainingData');
-
-    accurancy=zeros(k,3);
-    maxC=zeros(k,1);
-    noClasses=size(unique(classification),1);
-    cm=zeros(noClasses);
-    randMap=randperm(size(trainingData,1));
-    disp('startCV')
-    classes=classification; %necessary since otherwise classes are not passed to workers
-    clear classification;
-    parfor i=1:k
-        leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
-        leaveClasses=classes(mod(randMap,k)==i-1);
-        remaining=trainingData(mod(randMap,k)~=i-1,:,:);
-        remainingClasses=classes(mod(randMap,k)~=i-1);
-        fprintf('%s create %ith model\n',datestr(datetime('now')),i)
-        
-        [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,maxPerClass);
-        disp(datestr(datetime('now')))
-
-        [predictions,accurancy(i,:),~]=svmpredict(leaveClasses,leaveOut(:,:),model);
-        cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
-        %cf: for each cue what was the prediction (maybe colourCoded)
-    end
-    meanAccurancy=mean(accurancy(:,1))
-
-    fig=figure;
-    cmScaled=zeros(size(cm));
-    for i=1:size(cm,1)
-        cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
-    end
-    imagesc(cmScaled)
-    colorbar();
-    saveas(fig,sprintf('/nfs/wsi/ti/messor/hohlochj/plots/%s%i%icm200ms1sPause.fig',subject,number,EEG),'fig');
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i%i200ms1sPause.fig',subject,number,EEG),'meanAccurancy','maxC','cmScaled','-v7.3');
-end
diff --git a/usedMcode/balanceClasses.m b/usedMcode/balanceClasses.m
new file mode 100644
index 0000000..54e7ba0
--- /dev/null
+++ b/usedMcode/balanceClasses.m
@@ -0,0 +1,20 @@
+function [trainClasses,trainData]=balanceClasses(trainClasses,trainData,maxPerClass,noClasses)
+%     if maxPerClass > min(histcounts(trainClasses,noClasses))
+%         maxPerClass=min(histcounts(trainClasses));
+%         warning('not enough Data per Class; set maxPerClass to %i', maxPerClass);
+%     end
+    i=1;
+    delPerClass=histcounts(trainClasses,noClasses)-maxPerClass;
+    delIndex=zeros(sum(delPerClass),1);
+    while sum(delPerClass)>0
+        r=fix(size(trainClasses,1)*rand(1)+1);
+        if delPerClass(trainClasses(r)+1)>0 && all(delIndex~=r)
+            delIndex(i)=r;
+            i=i+1;
+            delPerClass(trainClasses(r)+1)=delPerClass(trainClasses(r)+1)-1;
+        end
+    end
+    trainClasses(delIndex)=[];
+    trainData(delIndex,:)=[];
+    assert(size(trainClasses,1)>0)
+end
\ No newline at end of file
diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
new file mode 100644
index 0000000..aceaf65
--- /dev/null
+++ b/usedMcode/callAll.m
@@ -0,0 +1,27 @@
+addpath('/home/hohlochj/masterarbeit')
+pathToFile='/nfs/wsi/ti/messor/hohlochj/origData/dirs.txt';
+% pathToFile='/home/jph/Uni/masterarbeit/Block1_ReachingMovements/dirs.txt';
+
+windowEMG=0.2;
+windowEEG=1;
+shift=0.2;
+pburgOrder=50;
+minEEGFreq=0;
+maxEEGFreq=200;
+k=10;
+maxExpC=0;
+maxPerClass=250;
+EEG=true;
+poolObj=parpool(32);
+
+[subjects,numbers]=structForAllDays(pathToFile);
+for i=1:size(subjects,2)
+    subject=subjects{i};
+    for number=numbers{i}
+%         fprintf('%s%i\n',subject,number);
+        readEEGEciton(subject,number,windowEMG,windowEEG,shift,5,7500,pburgOrder,minEEGFreq,maxEEGFreq);
+        svmEciton(subject,number,false,k,maxExpC,maxPerClass);
+        svmEciton(subject,number,EEG,k,maxExpC,maxPerClass);
+    end
+end
+delete(poolObj)
diff --git a/usedMcode/chooseForCode.m b/usedMcode/chooseForCode.m
new file mode 100644
index 0000000..aa15422
--- /dev/null
+++ b/usedMcode/chooseForCode.m
@@ -0,0 +1,12 @@
+function [ splitSamplesCode ] = chooseForCode(code,codes, splitSamples)
+%chooseForCode chooses the samples for given code
+%   input the code to choose, the list of codes, the number of Samples and
+%   the list of Samples to get the list with the given code
+    splitSamplesCode=[];
+    for i=1:size(codes)
+        if code == codes(i)
+            splitSamplesCode=[splitSamplesCode,splitSamples(:,:,i)];
+        end
+    end
+end
+
diff --git a/usedMcode/classifyAccordingToEMG.m b/usedMcode/classifyAccordingToEMG.m
new file mode 100644
index 0000000..4937188
--- /dev/null
+++ b/usedMcode/classifyAccordingToEMG.m
@@ -0,0 +1,26 @@
