diff --git a/usedMcode/readAll.m b/usedMcode/readAll.m
new file mode 100644
index 0000000..78f28fe
--- /dev/null
+++ b/usedMcode/readAll.m
@@ -0,0 +1,55 @@
+function [savePath]=readEEG(pathToFile, subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause,noLFsamples)
+
+    %fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
+    savePath=strcat(pathToFile,sprintf('../matlabData/%s%i%imsWindowEMG%isWindowEEG%imsShiftFreq%ito%iPause%ipBurg%i.mat',subject,number,windowEMG*1000,windowEEG,shift*1000,minEEGFreq,maxEEGFreq,pause,pburgOrder));
+    
+    %only create file if it doesn't exist yet
+    if exist(savePath, 'file') ~= 2
+        fprintf(strcat(savePath,' not existing; creating\n'));
+        trainingDataEEGcell=cell(maxFile,1);
+        trainingDataEEGlfCell=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},trainingDataEEGlfCell{i},trainingDataEMGcell{i},kin{i}]=generateTrainingData(sig,tmpKin(:,1:4),windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq,noLFsamples);
+            classesCell{i}=stat.StimulusCode;
+            %fprintf('%ith file processed\n',i)
+        end
+        bci_sf=params.SamplingRate.NumericValue;
+
+        clear sig
+
+        trainingDataEEG=cell2mat(trainingDataEEGcell);
+        trainingDataEEGlf=cell2mat(trainingDataEEGlfCell);
+        trainingDataEMG=cell2mat(trainingDataEMGcell);
+        classesMat=cell2mat(classesCell);
+        kinMat=cell2mat(kin);
+        clear trainingDataEEGcell trainingDataEMGcell classesCell kin
+
+        classificationWithPause=classifyAccordingToEMG(size(trainingDataEEG,1), trainingDataEMG,classesMat,shift,bci_sf,threshold,pause);
+        clear classesMat
+
+        smoothClassification=zeros(size(classificationWithPause));
+        for i=1:size(classificationWithPause,1)
+            smoothClassification(i)=round(mode(classificationWithPause(max(i-fix(bci_sf/1000),1):min(i+fix(bci_sf/1000),end))));       
+        end
+
+        clear classificationWithPause
+
+        trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
+        trainingDataEEGlf=trainingDataEEGlf(smoothClassification~=-1,:,:);
+        trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
+        classification=smoothClassification(smoothClassification~=-1);
+        kinematics=kinMat(smoothClassification~=-1,:);
+
+        clear smoothClassification i
+        save(savePath,'trainingDataEEG','trainingDataEEGlf','trainingDataEMG','classification','kinematics','-v7.3');
+
+
+        %fprintf('finished reading %s%i %s\n',subject,number,datestr(datetime('now')));
+    end
+end
diff --git a/usedMcode/readAllPos.m b/usedMcode/readAllPos.m
new file mode 100644
index 0000000..dd6f85b
--- /dev/null
+++ b/usedMcode/readAllPos.m
@@ -0,0 +1,52 @@
+function [savePath]=readEEGPos(pathToFile, subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause)
+
+    %fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
+    savePath=strcat(pathToFile,sprintf('../matlabData/%s%i%imsWindowEMG%isWindowEEG%imsShiftFreq%ito%iPause%ipBurg%iPos.mat',subject,number,windowEMG*1000,windowEEG,shift*1000,minEEGFreq,maxEEGFreq,pause,pburgOrder));
+    
+    %only create file if it doesn't exist yet
+    if exist(savePath, 'file') ~= 2
+        fprintf(strcat(savePath,' not existing; creating\n'));
+        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}]=generateTrainingDataPos(sig,tmpKin(:,1:4),windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
+            classesCell{i}=stat.StimulusCode;
+            %fprintf('%ith file processed\n',i)
+        end
+        bci_sf=params.SamplingRate.NumericValue;
+
+        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,bci_sf,threshold,pause);
+        clear classesMat
+
+        smoothClassification=zeros(size(classificationWithPause));
+        for i=1:size(classificationWithPause,1)
+            smoothClassification(i)=round(mode(classificationWithPause(max(i-fix(bci_sf/1000),1):min(i+fix(bci_sf/1000),end))));       
+        end
+
+        clear classificationWithPause
+
+        trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
+        trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
+        classification=smoothClassification(smoothClassification~=-1);
+        kinematics=kinMat(smoothClassification~=-1,:);
+
+        clear smoothClassification i
+        save(savePath,'trainingDataEEG','trainingDataEMG','classification','kinematics','-v7.3');
+
+
+        %fprintf('finished reading %s%i %s\n',subject,number,datestr(datetime('now')));
