diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index 2f2508b..5f35c2d 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -47,18 +47,18 @@
 % run for all 51 runs
 if allSubjects
     
-     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]);
+%      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]);
@@ -89,7 +89,11 @@
 %     maxRidgeParamIndexEEGemg=zeros([j,size(EMGChannels,2),k]);
 %     correlationEEGemg=zeros([j,size(EMGChannels,2)]);
 %     maxRidgeParamIndexAutoencKin=zeros([j,noSynergies,k]);
+     maxRidgeParamIndexPCAKin=zeros([j,noSynergies,k]);
+     maxRidgeParamIndexNNMFKin=zeros([j,noSynergies,k]);
 %     correlationAutoencKin=zeros([j,3]);
+     correlationPCAKin=zeros([j,3]);
+     correlationNNMFKin=zeros([j,3]);
 %     correlationViaAutoenc=zeros([j,3]);
 %     correlationViaPCA=zeros([j,3]);
 %     correlationViaNNMF=zeros([j,3]);
@@ -102,9 +106,9 @@
         number=numbersMat(j);
         subject=subjectsForNumbers{j};
         savePath=readAll(pathToFile,subject,number,windowEMG,windowEEG,shiftEMG,shiftEEG,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause,noLFsamples,EMGChannels,noSynergies);
-         [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);
+%          [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);
@@ -122,6 +126,8 @@
 %         [correlationLFnnmf(j,:),maxRidgeParamIndexLFnnmf(j,:,:)]=ridgeCV(savePath,'LF','NNMF',k,ridgeParams,eegOffset);
 %        [correlationEEGemg(j,:),maxRidgeParamIndexEEGemg(j,:,:)]=ridgeCV(savePath,'EEG','EMG',k,ridgeParams,eegOffset);
 %        [correlationAutoencKin(j,:),maxRidgeParamIndexAutoencKin(j,:,:)]=ridgeCV(savePath,'Autoenc','kin',k,ridgeParams,eegOffset);
+        [correlationPCAKin(j,:),maxRidgeParamIndexPCAKin(j,:,:)]=ridgeCV(savePath,'PCA','kin',k,ridgeParams,eegOffset);
+        [correlationNNMFKin(j,:),maxRidgeParamIndexNNMFKin(j,:,:)]=ridgeCV(savePath,'NNMF','kin',k,ridgeParams,eegOffset);
 %        [correlationViaAutoenc(j,:),viaCorrelationAutoenc(j,:)]=ridgeCVvia(savePath,'EEG','Autoenc','kin',k,ridgeParams,eegOffset);
 %        [correlationViaPCA(j,:),viaCorrelationPCA(j,:)]=ridgeCVvia(savePath,'EEG','PCA','kin',k,ridgeParams,eegOffset);
 %        [correlationViaNNMF(j,:),viaCorrelationNNMF(j,:)]=ridgeCVvia(savePath,'EEG','NNMF','kin',k,ridgeParams,eegOffset);
diff --git a/usedMcode/evaluationCorrelations.m b/usedMcode/evaluationCorrelations.m
index cfefdcc..c226946 100644
--- a/usedMcode/evaluationCorrelations.m
+++ b/usedMcode/evaluationCorrelations.m
@@ -130,4 +130,51 @@
 anova1([correlations.EMG.kin.Default,correlations.Autoenc.kin.Default],{'EMG','EMG','EMG','Autoenc','Autoenc','Autoenc'})
 
 title('Predict Velocities from EMG or Synergies')
-mySaveFigure(gcf,strcat(figureSavePath,'EMGautoenc'))
\ No newline at end of file
+mySaveFigure(gcf,strcat(figureSavePath,'EMGautoenc'))
+
+% prediction from EEG
+input=correlations.EEG;
+
+emgPrediction=input.emg.Default;
+autoencPrediction=input.Autoenc.Default;
+pcaPrediction=input.PCA.Default;
+nnmfPrediction=input.NNMF.Default;
+
+EMG=cell(size(emgPrediction,2),1);
+EMG(:)={'EMG'};
+Autoenc=cell(size(autoencPrediction,2),1);
+Autoenc(:)={'Autoenc'};
+PCA=cell(size(pcaPrediction,2),1);
+PCA(:)={'PCA'};
+NNMF=cell(size(nnmfPrediction,2),1);
+NNMF(:)={'NMF'};
+
+anova1([emgPrediction,autoencPrediction,pcaPrediction,nnmfPrediction],cat(1,EMG,Autoenc,PCA,NNMF))
+
+title('Predict Synergies or EMG from EEG')
+ylabel('correlation')
+mySaveFigure(gcf,strcat(figureSavePath,'predictEMGSyn'))
+
+
+anova1([emgPrediction,autoencPrediction],cat(1,EMG,Autoenc))
+
+title('Predict Autoencoder or EMG from EEG')
+ylabel('correlation')
+mySaveFigure(gcf,strcat(figureSavePath,'predictEMGAutoenc'))
+
+%% compare predictions via different synergies
+input=correlations.EEG.pos;
+
+viaAutoenc=input.ViaAutoenc;
+viaPCA=input.ViaPCA;
+viaNNMF=input.ViaNNMF;
+
+
+Autoenc=cell(size(viaAutoenc,2),1);
+Autoenc(:)={'Autoenc'};
+PCA=cell(size(viaPCA,2),1);
+PCA(:)={'PCA'};
+NNMF=cell(size(viaNNMF,2),1);
+NNMF(:)={'NMF'};
+
+anova1([viaAutoenc,viaPCA,viaNNMF],cat(1,Autoenc,PCA,NNMF))
\ No newline at end of file
diff --git a/usedMcode/ridgeCV.m b/usedMcode/ridgeCV.m
index 4d92c5f..7b06653 100644
--- a/usedMcode/ridgeCV.m
+++ b/usedMcode/ridgeCV.m
@@ -13,8 +13,12 @@
         trainingData=trainingDataEEGlf(eegOffset+1:end,:);
     elseif strcmp(data,'Autoenc')
         trainingData=synergiesAutoenc;
+    elseif strcmp(data,'PCA')
+        trainingData=synergiesPCA;
+    elseif strcmp(data,'NNMF')
+        trainingData=synergiesNNMF;
     else
-        error('only EEG, EMG and LF are valid inputs for data');
+        error('only EEG, EMG, LF, Autoenc, PCA and NNMF are valid inputs for data');
     end
     % if data is smaller then goal, goal is scaled down
     factor=size(trainingDataEMG,1)/(size(trainingData,1)+eegOffset);