+function [classification]=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG, classes,shift,frequency,threshold)
+% classifyAccordingToEMG data classified as -1 should be taken out
+    classification=zeros([size(trainingDataEEG,1),1]);
+    oldclass=0;
+    for i=1:size(trainingDataEEG,1)
+        if mean(trainingDataEMG(i,:)) > threshold % is movement
+            class=mode(classes(max(round(shift*frequency*i-frequency),1):min(round(i*shift*frequency-1),end)));
+            if class <= 5 % task is to move
+                if oldclass==0
+                    for j=max(fix(i-1/shift),1):fix(i-0.5/shift) % Pause before beginning of movement
+                        classification(j)=-1; %code for pause
+                    end
+                    for j=max(fix(i-0.5/shift),1):i % half a second before executed movement there should be activity (esp. EEG)
+                        classification(j)=-1; %if -1 there is a pause of 1s befor movement onset
+                    end
+                    oldclass=class;
+                end
+                classification(i)=class;
+            else % task is to rest but movement not finished
+                classification(i)=oldclass;
+            end
+        else
+            oldclass=0;
+        end
+    end
+end
\ No newline at end of file
diff --git a/usedMcode/generateTrainingData.m b/usedMcode/generateTrainingData.m
new file mode 100644
index 0000000..6cc8601
--- /dev/null
+++ b/usedMcode/generateTrainingData.m
@@ -0,0 +1,23 @@
+function [trainingDataEEG,trainingDataEMG] = generateTrainingData(signal,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq)
+    frequency=params.SamplingRate.NumericValue;
+    signalWindowEMG=frequency*windowEMG;
+    signalWindowEEG=frequency*windowEEG;
+    tempEEG=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shift+1),maxEEGFreq-minEEGFreq+1]);
+    tempEMG=zeros(fix([size(signal,2)-3-32,fix(size(signal,1)/signalWindowEMG-1)*windowEMG/shift+1]));
+    
+    %Filter around 50Hz and below 2 Hz
+    [A,B]= butter(2,[48 52]/(frequency/2),'stop');
+    [C,D]= butter(2,1/(frequency/2),'high');
+    [E,F]= butter(2,[148 152]/(frequency/2),'stop');
+
+    parfor i=1:32 %filter single channel, w/o HEOG, Synchro and 0s
+        tempEEG(i,:,:)=shiftingPburg(filtfilt(double(E),double(F),filtfilt(double(C),double(D),filtfilt(double(A),double(B),double(signal(:,i))))),frequency,windowEEG,shift,pburgOrder,minEEGFreq,maxEEGFreq);
+        %filtfilt stat filter
+    end % TODO: 8-30Hz, pburg bei kurzen Fenstern (Ordnung: Je nach SamplingRate, bei 2500: 32-50, Alpha, Beta peaks sollten sichtbar sein)
+    trainingDataEEG=permute(tempEEG,[2 1 3]);
+    
+    parfor i=33:size(signal,2)-3
+        tempEMG(i-32,:,:)=waveformLength(filter(C,D,filter(A,B,signal(:,i))),frequency,windowEMG,shift);
+    end
+    trainingDataEMG=permute(tempEMG,[2 1 3]);
+end
diff --git a/usedMcode/kfoldCV.m b/usedMcode/kfoldCV.m
new file mode 100644
index 0000000..94f27d8
--- /dev/null
+++ b/usedMcode/kfoldCV.m
@@ -0,0 +1,32 @@
+function [model,maxC] = kfoldCV(classification,trainingData,k,cExpMax,maxDataPerClass)
+    noClasses=size(unique(classification),1);
+if cExpMax>0 %CV only if necessary
+    cvAccurancy=zeros(2*cExpMax+1,1);
+    parfor cExp=1:2*cExpMax+1
+        c=2^(cExp-cExpMax-1);
+
+        randomMapping=transpose(randperm(size(trainingData,1)));
+        accurancy=zeros(k,3);
+
+        for i=1:k
+            trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
+            testData=trainingData(mod(randomMapping,k)+1==i,:,:);
+            trainClasses=classification(mod(randomMapping,k)+1~=i);
+            testClasses=classification(mod(randomMapping,k)+1==i);
+            %fprintf('i=%i, k=%i, c=%i\n',i,k,c)
+            [trainClasses,trainData]=balanceClasses(trainClasses,trainData,maxDataPerClass,noClasses);
+            model=svmtrain(trainClasses,trainData(:,:),sprintf('-t 0 -c %f -q',c));
+            [~, accurancy(i,:), ~]=svmpredict(testClasses, testData(:,:), model,'-q');
+        end
+
+        cvAccurancy(cExp)=mean(accurancy(:,1));
+    end
+    [~,maxC]=max(cvAccurancy);
+else
+    maxC=1; %no gridsearch since only one C possible
+end
+    bestC=2^(maxC-cExpMax-1);
