diff --git a/text/TODO.txt b/text/TODO.txt
index a867298..027407a 100644
--- a/text/TODO.txt
+++ b/text/TODO.txt
@@ -41,3 +41,8 @@
 Synergien aus WL (50ms step 200 ms window) (done)
 
 Synergien lernen
+
+shift echt variabel, bisher EEG und EMG gleich
+
+Refactoring, Comments
+
diff --git a/usedMcode/classifyAccordingToEMG.m b/usedMcode/classifyAccordingToEMG.m
index 048f540..92b261d 100644
--- a/usedMcode/classifyAccordingToEMG.m
+++ b/usedMcode/classifyAccordingToEMG.m
@@ -1,5 +1,6 @@
-function [classification]=classifyAccordingToEMG(sizeTrainingData, trainingDataEMG, classes,shift,frequency,threshold,pause)
+function [classification]=classifyAccordingToEMG(trainingDataEMG, classes,shift,frequency,threshold,pause)
 % classifyAccordingToEMG data classified as -1 should be taken out
+    sizeTrainingData=size(trainingDataEMG,1);
     classification=zeros([sizeTrainingData,1]);
     oldclass=0;
     for i=1:sizeTrainingData
diff --git a/usedMcode/generateTrainingData.m b/usedMcode/generateTrainingData.m
index 745d301..747c807 100644
--- a/usedMcode/generateTrainingData.m
+++ b/usedMcode/generateTrainingData.m
@@ -1,10 +1,8 @@
 function [trainingDataEEG,trainingDataEEGlf,trainingDataEMG,kinPerSec] = generateTrainingData(signal,kin,windowEMG,windowEEG,shiftEMG,shiftEEG,params,pburgOrder,minEEGFreq,maxEEGFreq,noLFsamples,EMGChannels)
     bci_sf=params.SamplingRate.NumericValue;
-    signalWindowEMG=bci_sf*windowEMG;
     signalWindowEEG=bci_sf*windowEEG;
     tempEEG=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shiftEEG+1),maxEEGFreq-minEEGFreq+1]);
     tempEEGlf=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shiftEEG+1),noLFsamples]);
-    tempEMG=zeros(fix([6,fix(size(signal,1)/signalWindowEMG-1)*windowEMG/shiftEMG+1]));
     
     %Filter around 50Hz and below 2 Hz
     [A,B]= butter(2,[48 52]/(bci_sf/2),'stop');
diff --git a/usedMcode/readAll.m b/usedMcode/readAll.m
index 39accc7..79e6486 100644
--- a/usedMcode/readAll.m
+++ b/usedMcode/readAll.m
@@ -39,9 +39,8 @@
         kinMat=cell2mat(kin);
         clear trainingDataEEGcell trainingDataEMGcell classesCell kin
 
-        classificationWithPause=classifyAccordingToEMG(size(...
-            trainingDataEEG,1), trainingDataEMG,classesMat,shiftEMG,...
-            bci_sf,threshold,pause);
+        classificationWithPause=classifyAccordingToEMG(trainingDataEMG,...
+            classesMat,shiftEMG,bci_sf,threshold,pause);
         clear classesMat
 
         smoothClassificationEMG=zeros(size(classificationWithPause));
diff --git a/usedMcode/waveformLengthAll.m b/usedMcode/waveformLengthAll.m
index 3697b40..34eadcc 100644
--- a/usedMcode/waveformLengthAll.m
+++ b/usedMcode/waveformLengthAll.m
@@ -1,4 +1,4 @@
-function [EMG]=waveformLengthAll(sig,bci_sf,windowEMG,windowShift)
+function [EMG]=waveformLengthAll(sig,bci_sf,windowEMG,shiftEMG)
 
     %Filter around 50Hz and below 2 Hz
     [A,B]= butter(2,[48 52]/(bci_sf/2),'stop');
@@ -6,9 +6,9 @@
     [E,F]= butter(2,[148 152]/(bci_sf/2),'stop');
 
     signalWindow=bci_sf*windowEMG;
-    shiftProp=windowEMG/windowShift;
+    shiftProp=windowEMG/shiftEMG;
     EMG=zeros((floor(size(sig,1)/signalWindow)-1)*shiftProp+1,size(sig,2));
     parfor i=1:size(sig,2)
-        EMG(:,i)=waveformLength(filtfilt(double(E),double(F),filtfilt(double(C),double(D),filtfilt(double(A),double(B),double(sig(:,i))))),bci_sf,windowEMG,windowShift);
+        EMG(:,i)=waveformLength(filtfilt(double(E),double(F),filtfilt(double(C),double(D),filtfilt(double(A),double(B),double(sig(:,i))))),bci_sf,windowEMG,shiftEMG);
     end
 end
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