diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index dcd1ff9..829b295 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -40,12 +40,18 @@
 
 if allSubjects
     
-     meanAccurancysEMG=zeros([j,1]);
-     meanAccurancysEEG=zeros([j,1]);
-     meanAccurancysLF=zeros([j,1]);
+     meanAccuracysEMG=zeros([j,1]);
+     meanAccuracysEEG=zeros([j,1]);
+     meanAccuracysLF=zeros([j,1]);
      maxCEMG=zeros([j,k,1]);
      maxCEEG=zeros([j,k,1]);
      maxCLF=zeros([j,k,1]);
+     meanAccuracysEMGmovements=zeros([j,1]);
+     meanAccuracysEEGmovements=zeros([j,1]);
+     meanAccuracysLFmovements=zeros([j,1]);
+     maxCEMGmovements=zeros([j,k,1]);
+     maxCEEGmovements=zeros([j,k,1]);
+     maxCLFmovements=zeros([j,k,1]);
 %     cmScaledEMG=zeros([j,5,5]);
 %     cmScaledEEG=zeros([j,5,5]);
 %     cmScaledLF=zeros([j,5,5]);
@@ -73,28 +79,28 @@
 %     correlationEMGnnmf=zeros([j,noSynergies]);
 %     correlationEEGnnmf=zeros([j,noSynergies]);
 %     correlationLFnnmf=zeros([j,noSynergies]);
-    maxRidgeParamIndexEEGemg=zeros([j,size(EMGChannels,2),k]);
-    correlationEEGemg=zeros([j,size(EMGChannels,2)]);
-    maxRidgeParamIndexAutoencKin=zeros([j,noSynergies,k]);
-    correlationAutoencKin=zeros([j,3]);
-    correlationViaAutoenc=zeros([j,3]);
-    correlationViaPCA=zeros([j,3]);
-    correlationViaNNMF=zeros([j,3]);
-    viaCorrelationAutoenc=zeros(j,noSynergies);
-    viaCorrelationPCA=zeros(j,noSynergies);
-    viaCorrelationNNMF=zeros(j,noSynergies);
-    correlationEMGViaAutoenc=zeros([j,size(EMGChannels,2)]);
+%     maxRidgeParamIndexEEGemg=zeros([j,size(EMGChannels,2),k]);
+%     correlationEEGemg=zeros([j,size(EMGChannels,2)]);
+%     maxRidgeParamIndexAutoencKin=zeros([j,noSynergies,k]);
+%     correlationAutoencKin=zeros([j,3]);
+%     correlationViaAutoenc=zeros([j,3]);
+%     correlationViaPCA=zeros([j,3]);
+%     correlationViaNNMF=zeros([j,3]);
+%     viaCorrelationAutoenc=zeros(j,noSynergies);
+%     viaCorrelationPCA=zeros(j,noSynergies);
+%     viaCorrelationNNMF=zeros(j,noSynergies);
+%     correlationEMGViaAutoenc=zeros([j,size(EMGChannels,2)]);
     