+    [balancedClasses, balanceData]=balanceClasses(classification,trainingData,maxDataPerClass,noClasses);
+    model=svmtrain(balancedClasses, balanceData(:,:),sprintf('-t 0 -c %f -q',bestC));
+
+end
diff --git a/usedMcode/readEEG.m b/usedMcode/readEEG.m
new file mode 100644
index 0000000..f7d1925
--- /dev/null
+++ b/usedMcode/readEEG.m
@@ -0,0 +1,42 @@
+function readEEG(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
+
+    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
+
+    trainingDataEEGcell=cell(maxFile,1);
+    trainingDataEMGcell=cell(maxFile,1);
+    classesCell=cell(maxFile,1);
+
+    for i=1:maxFile
+        [sig, stat, params] = load_bcidat(sprintf('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/%s/%s_B100%i/AO_B1S001R0%i.dat',subject,subject,number,i));
+        [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
+        classesCell{i}=stat.StimulusCode;
+        fprintf('%ith file processed\n',i)
+    end
+
+    clear sig
+
+    trainingDataEEG=cell2mat(trainingDataEEGcell);
+    trainingDataEMG=cell2mat(trainingDataEMGcell);
+    classesMat=cell2mat(classesCell);
+    clear trainingDataEEGcell trainingDataEMGcell classesCell
+
+    classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
+    clear classesMat
+
+    smoothClassification=zeros(size(classificationWithPause));
+    for i=1:size(classificationWithPause,1)
+        smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
+    end
+
+    clear classificationWithPause
+
+    trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
+    trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
+    classification=smoothClassification(smoothClassification~=-1);
+
+    clear smoothClassification i
+    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),trainingDataEEG,trainingDataEMG,classification);
+    
+
+    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
+end
\ No newline at end of file
diff --git a/usedMcode/readEEGEciton.m b/usedMcode/readEEGEciton.m
new file mode 100644
index 0000000..0a527cb
--- /dev/null
+++ b/usedMcode/readEEGEciton.m
@@ -0,0 +1,42 @@
+function readEEGEciton(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
+
+    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
+
+    trainingDataEEGcell=cell(maxFile,1);
+    trainingDataEMGcell=cell(maxFile,1);
+    classesCell=cell(maxFile,1);
+
+    for i=1:maxFile
+        [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/origData/%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,i));
+        [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
+        classesCell{i}=stat.StimulusCode;
+        fprintf('%ith file processed\n',i)
+    end
+
+    clear sig
+
+    trainingDataEEG=cell2mat(trainingDataEEGcell);
+    trainingDataEMG=cell2mat(trainingDataEMGcell);
+    classesMat=cell2mat(classesCell);
+    clear trainingDataEEGcell trainingDataEMGcell classesCell
+
+    classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
+    clear classesMat
+
+    smoothClassification=zeros(size(classificationWithPause));
+    for i=1:size(classificationWithPause,1)
+        smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
+    end
+
+    clear classificationWithPause
+
+    trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
+    trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
+    classification=smoothClassification(smoothClassification~=-1);
+
+    clear smoothClassification i
+    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),'trainingDataEEG','trainingDataEMG','classification','-v7.3');
+    
+
+    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
+end
diff --git a/usedMcode/shiftingPburg.m b/usedMcode/shiftingPburg.m
new file mode 100644
index 0000000..e43fbaa
--- /dev/null
+++ b/usedMcode/shiftingPburg.m
@@ -0,0 +1,12 @@
+function [ EEG ] = shiftingPburg( signal,frequency,windowEEG,shift,order,minEEGFreq,maxEEGFreq)
+%shiftingPburg uses pburg to calculate the power spectrum
+%   Detailed explanation goes here
+    signalShift=frequency*shift;
+    signalWindow=frequency*windowEEG;
+    shiftProp=windowEEG/shift;
+    EEG=zeros([fix(floor(size(signal,1)/signalWindow)-1)*shiftProp+1,fix(maxEEGFreq-minEEGFreq+1)]);
+    for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
