diff --git a/cutMovements.m b/cutMovements.m
new file mode 100644
index 0000000..bcd0132
--- /dev/null
+++ b/cutMovements.m
@@ -0,0 +1,22 @@
+function [ splitSignals, code ] = cutMovements( stimulusCodes, signal )
+%cut Trials only, ignore resting phases
+
+noSamplesPerTrial=15000;
+% count how many recorded values belong to one trial
+changeIndices=[find(diff(double(stimulusCodes)));size(stimulusCodes,1)];
+codeCount=[diff(changeIndices); size(stimulusCodes,1)-sum(diff(changeIndices))];
+
+splitSignals=[];
+code=[];
+
+c=1;
+
+for i=1:size(codeCount,1)
+    if(codeCount(i)==noSamplesPerTrial && stimulusCodes(c)~=0)
+        splitSignals=cat(3,splitSignals,signal(c:c+codeCount(i)-1,:));
+        code=cat(1,code,stimulusCodes(c));
+    end
+    c=c+codeCount(i);
+end
+end
+
diff --git a/cutTrials.m b/cutTrials.m
deleted file mode 100644
index 18b87bd..0000000
--- a/cutTrials.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function [ splitSignals, code ] = cutTrials( states, signal )
-%cut Trials only, ignore resting phases
-
-noSamplesPerTrial=15000;
-% count how many recorded values belong to one trial
-changeIndices=[find(diff(double(states.StimulusCode)));size(states.StimulusCode,1)];
-codeCount=[diff(changeIndices); size(states.StimulusCode,1)-sum(diff(changeIndices))];
-
-splitSignals=[];
-code=[];
-
-c=1;
-
-for i=1:size(codeCount,1)
-    if(codeCount(i)==noSamplesPerTrial)
-        splitSignals=cat(3,splitSignals,signal(c:c+codeCount(i)-1,:));
-        code=cat(1,code,states.StimulusCode(c));
-    end
-    c=c+codeCount(i);
-end
-end
-
diff --git a/cutWindows.m b/cutWindows.m
index 2430ffe..61b5b46 100644
--- a/cutWindows.m
+++ b/cutWindows.m
@@ -1,5 +1,5 @@
-function [ splitSignals, code ] = cutWindows( states, signal, params, t )
-    changeIndices=cat(1,0,find(diff(double(states.StimulusCode))));
+function [ splitSignals, code ] = cutWindows( stimulusCodes, signal, params, t )
+    changeIndices=cat(1,0,find(diff(double(stimulusCodes))));
     codeCount=diff(changeIndices);
     interval=t*params.SamplingRate.NumericValue;
 
@@ -12,7 +12,7 @@
         tempc=0;
         while codeCount(i)-tempc >= interval
             splitSignals=cat(3,splitSignals,signal(c+tempc:c+tempc+interval-1,:));
-            code=cat(1,code,double(states.StimulusCode(c+tempc)));
+            code=cat(1,code,double(stimulusCodes(c+tempc)));
             tempc=tempc+interval;
         end
         c=c+codeCount(i);
diff --git a/generateTrainingData.m b/generateTrainingData.m
index a467afc..33ea013 100644
--- a/generateTrainingData.m
+++ b/generateTrainingData.m
@@ -1,7 +1,7 @@
-function [trainingDataEEG,trainingDataEMG] = generateTrainingData(splitSignals,NFFT,window,shift,params)
+function [trainingDataEEG,trainingDataEMG] = generateTrainingData(splitSignals,NFFT,window,shift,params,secs)
     frequency=params.SamplingRate.NumericValue;
     tempEEG2=zeros([32,size(splitSignals,3),NFFT/2+1]);
-    tempEMG2=zeros([size(splitSignals,2)-3-32,size(splitSignals,3),(floor(size(splitSignals,1)/(frequency*window))-1)*window/shift+1]);
+    tempEMG2=zeros(fix([size(splitSignals,2)-3-32,size(splitSignals,3),fix(floor(size(splitSignals,1)/(frequency*window))-1)*window/shift+1]));
     
     %Filter around 50Hz and below 2 Hz
     [A,B]= butter(2,[48 52]/(frequency/2),'stop');
@@ -10,14 +10,14 @@
     parfor i=1:32 %filter single channel, w/o HEOG, Synchro and 0s
         tempEEG=zeros(size(splitSignals,3),NFFT/2+1);
         for j=1:size(splitSignals,3) %filter single trial
-            tempEEG(j,:)=pwelch(filter(C,D,filter(A,B,splitSignals(:,i,j))),[],[],NFFT,frequency);
+            tempEEG(j,:)=pwelch(filter(C,D,filter(A,B,splitSignals(1:secs*frequency,i,j))),[],[],NFFT,frequency);
         end
         tempEEG2(i,:,:)=tempEEG;
     end
     trainingDataEEG=permute(tempEEG2,[2 1 3]);
     
