diff --git a/text/TODO.txt b/text/TODO.txt
index 6160808..9d5e2b6 100644
--- a/text/TODO.txt
+++ b/text/TODO.txt
@@ -66,17 +66,16 @@
 Calculations done:
 - offset (0,1,2)
 - pause (0,1)
-- Pos (offset 0-2)
 
 Calculations todo:
+- Pos (offset 0-2)
 - c
 - ridge
 - maxFreq 
 - maxPerclass?
-- noLFsample höher, Frequenzen testen?
-
+- noLFsample höher(10), Frequenzen testen?
 Aus EEG EMG vorhersagen (WL) (regression, dann corr) (running)
 Aus Synergien kin (running)
-Aus EEG (über Synergien) kinematik (testing)
+Aus EEG (über Synergien) kinematik
 
 Intro nur Motivation, Rest in II
diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index e939467..92acc31 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -80,6 +80,9 @@
     correlationViaAutoenc=zeros(j);
     correlationViaPCA=zeros(j);
     correlationViaNNMF=zeros(j);
+    viaCorrelationAutoenc=zeros(j,noSynergies);
+    viaCorrelationPCA=zeros(j,noSynergies);
+    viaCorrelationNNMF=zeros(j,noSynergies);
     
     parfor j=1:size(numbersMat,2)
         number=numbersMat(j);
@@ -102,9 +105,9 @@
 %         [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);
-        correlationViaAutoenc(j)=ridgeCVvia(savePath,'EEG','Autoenc','kin',k,ridgeParams,eegOffset);
-        correlationViaPCA(j)=ridgeCVvia(savePath,'EEG','PCA','kin',k,ridgeParams,eegOffset);
-        correlationViaNNMF(j)=ridgeCVvia(savePath,'EEG','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);
         
         fprintf('%s%i finished %s\n',subject,number,datestr(datetime('now')))
     end
@@ -133,9 +136,9 @@
 %     [correlationLFnnmf,maxRidgeParamIndexLFnnmf(:,:)]=ridgeCV(savePath,'LF','NNMF',k,ridgeParams,eegOffset);
 %     [correlationEEGemg,maxRidgeParamIndexEEGemg(:,:)]=ridgeCV(savePath,'EEG','EMG',k,ridgeParams,eegOffset);
 %     [correlationAutoencKin,maxRidgeParamIndexAutoencKin(:,:)]=ridgeCV(savePath,'Autoenc','kin',k,ridgeParams,eegOffset);
-    correlationViaAutoenc=ridgeCVvia(savePath,'EEG','Autoenc','kin',k,[100],eegOffset);
-    correlationViaPCA=ridgeCVvia(savePath,'EEG','PCA','kin',k,[100],eegOffset);
-    correlationViaNNMF=ridgeCVvia(savePath,'EEG','NNMF','kin',k,[100],eegOffset);
+    [correlationViaAutoenc,viaCorrelationAutoenc]=ridgeCVvia(savePath,'EEG','Autoenc','kin',k,[100],eegOffset);
+    [correlationViaPCA,viaCorrelationPCA]=ridgeCVvia(savePath,'EEG','PCA','kin',k,[100],eegOffset);
+    [correlationViaNNMF,viaCorrelationNNMF]=ridgeCVvia(savePath,'EEG','NNMF','kin',k,[100],eegOffset);
     fprintf('%s%i finished %s\n',subject,number,datestr(datetime('now')))
     
     save(strcat(pathToFile,sprintf('../matlabData/%s_call%s%i-%s.mat',datestr(datetime('now')),subject,number,name)));
diff --git a/usedMcode/ridgeCVvia.m b/usedMcode/ridgeCVvia.m
index 25e5d7d..af0ff60 100644
--- a/usedMcode/ridgeCVvia.m
+++ b/usedMcode/ridgeCVvia.m
@@ -1,4 +1,4 @@
-function [correlation]=ridgeCVvia(savePath,data,via,goal,k,ridgeParams,eegOffset)
+function [correlation,viaCorrelation]=ridgeCVvia(savePath,data,via,goal,k,ridgeParams,eegOffset)
     load(savePath);
     clear classification;
     eeg=false;
@@ -49,6 +49,7 @@
 
     viaDataPredicted=zeros(size(viaData));
     correlation=zeros(size(predicted,2));
+    viaCorrelation=zeros(size(predicted,2));
 
     parfor j=1:size(viaData,2)
         pred=viaData(:,j);
@@ -56,7 +57,7 @@
         coeffs=ridge(pred,trainingData,ridgeParams(1),0);
         
         viaDataPredicted(:,j)=coeffs(1)+trainingData*coeffs(2:end);
-
+        viaCorrelation(:,j)=corr(viaData(:,j),viaDataPredicted(:,j));
     end
     
     parfor j=1:size(predicted,2)