+        [EEG(j,:),~]=pburg(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow),order,minEEGFreq:maxEEGFreq,frequency);
+    end
+end
+
diff --git a/usedMcode/structForAllDays.m b/usedMcode/structForAllDays.m
new file mode 100644
index 0000000..d8c5f3a
--- /dev/null
+++ b/usedMcode/structForAllDays.m
@@ -0,0 +1,21 @@
+function [names,numbers]=structForAllDays(dirs)
+    fileID=fopen(dirs);
+    text=textscan(fileID,'%s','Delimiter','\n');
+    text=text{1};
+    j=0;
+    k=0;
+    names=cell(1);
+    for i=1:size(text)
+        tmp=char(text(i));
+        name=strcat(tmp(1:2));
+        if (j~=0 && all(squeeze(names{j}==name)))
+            k=k+1;
+            numbers{j}(k)=str2double(tmp(end));
+        else
+            j=j+1;
+            names{j}=name;
+            k=1;
+            numbers{j}(k)=str2double(tmp(end));
+        end
+    end
+end
\ No newline at end of file
diff --git a/usedMcode/svmEciton.m b/usedMcode/svmEciton.m
new file mode 100644
index 0000000..992191a
--- /dev/null
+++ b/usedMcode/svmEciton.m
@@ -0,0 +1,47 @@
+function svmEciton(subject,number,EEG,k,maxExpC,maxPerClass)
+    load(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number));
+%    fprintf('%i,%i,%i',size(trainingDataEMG,1),size(trainingDataEEG,1),size(classification,1))
+    addpath('/nfs/wsi/ti/messor/hohlochj/libsvm/matlab');
+
+    %choose to estimate based on EEG or EMG
+    if EEG
+        trainingData=trainingDataEEG;
+    else
+        trainingData=trainingDataEMG;
+    end
+    clear trainingDataEEG;
+    clear trainingDataEMG;
+    
+    accurancy=zeros(k,3);
+    maxC=zeros(k,1);
+    noClasses=size(unique(classification),1);
+    cm=zeros(noClasses);
+    randMap=randperm(size(trainingData,1));
+    disp('startCV')
+    classes=classification; %necessary since otherwise classes are not passed to workers
+    clear classification;
+    parfor i=1:k
+        leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
+        leaveClasses=classes(mod(randMap,k)==i-1);
+        remaining=trainingData(mod(randMap,k)~=i-1,:,:);
+        remainingClasses=classes(mod(randMap,k)~=i-1);
+        fprintf('%s create %ith model\n',datestr(datetime('now')),i)
+        
+        [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,maxPerClass);
+        disp(datestr(datetime('now')))
+
+        [predictions,accurancy(i,:),~]=svmpredict(leaveClasses,leaveOut(:,:),model);
+        cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
+    end
+    meanAccurancy=mean(accurancy(:,1))
+
+    fig=figure;
+    cmScaled=zeros(size(cm));
+    for i=1:size(cm,1)
+        cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
+    end
+    imagesc(cmScaled)
+    colorbar();
+    saveas(fig,sprintf('/nfs/wsi/ti/messor/hohlochj/plots/%s%i%icm200ms1sPause.fig',subject,number,EEG),'fig');
+    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i%i200ms1sPause.fig',subject,number,EEG),'meanAccurancy','maxC','cmScaled','-v7.3');
+end
diff --git a/usedMcode/waveformLength.m b/usedMcode/waveformLength.m
new file mode 100644
index 0000000..3b927f3
--- /dev/null
+++ b/usedMcode/waveformLength.m
@@ -0,0 +1,12 @@
+function [WL]=waveformLength(signal,frequency,window,windowShift)
+%waveformLength computes waveformLength of signal with given frequency, window and
+%shift
+    signalShift=frequency*windowShift;
+    signalWindow=frequency*window;
+    shiftProp=window/windowShift;
+    WL=zeros((floor(size(signal,1)/signalWindow)-1)*shiftProp+1,1);
+    for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
+        WL(j)=sum(abs(diff(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow))));
+    end
+end
+
diff --git a/waveformLength.m b/waveformLength.m
deleted file mode 100644
index 3b927f3..0000000
--- a/waveformLength.m
+++ /dev/null
@@ -1,12 +0,0 @@
-function [WL]=waveformLength(signal,frequency,window,windowShift)
-%waveformLength computes waveformLength of signal with given frequency, window and
-%shift
-    signalShift=frequency*windowShift;
-    signalWindow=frequency*window;
-    shiftProp=window/windowShift;
-    WL=zeros((floor(size(signal,1)/signalWindow)-1)*shiftProp+1,1);
-    for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
-        WL(j)=sum(abs(diff(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow))));
-    end
-end
-