diff --git a/readEEG.m b/readEEG.m
deleted file mode 100644
index ad4d469..0000000
--- a/readEEG.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function readEEG(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
-
-    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
-
-    trainingDataEEGcell=cell(maxFile,1);
-    trainingDataEMGcell=cell(maxFile,1);
-    classesCell=cell(maxFile,1);
-
-    for i=1:maxFile
-        [sig, stat, params] = load_bcidat(sprintf('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,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(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),[trainingDataEEG,trainingDataEMG,classification]);
-    
-
-    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
-end
\ No newline at end of file
diff --git a/readEEGEciton.m b/readEEGEciton.m
index beeac35..151c50f 100644
--- a/readEEGEciton.m
+++ b/readEEGEciton.m
@@ -7,7 +7,7 @@
     classesCell=cell(maxFile,1);
 
     for i=1:maxFile
-        [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/origData/%s/%s_B100%i/AO_B1S001R0%i.dat',subject,subject,number,i));
+        [sig, stat, params] = load_bcidat(sprintf('/nfs/wsi/ti/messor/hohlochj/origData/%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,i));
         [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
         classesCell{i}=stat.StimulusCode;
         fprintf('%ith file processed\n',i)
@@ -35,7 +35,7 @@
     classification=smoothClassification(smoothClassification~=-1);
 
     clear smoothClassification i
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),trainingDataEEG,trainingDataEMG,classification);
+    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),[trainingDataEEG,trainingDataEMG,classification]);
     
 
     fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));