diff --git a/text/TODO.txt b/text/TODO.txt
index 1b5d439..103bf79 100644
--- a/text/TODO.txt
+++ b/text/TODO.txt
@@ -2,8 +2,10 @@
 
 Alle VP auf eciton (done)
 
+pBurgOrder bestimmen
+
 Matching Bewegung EEG
-  - durch interpolation (ridge)
+  - durch ridge-Regression (EEG als feature)
 
 
 
diff --git a/usedMcode/readEEG.m b/usedMcode/readEEG.m
index f7d1925..142dea6 100644
--- a/usedMcode/readEEG.m
+++ b/usedMcode/readEEG.m
@@ -1,16 +1,18 @@
-function readEEG(subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
+function readEEG(pathToFile, subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq)
 
-    fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
+    %fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
 
     trainingDataEEGcell=cell(maxFile,1);
     trainingDataEMGcell=cell(maxFile,1);
     classesCell=cell(maxFile,1);
+    kin=cell([maxFile,1]);
 
     for i=1:maxFile
-        [sig, stat, params] = load_bcidat(sprintf('/home/jph/Uni/masterarbeit/Block1_ReachingMovements/%s/%s_B100%i/AO_B1S001R0%i.dat',subject,subject,number,i));
-        [trainingDataEEGcell{i},trainingDataEMGcell{i}]=generateTrainingData(sig,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
+        [sig, stat, params] = load_bcidat(strcat(pathToFile,sprintf('%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,i)));
+        tmpKin=csvread(strcat(pathToFile,sprintf('kin/%s_B1S00%iR0%i.txt',subject,number,i)),1,0);
+        [trainingDataEEGcell{i},trainingDataEMGcell{i},kin{i}]=generateTrainingData(sig,tmpKin,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
         classesCell{i}=stat.StimulusCode;
-        fprintf('%ith file processed\n',i)
+        %fprintf('%ith file processed\n',i)
     end
 
     clear sig
@@ -18,7 +20,8 @@
     trainingDataEEG=cell2mat(trainingDataEEGcell);
     trainingDataEMG=cell2mat(trainingDataEMGcell);
     classesMat=cell2mat(classesCell);
-    clear trainingDataEEGcell trainingDataEMGcell classesCell
+    kinMat=cell2mat(kin);
+    clear trainingDataEEGcell trainingDataEMGcell classesCell kin
 
     classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
     clear classesMat
@@ -33,10 +36,11 @@
     trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
     trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
     classification=smoothClassification(smoothClassification~=-1);
+    kinematics=kinMat(smoothClassification~=-1,:);
 
     clear smoothClassification i
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),trainingDataEEG,trainingDataEMG,classification);
+    save(strcat(pathToFile,sprintf('../matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number)),'trainingDataEEG','trainingDataEMG','classification','kinematics','-v7.3');
     
 
-    fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
-end
\ No newline at end of file
+    %fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
+end
diff --git a/usedMcode/readEEGEciton.m b/usedMcode/readEEGEciton.m
deleted file mode 100644
index 316f1a7..0000000
--- a/usedMcode/readEEGEciton.m
+++ /dev/null
@@ -1,46 +0,0 @@
-function readEEGEciton(pathToFile, 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);
-    kin=cell([maxFile,1]);
-
-    for i=1:maxFile
-        [sig, stat, params] = load_bcidat(strcat(pathToFile,sprintf('%s/%s_B100%i/%s_B1S00%iR0%i.dat',subject,subject,number,subject,number,i)));
-        tmpKin=csvread(strcat(pathToFile,sprintf('kin/%s_B1S00%iR0%i.txt',subject,number,i)),1,0);
-        [trainingDataEEGcell{i},trainingDataEMGcell{i},kin{i}]=generateTrainingData(sig,tmpKin,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);
-    kinMat=cell2mat(kin);
-    clear trainingDataEEGcell trainingDataEMGcell classesCell kin
-
-    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);
-    kinematics=kinMat(smoothClassification~=-1,:);
-
-    clear smoothClassification i
-    save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number),'trainingDataEEG','trainingDataEMG','classification','kinematics','-v7.3');
-    
-
-    %fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
-end