diff --git a/svmEcitonEEG.m b/svmEcitonEEG.m
new file mode 100644
index 0000000..5752e56
--- /dev/null
+++ b/svmEcitonEEG.m
@@ -0,0 +1,44 @@
+close all
+clear variables
+clc
+
+load('/nfs/wsi/ti/messor/hohlochj/matlabData/AO1200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat');
+addpath('/nfs/wsi/ti/messor/hohlochj/libsvm/matlab');
+%k=2; 
+%maxExpC=0; % c\in {2^i|i=-maxExpC:1:maxExpC}
+
+%choose to estimate based on EEG or EMG
+trainingData=trainingDataEEG;
+clear trainingDataEEG;
+clear trainingDataEMG;
+
+accurancy=zeros(k,3);
+maxC=zeros(k,1);
+noClasses=size(unique(classification),1);
+cm=zeros(noClasses);
+randMap=randperm(size(trainingData,1));
+disp('startCV')
+parfor i=1:k
+    leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
+    leaveClasses=classification(mod(randMap,k)==i-1);
+    remaining=trainingData(mod(randMap,k)~=i-1,:,:);
+    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,1000);
+    disp(datestr(datetime('now')))
+    
+    [predictions,accurancy(i,:),pvalues]=svmpredict(leaveClasses,leaveOut(:,:),model);
+    cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
+    %cf: for each cue what was the prediction (maybe colourCoded)
+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,'/nfs/wsi/ti/messor/hohlochj/plots/cmEMG200ms1sPause.fig','fig');