diff --git a/readEciton.m b/readEciton.m
new file mode 100644
index 0000000..205a4ca
--- /dev/null
+++ b/readEciton.m
@@ -0,0 +1,55 @@
+close all
+clear variables
+%params
+disp('start')
+disp(datestr(datetime('now')))
+windowEMG=0.2;
+windowEEG=1;
+shift=0.2;
+k=10;
+maxExpC=5;
+maxFile=5;
+threshold=7500;
+pburgOrder=32;
+minEEGFreq=0; %Hz
+maxEEGFreq=200; %Hz
+
+
+trainingDataEEGcell=cell(maxFile,1);
+trainingDataEMGcell=cell(maxFile,1);
+classesCell=cell(maxFile,1);
+
+parfor i=1:maxFile
+    [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/Block1_ReachingMovements/AO/AO_B1001/AO_B1S001R0%i.dat',i));
+    [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
+    classesCell{i}=stat.StimulusCode;
+    fprintf('%ith file processed\n',i)
+end
+
+clear sig
+
+trainingDataEEG=cell2mat(trainingDataEEGcell);
+trainingDataEMG=cell2mat(trainingDataEMGcell);
+classesMat=cell2mat(classesCell);
+clear trainingDataEEGcell trainingDataEMGcell classesCell
+
+classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
+clear classesMat
+
+smoothClassification=zeros(size(classificationWithPause));
+for i=1:size(classificationWithPause,1)
+    smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
+end
+
+clear classificationWithPause
+
+trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
+trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
+classification=smoothClassification(smoothClassification~=-1);
+
+clear smoothClassification i
+save /nfs/wsi/ti/messor/hohlochj/matlabData/AO1200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat
+
+
+disp('end')
+disp(datestr(datetime('now')))
diff --git a/svmEciton.m b/svmEciton.m
new file mode 100644
index 0000000..3dfd146
--- /dev/null
+++ b/svmEciton.m
@@ -0,0 +1,44 @@
+close all
+clear variables
+clc
+
+load('/nfs/wsi/ti/messor/hohlochj/matlabData/AO1200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat');
+
+%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');