diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index 42e8ade..ca8dc86 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -21,7 +21,7 @@
 k=10;
 maxExpC=0;
 maxPerClass=250;
-poolObj=parpool(12);
+poolObj=parpool(3);
 
 [subjects,numbers]=namesAndNumbers(pathToFile);
 numbersMat=cell2mat(numbers);
@@ -29,7 +29,7 @@
 j=1;
 for i=1:size(subjects,2)
     subject=subjects{i};
-    for number=numbers{i} 
+    for number=numbers{i}
         subjectsForNumbers{j}=subject;
         j=j+1;
     end
@@ -72,7 +72,7 @@
     correlationEMGnnmf=zeros([j,noSynergies]);
     correlationEEGnnmf=zeros([j,noSynergies]);
     correlationLFnnmf=zeros([j,noSynergies]);
-
+    
     parfor j=1:size(numbersMat,2)
         number=numbersMat(j);
         subject=subjectsForNumbers{j};
@@ -94,7 +94,7 @@
         [correlationLFnnmf(j,:),maxRidgeParamIndexLFnnmf(j,:,:)]=ridgeCV(savePath,'LF','NNMF',k,ridgeParams,shiftEMG,shiftEEG);
         fprintf('%s%i finished %s\n',subject,number,datestr(datetime('now')))
     end
-
+    
     save(strcat(pathToFile,sprintf('../matlabData/%s_callAll-%s.mat',datestr(datetime('now'),name))));
 else
     j=fix(rand()*size(numbersMat,2)+1);
diff --git a/usedMcode/readAll.m b/usedMcode/readAll.m
index 79e6486..058d9f0 100644
--- a/usedMcode/readAll.m
+++ b/usedMcode/readAll.m
@@ -5,7 +5,7 @@
     savePath=strcat(pathToFile,...
         sprintf('../matlabData/%s%i%imsWindowEMG%isWindowEEG%imsShiftEMG%imsShiftEEGFreq%ito%iPause%ipBurg%i.mat',...
         subject,number,windowEMG*1000,windowEEG,shiftEMG*1000,shiftEEG*1000,minEEGFreq,maxEEGFreq,pause,pburgOrder));
-    
+
     %only create file if it doesn't exist yet
     if exist(savePath, 'file') ~= 2
         fprintf(strcat(savePath,' not existing; creating\n'));
@@ -46,14 +46,16 @@
         smoothClassificationEMG=zeros(size(classificationWithPause));
         for i=1:size(classificationWithPause,1)
             smoothClassificationEMG(i)=round(mode(classificationWithPause(...
-                max(i-fix(bci_sf/1000),1):min(i+fix(bci_sf/1000),end))));       
+                max(i-fix(bci_sf/1000),1):min(i+fix(bci_sf/1000),end))));
         end
 
         clear classificationWithPause
-        
+
         smoothClassificationEEG=zeros([size(trainingDataEEG,1),1]);
         for i=1:size(smoothClassificationEEG,1)
-            smoothClassificationEEG(i)=mode(smoothClassificationEMG(fix(round(shiftEEG/shiftEMG*(i-1))+1):min(fix(round(shiftEEG/shiftEMG*i)),max(size(smoothClassificationEMG)))));       
+            smoothClassificationEEG(i)=mode(smoothClassificationEMG(fix(...
+                round(shiftEEG/shiftEMG*(i-1))+1):min(fix(round(...
+                shiftEEG/shiftEMG*i)),max(size(smoothClassificationEMG)))));
         end
 
         trainingDataEEG=trainingDataEEG(smoothClassificationEEG~=-1,:,:);
@@ -61,8 +63,11 @@
         trainingDataEMG=trainingDataEMG(smoothClassificationEMG~=-1,:);
         classificationEMG=smoothClassificationEMG(smoothClassificationEMG~=-1);
         classificationEEG=smoothClassificationEEG(smoothClassificationEEG~=-1);
+
+        % all created matching the size of EMGdata, if necessary downsampling
+        % has to be done when used
         kinematics=kinMat(smoothClassificationEMG~=-1,:);
-        ae=trainAutoencoder(trainingDataEMG',noSynergies,'ShowProgressWindow',false);        
+        ae=trainAutoencoder(trainingDataEMG',noSynergies,'ShowProgressWindow',false);
         synergiesAutoenc=encode(ae,trainingDataEMG')';
         COEFF=pca(trainingDataEMG,'Centered',false);
         synergiesPCA=trainingDataEMG/COEFF(:,1:4)';
@@ -71,10 +76,12 @@
 
         clear smoothClassification i
         save(savePath,'trainingDataEEG','trainingDataEEGlf','trainingDataEMG',...
-            'classificationEMG','classificationEEG','kinematics','synergiesAutoenc','synergiesPCA',...
-            'synergiesNNMF','-v7.3');
+            'classificationEMG','classificationEEG','kinematics','synergiesAutoenc',...
+            'synergiesPCA','synergiesNNMF','-v7.3');
 
 
         %fprintf('finished reading %s%i %s\n',subject,number,datestr(datetime('now')));
+    else
+        fprintf(strcat(savePath,' yet existing; nothing to do\n'));
     end
 end
diff --git a/usedMcode/ridgeCV.m b/usedMcode/ridgeCV.m
index 63a1e50..3bcb0f6 100644
--- a/usedMcode/ridgeCV.m
+++ b/usedMcode/ridgeCV.m
@@ -1,7 +1,7 @@
 function [correlation,maxRidgeParamIndex]=ridgeCV(savePath,data,goal,k,ridgeParams,shiftEMG,shiftEEG)
     load(savePath);
     clear classification;
-    
+
     if strcmp(data,'EEG')
         trainingData=trainingDataEEG;
         factor=shiftEEG/shiftEMG;
@@ -14,11 +14,11 @@
     else
         error('only EEG, EMG and LF are valid inputs for data');
     end
-    
+
     clear trainingDataEEG;
     clear trainingDataEEGlf;
     clear trainingDataEMG;
-    
+
     if strcmp(goal,'Autoenc')
         predicted=shiftingMean(synergiesAutoenc,factor);
     elseif strcmp(goal,'PCA')
@@ -30,14 +30,14 @@
     else
         error('only kin, Autoenc, PCA and NNMF are valid inputs for goal');
     end
-    
+
     clear kinematics;
     clear synergies*;
-    
-    
+
+
     correlation=zeros(size(predicted,2),1);
     maxRidgeParamIndex=zeros(size(predicted,2),k);
-    
+
     for j=1:size(predicted,2)
         randMap=randperm(size(trainingData,1));
         pred=predicted(:,j);
@@ -49,7 +49,7 @@
             remainingData=trainingData(mod(randMap,k)~=i-1,:);
             remainingPred=pred(mod(randMap,k)~=i-1);
 
-            [coeffs,maxRidgeParamIndex(j,i)]=kFoldRidge(remainingData,remainingPred,k,ridgeParams,shiftEMG,shiftEEG);
+            [coeffs,maxRidgeParamIndex(j,i)]=kFoldRidge(remainingData,remainingPred,k,ridgeParams);
 
             correlations(i)=ridgeCorrelation(leaveData,leavePerd,coeffs);
         end