diff --git a/classifyAccordingToEMG.m b/classifyAccordingToEMG.m
index 8805818..4937188 100644
--- a/classifyAccordingToEMG.m
+++ b/classifyAccordingToEMG.m
@@ -11,7 +11,7 @@
                         classification(j)=-1; %code for pause
                     end
                     for j=max(fix(i-0.5/shift),1):i % half a second before executed movement there should be activity (esp. EEG)
-                        classification(j)=-1;
+                        classification(j)=-1; %if -1 there is a pause of 1s befor movement onset
                     end
                     oldclass=class;
                 end
diff --git a/generateTrainingData.m b/generateTrainingData.m
index fa16f55..5a04b26 100644
--- a/generateTrainingData.m
+++ b/generateTrainingData.m
@@ -1,16 +1,16 @@
-function [trainingDataEEG,trainingDataEMG] = generateTrainingData(signal,NFFT,windowEMG,windowEEG,shift,params,pburgOrder)
+function [trainingDataEEG,trainingDataEMG] = generateTrainingData(signal,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq)
     frequency=params.SamplingRate.NumericValue;
     signalWindowEMG=frequency*windowEMG;
     signalWindowEEG=frequency*windowEEG;
-    tempEEG=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shift+1),NFFT/2+1]);
+    tempEEG=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shift+1),maxEEGFreq-minEEGFreq]);
     tempEMG=zeros(fix([size(signal,2)-3-32,fix(size(signal,1)/signalWindowEMG-1)*windowEMG/shift+1]));
     
     %Filter around 50Hz and below 2 Hz
     [A,B]= butter(2,[48 52]/(frequency/2),'stop');
-    [C,D]= butter(2,2/(frequency/2),'high');
+    [C,D]= butter(2,1/(frequency/2),'high');
 
     parfor i=1:32 %filter single channel, w/o HEOG, Synchro and 0s
-        tempEEG(i,:,:)=shiftingPburg(filter(C,D,filter(A,B,signal(:,i))),frequency,windowEEG,shift,NFFT,pburgOrder);
+        tempEEG(i,:,:)=shiftingPburg(filter(C,D,filter(A,B,signal(:,i))),frequency,windowEEG,shift,pburgOrder,minEEGFreq,maxEEGFreq);
     end % TODO: 8-30Hz, pburg bei kurzen Fenstern (Ordnung: Je nach SamplingRate, bei 2500: 32-50, Alpha, Beta peaks sollten sichtbar sein)
     trainingDataEEG=permute(tempEEG,[2 1 3]);
     
diff --git a/read.m b/read.m
index 6f0b938..6210be3 100644
--- a/read.m
+++ b/read.m
@@ -1,9 +1,8 @@
 close all
-clear all
+clear variables
 %params
 disp('start')
 disp(datestr(datetime('now')))
-NFFT=2048;
 windowEMG=0.2;
 windowEEG=1;
 shift=0.2;
@@ -12,6 +11,8 @@
 maxFile=5;
 threshold=7500;
 pburgOrder=32;
+minEEGFreq=0; %Hz
+maxEEGFreq=200; %Hz
 
 
 trainingDataEEGcell=cell(maxFile,1);
@@ -20,7 +21,7 @@
 
 for i=1:maxFile
     [sig, stat, params] = load_bcidat(sprintf('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R0%i.dat',i));
-    [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,NFFT,windowEMG,windowEEG,shift,params,pburgOrder);
+    [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
     classesCell{i}=stat.StimulusCode;
     fprintf('%ith file processed\n',i)
 end
diff --git a/shiftingPburg.m b/shiftingPburg.m
index fe32166..e43fbaa 100644
--- a/shiftingPburg.m
+++ b/shiftingPburg.m
@@ -1,13 +1,12 @@
-function [ EEG ] = shiftingPburg( signal,frequency,window,windowShift,NFFT,order)
+function [ EEG ] = shiftingPburg( signal,frequency,windowEEG,shift,order,minEEGFreq,maxEEGFreq)
 %shiftingPburg uses pburg to calculate the power spectrum
 %   Detailed explanation goes here
-    signalShift=frequency*windowShift;
-    signalWindow=frequency*window;
-    shiftProp=window/windowShift;
-    EEG=zeros([fix(floor(size(signal,1)/signalWindow)-1)*shiftProp+1,NFFT/2+1]);
+    signalShift=frequency*shift;
+    signalWindow=frequency*windowEEG;
+    shiftProp=windowEEG/shift;
+    EEG=zeros([fix(floor(size(signal,1)/signalWindow)-1)*shiftProp+1,fix(maxEEGFreq-minEEGFreq+1)]);
     for j=1:fix(floor(size(signal,1)/signalWindow-1)*shiftProp+1)
-        EEG(j,:)=pburg(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow),order,NFFT);
+        [EEG(j,:),~]=pburg(signal((j-1)*signalShift+1:(j-1)*signalShift+signalWindow),order,minEEGFreq:maxEEGFreq,frequency);
     end
-
 end
 
diff --git a/svm.m b/svm.m
index c662231..44c78a5 100644
--- a/svm.m
+++ b/svm.m
@@ -1,5 +1,5 @@
 close all
-clear all
+clear variables
 clc
 
 load('/home/jph/Uni/masterarbeit/data/AO1200msEMGWindow1sEEGWindow200msShift1sPause.mat');
@@ -8,7 +8,7 @@
 %maxExpC=0; % c\in {2^i|i=-maxExpC:1:maxExpC}
 
 %choose to estimate based on EEG or EMG
-trainingData=trainingDataEEG;
+trainingData=trainingDataEMG;
 clear trainingDataEEG;
 clear trainingDataEMG;
 
@@ -25,7 +25,7 @@
     remainingClasses=classification(mod(randMap,k)~=i-1);
     disp(datestr(datetime('now')))
     fprintf('create %ith model\n',i)
-    [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,100);
+    [model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,1000);
     disp(datestr(datetime('now')))
     
     [predictions,accurancy(i,:),pvalues]=svmpredict(leaveClasses,leaveOut(:,:),model);
@@ -34,9 +34,11 @@
 end
 meanAccurancy=mean(accurancy(:,1))
 
+fig=figure;
 cmScaled=zeros(size(cm));
 for i=1:size(cm,1)
     cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
 end
 imagesc(cmScaled)
 colorbar();
+saveas(fig,'/home/jph/Uni/masterarbeit/plots/cmEMG200ms1sPause.fig','fig');