     parfor j=1:size(numbersMat,2)
         number=numbersMat(j);
         subject=subjectsForNumbers{j};
         savePath=readAll(pathToFile,subject,number,windowEMG,windowEEG,shiftEMG,shiftEEG,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause,noLFsamples,EMGChannels,noSynergies);
-         [meanAccurancysEMG(j),maxCEMG(j,:),~]=svmEciton(savePath,'EMG',k,maxExpC,maxPerClass,eegOffset,true);
-         [meanAccurancysEEG(j),maxCEEG(j,:),~]=svmEciton(savePath,'EEG',k,maxExpC,maxPerClass,eegOffset,true);
-         [meanAccurancysLF(j),maxCLF(j,:),~]=svmEciton(savePath,'LF',k,maxExpC,maxPerClass,eegOffset,true);
-%         [meanAccurancysEMG(j),maxCEMG(j,:),cmScaledEMG(j,:,:)]=svmEciton(savePath,'EMG',k,maxExpC,maxPerClass,eegOffset,false);
-%         [meanAccurancysEEG(j),maxCEEG(j,:),cmScaledEEG(j,:,:)]=svmEciton(savePath,'EEG',k,maxExpC,maxPerClass,eegOffset,false);
-%         [meanAccurancysLF(j),maxCLF(j,:),cmScaledLF(j,:,:)]=svmEciton(savePath,'LF',k,maxExpC,maxPerClass,eegOffset,false);
+         [meanAccuracysEMG(j),maxCEMG(j,:),~]=svmEciton(savePath,'EMG',k,maxExpC,maxPerClass,eegOffset,true);
+         [meanAccuracysEEG(j),maxCEEG(j,:),~]=svmEciton(savePath,'EEG',k,maxExpC,maxPerClass,eegOffset,true);
+         [meanAccuracysLF(j),maxCLF(j,:),~]=svmEciton(savePath,'LF',k,maxExpC,maxPerClass,eegOffset,true);
+%         [meanAccuracysEMGmovements(j),maxCEMGmovements(j,:),cmScaledEMG(j,:,:)]=svmEciton(savePath,'EMG',k,maxExpC,maxPerClass,eegOffset,false);
+%         [meanAccuracysEEGmovements(j),maxCEEGmovements(j,:),cmScaledEEG(j,:,:)]=svmEciton(savePath,'EEG',k,maxExpC,maxPerClass,eegOffset,false);
+%         [meanAccuracysLFmovements(j),maxCLFmovements(j,:),cmScaledLF(j,:,:)]=svmEciton(savePath,'LF',k,maxExpC,maxPerClass,eegOffset,false);
 %         [correlationEMGkin(j,:),maxRidgeParamIndexEMGkin(j,:,:)]=ridgeCV(savePath,'EMG','kin',k,ridgeParams,eegOffset);
 %         [correlationEEGkin(j,:),maxRidgeParamIndexEEGkin(j,:,:)]=ridgeCV(savePath,'EEG','kin',k,ridgeParams,eegOffset);
 %         [correlationLFkin(j,:),maxRidgeParamIndexLFkin(j,:,:)]=ridgeCV(savePath,'LF','kin',k,ridgeParams,eegOffset);
@@ -124,9 +130,9 @@
     number=numbersMat(j);
     subject=subjectsForNumbers{j};
     savePath=readAll(pathToFile,subject,number,windowEMG,windowEEG,shiftEMG,shiftEEG,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause,noLFsamples,EMGChannels,noSynergies);
-%     [meanAccurancysEMG,maxCEMG,cmScaledEMG(:,:)]=svmEciton(savePath,'EMG',k,maxExpC,maxPerClass,eegOffset);
-%     [meanAccurancysEEG,maxCEEG,cmScaledEEG(:,:)]=svmEciton(savePath,'EEG',k,maxExpC,maxPerClass,eegOffset);
-%     [meanAccurancysLF,maxCLF,cmScaledLF(:,:)]=svmEciton(savePath,'LF',k,maxExpC,maxPerClass,eegOffset);
+%     [meanAccuracysEMG,maxCEMG,cmScaledEMG(:,:)]=svmEciton(savePath,'EMG',k,maxExpC,maxPerClass,eegOffset);
+%     [meanAccuracysEEG,maxCEEG,cmScaledEEG(:,:)]=svmEciton(savePath,'EEG',k,maxExpC,maxPerClass,eegOffset);
+%     [meanAccuracysLF,maxCLF,cmScaledLF(:,:)]=svmEciton(savePath,'LF',k,maxExpC,maxPerClass,eegOffset);
 %     [correlationEMGkin,maxRidgeParamIndexEMGkin(:,:)]=ridgeCV(savePath,'EMG','kin',k,ridgeParams,eegOffset);
 %     [correlationEEGkin,maxRidgeParamIndexEEGkin(:,:)]=ridgeCV(savePath,'EEG','kin',k,ridgeParams,eegOffset);
 %     [correlationLFkin,maxRidgeParamIndexLFkin(:,:)]=ridgeCV(savePath,'LF','kin',k,ridgeParams,eegOffset);
diff --git a/usedMcode/kfoldCV.m b/usedMcode/kfoldCV.m
index c1ea267..3b422f5 100644
--- a/usedMcode/kfoldCV.m
+++ b/usedMcode/kfoldCV.m
@@ -1,12 +1,12 @@
 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);
+        cvAccuracy=zeros(2*cExpMax+1,1);
         for cExp=1:2*cExpMax+1
             c=2^(cExp-cExpMax-1);
 
             randomMapping=transpose(randperm(size(trainingData,1)));
-            accurancy=zeros(k,3);
+            accuracy=zeros(k,3);
 
             parfor i=1:k
                 trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
@@ -17,12 +17,12 @@
                 [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');
+                [~, accuracy(i,:), ~]=svmpredict(testClasses, testData(:,:), model,'-q');
             end
 
-            cvAccurancy(cExp)=mean(accurancy(:,1));
+            cvAccuracy(cExp)=mean(accuracy(:,1));
         end
-        [~,maxC]=max(cvAccurancy);
+        [~,maxC]=max(cvAccuracy);
     else
         maxC=1; %no gridsearch since only one C possible
     end
diff --git a/usedMcode/svmEciton.m b/usedMcode/svmEciton.m
index 7795a18..aceca35 100644
--- a/usedMcode/svmEciton.m
+++ b/usedMcode/svmEciton.m
@@ -23,7 +23,7 @@
     clear synergies*;
     
     if moveRest
-        classification=classification~=0;
+        classification=double(classification~=0);
     end
     
     accuracy=zeros(k,3);