diff --git a/readme.m b/readme.m
index babc2a6..b18cd01 100644
--- a/readme.m
+++ b/readme.m
@@ -44,7 +44,10 @@
 %Tipps
 % find, diff, xlim([0 0.1])
 
-%imagesc
+%%imagesc
+%     fig=figure;
+%     imagesc(cmScaled)
+%     colorbar();
 
 %libsvm
 % - svmTrain
diff --git a/text/TODO.txt b/text/TODO.txt
index 027407a..9da5ee1 100644
--- a/text/TODO.txt
+++ b/text/TODO.txt
@@ -40,9 +40,11 @@
 
 Synergien aus WL (50ms step 200 ms window) (done)
 
-Synergien lernen
+Synergien lernen (implementiert)
 
-shift echt variabel, bisher EEG und EMG gleich
+shift echt variabel, bisher EEG und EMG gleich (test läuft)
+
+offset für EEG
 
 Refactoring, Comments
 
diff --git a/usedMcode/kfoldCV.m b/usedMcode/kfoldCV.m
index af80d84..c1ea267 100644
--- a/usedMcode/kfoldCV.m
+++ b/usedMcode/kfoldCV.m
@@ -1,32 +1,34 @@
 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);
-    for cExp=1:2*cExpMax+1
-        c=2^(cExp-cExpMax-1);
+    if cExpMax>0 %CV only if necessary
+        cvAccurancy=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);
+            randomMapping=transpose(randperm(size(trainingData,1)));
+            accurancy=zeros(k,3);
 
-        parfor i=1:k
-            trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
-            testData=trainingData(mod(randomMapping,k)+1==i,:,:);
-            trainClasses=classification(mod(randomMapping,k)+1~=i);
-            testClasses=classification(mod(randomMapping,k)+1==i);
-            %fprintf('i=%i, k=%i, c=%i\n',i,k,c)
-            [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');
+            parfor i=1:k
+                trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
+                testData=trainingData(mod(randomMapping,k)+1==i,:,:);
+                trainClasses=classification(mod(randomMapping,k)+1~=i);
+                testClasses=classification(mod(randomMapping,k)+1==i);
+                %fprintf('i=%i, k=%i, c=%i\n',i,k,c)
+                [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');
+            end
+
+            cvAccurancy(cExp)=mean(accurancy(:,1));
         end
-
-        cvAccurancy(cExp)=mean(accurancy(:,1));
+        [~,maxC]=max(cvAccurancy);
+    else
+        maxC=1; %no gridsearch since only one C possible
     end
-    [~,maxC]=max(cvAccurancy);
-else
-    maxC=1; %no gridsearch since only one C possible
-end
     bestC=2^(maxC-cExpMax-1);
-    [balancedClasses, balanceData]=balanceClasses(classification,trainingData,maxDataPerClass,noClasses);
+    [balancedClasses, balanceData]=balanceClasses(classification,trainingData,...
+        maxDataPerClass,noClasses);
     model=svmtrain(balancedClasses, balanceData(:,:),sprintf('-t 0 -c %f -q',bestC));
 
 end
diff --git a/usedMcode/svmEciton.m b/usedMcode/svmEciton.m
index c9456f2..2293c49 100644
--- a/usedMcode/svmEciton.m
+++ b/usedMcode/svmEciton.m
@@ -46,9 +46,4 @@
     for i=1:size(cm,1)
         cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
     end
-%     fig=figure;
-%     imagesc(cmScaled)
-%     colorbar();
-    %saveas(fig,strcat(pathToFile,sprintf('../plots/%s%i%icm200ms1sPause.fig',subject,number,EEG)),'fig');
-    %save(strcat(pathToFile,sprintf('../matlabData/%s%i%i200ms1sPause.mat',subject,number,EEG)),'meanAccurancy','maxC','cmScaled','-v7.3');
 end