diff --git a/readEEGEciton.m b/readEEGEciton.m
index 0a527cb..69661ee 100644
--- a/readEEGEciton.m
+++ b/readEEGEciton.m
@@ -1,6 +1,6 @@
 function readEEGEciton(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
 
-    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
+    %fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
 
     trainingDataEEGcell=cell(maxFile,1);
     trainingDataEMGcell=cell(maxFile,1);
@@ -10,7 +10,7 @@
         [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/origData/%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,i));
         [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
         classesCell{i}=stat.StimulusCode;
-        fprintf('%ith file processed\n',i)
+        %fprintf('%ith file processed\n',i)
     end
 
     clear sig
@@ -38,5 +38,5 @@
     save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),'trainingDataEEG','trainingDataEMG','classification','-v7.3');
     
 
-    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
+    %fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
 end
diff --git a/svmEciton.m b/svmEciton.m
index 52d7aaf..cef349e 100644
--- a/svmEciton.m
+++ b/svmEciton.m
@@ -20,16 +20,16 @@
     noClasses=size(unique(classification),1);
     cm=zeros(noClasses);
     randMap=randperm(size(trainingData,1));
-    disp('startCV')
+    %disp('startCV')
     parfor i=1:k
         leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
         leaveClasses=classification(mod(randMap,k)==i-1);
         remaining=trainingData(mod(randMap,k)~=i-1,:,:);
         remainingClasses=classification(mod(randMap,k)~=i-1);
-        disp(datestr(datetime('now')))
-        fprintf('create %ith model\n',i)
+        %disp(datestr(datetime('now')))
+        %fprintf('create %ith model\n',i)
         [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,maxPerClass);
-        disp(datestr(datetime('now')))
+        %disp(datestr(datetime('now')))
 
         [predictions,accurancy(i,:),~]=svmpredict(leaveClasses,leaveOut(:,:),model);
         cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
@@ -45,5 +45,6 @@
     imagesc(cmScaled)
     colorbar();
     saveas(fig,sprintf('/nfs/wsi/ti/messor/hohlochj/plots/%s%i%icm200ms1sPause.fig',subject,number,EEG),'fig');
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i%i200ms1sPause.fig',subject,number,EEG),'meanAccurancy','maxC','cmScaled','-v7.3');
+    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i%i200ms1sPause.mat',subject,number,EEG),'meanAccurancy','maxC','cmScaled','-v7.3');
+    fprintf('%s%i%i finished %s\n',subject,number,EEG,datestr(datetime('now')))
 end