diff --git a/cutWindows.m b/cutWindows.m
index 44b50d6..2430ffe 100644
--- a/cutWindows.m
+++ b/cutWindows.m
@@ -11,7 +11,7 @@
     for i=2:(size(codeCount,1)) %exclude beginning and end (stimulus 0)
         tempc=0;
         while codeCount(i)-tempc >= interval
-            splitSignals=cat(3,splitSignals,signal(c+tempc:c+tempc+2499,:));
+            splitSignals=cat(3,splitSignals,signal(c+tempc:c+tempc+interval-1,:));
             code=cat(1,code,double(states.StimulusCode(c+tempc)));
             tempc=tempc+interval;
         end
diff --git a/readme.m b/readme.m
index 13fd3d6..fb22dd8 100644
--- a/readme.m
+++ b/readme.m
@@ -52,4 +52,8 @@
 % cross validation (selber impelmentieren)
 
 % Aufteilung EMG xor EEG (done)
-% danach dann Bewegungen aufteilen
\ No newline at end of file
+% danach dann Bewegungen aufteilen
+%  -> testen ob mit EMG besser
+% Ideen:
+% - alle Daten, zumindest von einer PErson einlesen
+
diff --git a/svm.m b/svm.m
index fe62691..9bf653f 100644
--- a/svm.m
+++ b/svm.m
@@ -1,11 +1,11 @@
 %params
 NFFT=2048;
-cutWindow=1;
+cutWindow=2;
 window=0.2;
 shift=0.05;
 k=10;
-rounds=75;
-maxExpC=7;
+rounds=10;
+maxExpC=5;
 
 %svm
 [signal, states, params] = load_bcidat('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R01.dat');
@@ -28,14 +28,17 @@
 %classify for move/rest
 %classification=double(code<=5);
 
+%choose to estimate based on EEG or EMG
+trainingData=trainingDataEMG;
+
 
 accurancy=zeros(rounds,3);
 means=zeros(rounds,1);
 for i=1:rounds
-    randMap=randperm(size(trainingDataEEG,1));
-    leaveOut=trainingDataEEG(mod(randMap,k+2)==0,:,:);
+    randMap=randperm(size(trainingData,1));
+    leaveOut=trainingData(mod(randMap,k+2)==0,:,:);
     leaveClasses=classification(mod(randMap,k+2)==0);
-    remaining=trainingDataEEG(mod(randMap,k+2)~=0,:,:);
+    remaining=trainingData(mod(randMap,k+2)~=0,:,:);
     remainingClasses=classification(mod(randMap,k+2)~=0);
 
     model=kfoldCV(remainingClasses,remaining,k,maxExpC);