     parfor i=33:size(splitSignals,2)-3
-        tempEMG=zeros([size(splitSignals,3),(floor(size(splitSignals,1)/(frequency*window))-1)*window/shift+1]);
+        tempEMG=zeros(fix([size(splitSignals,3),(floor(size(splitSignals,1)/(frequency*window))-1)*window/shift+1]));
         for j=1:size(splitSignals,3) %filter single trial
             tempEMG(j,:)=waveformLength(filter(C,D,filter(A,B,splitSignals(:,i,j))),frequency,window,shift);
         end
diff --git a/kfoldCV.m b/kfoldCV.m
index 7a7e62f..29deb48 100644
--- a/kfoldCV.m
+++ b/kfoldCV.m
@@ -1,8 +1,8 @@
 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);
+    leaveOut=trainingData;
+    leaveClasses=classification;
     remaining=trainingData(mod(randMap,k+1)~=0,:,:);
     remainingClasses=classification(mod(randMap,k+1)~=0);
     cvAccurancy=zeros(2*cExpMax+1,1);
diff --git a/read.m b/read.m
new file mode 100644
index 0000000..d13a0ff
--- /dev/null
+++ b/read.m
@@ -0,0 +1,7 @@
+signal=[];
+stimulusCodes=[];
+for i=1:5
+    [sig, stat, params] = load_bcidat(sprintf('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R0%i.dat',i));
+    signal=cat(1,signal,sig);
+    stimulusCodes=cat(1,stat.StimulusCode,stimulusCodes);
+end
diff --git a/readme.m b/readme.m
index fb22dd8..28b94dd 100644
--- a/readme.m
+++ b/readme.m
@@ -52,8 +52,13 @@
 % cross validation (selber impelmentieren)
 
 % Aufteilung EMG xor EEG (done)
-% danach dann Bewegungen aufteilen
-%  -> testen ob mit EMG besser
-% Ideen:
-% - alle Daten, zumindest von einer PErson einlesen
+% danach dann Bewegungen aufteilenC
 
+
+% von einer Person und einem Tag zusammen (done)
+% nur die ersten zwei Sekunden der Bewegung (done [generateTrainingData])
+% für WL keine Fenster sondern ganze Bewegung (done s.o.)
+% äußere Crossvalidation
+% Confusion
+% Ruhe / Bewegung aufteilen EEG EMG
+%  -> testen ob mit EMG besser
diff --git a/svm.m b/svm.m
index 9bf653f..3780e04 100644
--- a/svm.m
+++ b/svm.m
@@ -1,19 +1,20 @@
 %params
 NFFT=2048;
-cutWindow=2;
+cutWindowEEG=2;
 window=0.2;
 shift=0.05;
 k=10;
-rounds=10;
-maxExpC=5;
+rounds=1;
+maxExpC=7;
 
 %svm
-[signal, states, params] = load_bcidat('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R01.dat');
-% [ splitSignals, code ] = cutTrials( states, signal );
-[ splitSignals, code ] = cutWindows( states, signal, params, cutWindow );
+load('/home/jph/Uni/masterarbeit/data/AO1.mat');
+
+[ splitSignals, code ] = cutMovements( stimulusCodes, signal );
+%[ splitSignals, code ] = cutWindows( stimulusCodes, signal, params, cutWindow );
 
 %trainingData has format trial x ch x powerSpectrum
-[trainingDataEEG,trainingDataEMG]=generateTrainingData(splitSignals,NFFT,window,shift,params);
+[trainingDataEEG,trainingDataEMG]=generateTrainingData(splitSignals,NFFT,window,shift,params,cutWindowEEG);
 
 %classify for different movements and rest
 classification=zeros(size(code));
@@ -25,11 +26,13 @@
     end
 end
 
+
 %classify for move/rest
+% cann't use cutMovements then
 %classification=double(code<=5);
 
 %choose to estimate based on EEG or EMG
-trainingData=trainingDataEMG;
+trainingData=trainingDataEEG;
 
 
 accurancy=zeros(rounds,3);
@@ -44,7 +47,8 @@
     model=kfoldCV(remainingClasses,remaining,k,maxExpC);
 
     [predictions,accurancy(i,:),pvalues]=svmpredict(leaveClasses,leaveOut(:,:),model);
-    means(i)=mean(leaveClasses-predictions);
+    means(i)=mean(leaveClasses-predictions); %confusion matrix
+    %cf: for each cue what was the prediction (maybe colourCoded)
 end
 m=mean(accurancy(:,1))
 mm=mean(means)
\ No newline at end of file
diff --git a/waveformLength.m b/waveformLength.m
index cfc0298..8239c05 100644
--- a/waveformLength.m
+++ b/waveformLength.m
@@ -5,7 +5,7 @@
     signalWindow=frequency*window;
     shiftProp=window/windowShift;
     WL=zeros((floor(size(signal,1)/signalWindow)-1)*shiftProp+1,1);
-    parfor j=1:(size(signal,1)/signalWindow-1)*shiftProp+1
+    for j=1:(size(signal,1)/signalWindow-1)*shiftProp+1
         WL(j)=sum(diff(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow)));
     end
 end