diff --git a/balanceClasses.m b/balanceClasses.m
index e389592..3ccbd98 100644
--- a/balanceClasses.m
+++ b/balanceClasses.m
@@ -1,5 +1,5 @@
-function [trainClasses,trainData]=balanceClasses(trainClasses,trainData,eps)
-    while sum(abs(diff(histcounts(trainClasses,size(unique(trainClasses),2)))))> 0+eps
+function [trainClasses,trainData]=balanceClasses(trainClasses,trainData,noClasses,eps)
+    while sum(abs(diff(histcounts(trainClasses,noClasses))))> 0+eps
         mostFrequent=find(trainClasses==mode(trainClasses));
         
         r=floor(size(mostFrequent,2)*rand(1)+1);
@@ -7,4 +7,5 @@
         trainClasses=trainClasses([1:delIndex-1,delIndex+1:end]);
         trainData=trainData([1:delIndex-1,delIndex+1:end]);
     end
+    assert(size(trainClasses,2)>0)
 end
\ No newline at end of file
diff --git a/kfoldCV.m b/kfoldCV.m
index 9deb989..7a7e62f 100644
--- a/kfoldCV.m
+++ b/kfoldCV.m
@@ -1,5 +1,5 @@
 function [model] = kfoldCV(classification,trainingData,k,cExpMax)
-
+    noClasses=size(unique(classification),2);
     randMap=randperm(size(trainingData,1));
     leaveOut=trainingData(mod(randMap,k+1)==0,:,:);
     leaveClasses=classification(mod(randMap,k+1)==0);
@@ -17,16 +17,16 @@
             testData=remaining(mod(randomMapping,k)+1==i,:,:);
             trainClasses=remainingClasses(mod(randomMapping,k)+1~=i);
             testClasses=remainingClasses(mod(randomMapping,k)+1==i);
-            [trainClasses,trainData]=balanceClasses(trainClasses,trainData,0.00001);
+            [trainClasses,trainData]=balanceClasses(trainClasses,trainData,noClasses,0.00001);
             model=svmtrain(trainClasses,trainData(:,:),sprintf('-t 0 -c %f -q',c));
             [~, accurancy(i,:), ~]=svmpredict(testClasses, testData(:,:), model,'-q');
         end
 
-        cvAccurancy=mean(accurancy(:,1));
+        cvAccurancy(cExp+cExpMax+1)=mean(accurancy(:,1));
     end
     [~,i]=max(cvAccurancy);
     bestC=2^(i-cExpMax-1);
-    [balanceLeftClasses, balanceLeftOut]=balanceClasses(leaveClasses,leaveOut,0.00001);
+    [balanceLeftClasses, balanceLeftOut]=balanceClasses(leaveClasses,leaveOut,noClasses,0.00001);
     model=svmtrain(balanceLeftClasses, balanceLeftOut(:,:),sprintf('-t 0 -c %f -q',bestC));
 
 end
\ No newline at end of file
diff --git a/svm.m b/svm.m
index f30be1f..fe62691 100644
--- a/svm.m
+++ b/svm.m
@@ -1,11 +1,11 @@
 %params
 NFFT=2048;
-cutWindow=2;
+cutWindow=1;
 window=0.2;
 shift=0.05;
 k=10;
-rounds=1;
-maxExpC=5;
+rounds=75;
+maxExpC=7;
 
 %svm
 [signal, states, params] = load_bcidat('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R01.dat');
@@ -15,7 +15,18 @@
 %trainingData has format trial x ch x powerSpectrum
 [trainingDataEEG,trainingDataEMG]=generateTrainingData(splitSignals,NFFT,window,shift,params);
 
-classification=code;%double(code<=5);
+%classify for different movements and rest
+classification=zeros(size(code));
+for i=1:size(code)
+    if code(i)<=5
+        classification(i)=code(i);
+    else
+        classification(i)=0;
+    end
+end
+
+%classify for move/rest
+%classification=double(code<=5);
 
 
 accurancy=zeros(rounds,3);