+    end
+end
diff --git a/usedMcode/readEEG.m b/usedMcode/readEEG.m
deleted file mode 100644
index 78f28fe..0000000
--- a/usedMcode/readEEG.m
+++ /dev/null
@@ -1,55 +0,0 @@
-function [savePath]=readEEG(pathToFile, subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause,noLFsamples)
-
-    %fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
-    savePath=strcat(pathToFile,sprintf('../matlabData/%s%i%imsWindowEMG%isWindowEEG%imsShiftFreq%ito%iPause%ipBurg%i.mat',subject,number,windowEMG*1000,windowEEG,shift*1000,minEEGFreq,maxEEGFreq,pause,pburgOrder));
-    
-    %only create file if it doesn't exist yet
-    if exist(savePath, 'file') ~= 2
-        fprintf(strcat(savePath,' not existing; creating\n'));
-        trainingDataEEGcell=cell(maxFile,1);
-        trainingDataEEGlfCell=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},trainingDataEEGlfCell{i},trainingDataEMGcell{i},kin{i}]=generateTrainingData(sig,tmpKin(:,1:4),windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq,noLFsamples);
-            classesCell{i}=stat.StimulusCode;
-            %fprintf('%ith file processed\n',i)
-        end
-        bci_sf=params.SamplingRate.NumericValue;
-
-        clear sig
-
-        trainingDataEEG=cell2mat(trainingDataEEGcell);
-        trainingDataEEGlf=cell2mat(trainingDataEEGlfCell);
-        trainingDataEMG=cell2mat(trainingDataEMGcell);
-        classesMat=cell2mat(classesCell);
-        kinMat=cell2mat(kin);
-        clear trainingDataEEGcell trainingDataEMGcell classesCell kin
-
-        classificationWithPause=classifyAccordingToEMG(size(trainingDataEEG,1), trainingDataEMG,classesMat,shift,bci_sf,threshold,pause);
-        clear classesMat
-
-        smoothClassification=zeros(size(classificationWithPause));
-        for i=1:size(classificationWithPause,1)
-            smoothClassification(i)=round(mode(classificationWithPause(max(i-fix(bci_sf/1000),1):min(i+fix(bci_sf/1000),end))));       
-        end
-
-        clear classificationWithPause
-
-        trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
-        trainingDataEEGlf=trainingDataEEGlf(smoothClassification~=-1,:,:);
-        trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
-        classification=smoothClassification(smoothClassification~=-1);
-        kinematics=kinMat(smoothClassification~=-1,:);
-
-        clear smoothClassification i
-        save(savePath,'trainingDataEEG','trainingDataEEGlf','trainingDataEMG','classification','kinematics','-v7.3');
-
-
-        %fprintf('finished reading %s%i %s\n',subject,number,datestr(datetime('now')));
-    end
-end
diff --git a/usedMcode/readEEGPos.m b/usedMcode/readEEGPos.m
deleted file mode 100644
index dd6f85b..0000000
--- a/usedMcode/readEEGPos.m
+++ /dev/null
@@ -1,52 +0,0 @@
-function [savePath]=readEEGPos(pathToFile, subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq,pause)
-
-    %fprintf('start read %s%i %s\n',subject,number,datestr(datetime('now')));
-    savePath=strcat(pathToFile,sprintf('../matlabData/%s%i%imsWindowEMG%isWindowEEG%imsShiftFreq%ito%iPause%ipBurg%iPos.mat',subject,number,windowEMG*1000,windowEEG,shift*1000,minEEGFreq,maxEEGFreq,pause,pburgOrder));
-    
-    %only create file if it doesn't exist yet
-    if exist(savePath, 'file') ~= 2
-        fprintf(strcat(savePath,' not existing; creating\n'));
-        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}]=generateTrainingDataPos(sig,tmpKin(:,1:4),windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
-            classesCell{i}=stat.StimulusCode;
-            %fprintf('%ith file processed\n',i)
-        end
-        bci_sf=params.SamplingRate.NumericValue;
-
-        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,bci_sf,threshold,pause);
-        clear classesMat
-
-        smoothClassification=zeros(size(classificationWithPause));
-        for i=1:size(classificationWithPause,1)
-            smoothClassification(i)=round(mode(classificationWithPause(max(i-fix(bci_sf/1000),1):min(i+fix(bci_sf/1000),end))));       
-        end
-
-        clear classificationWithPause
-
-        trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
-        trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
-        classification=smoothClassification(smoothClassification~=-1);
-        kinematics=kinMat(smoothClassification~=-1,:);
-
-        clear smoothClassification i
-        save(savePath,'trainingDataEEG','trainingDataEMG','classification','kinematics','-v7.3');
-
-
-        %fprintf('finished reading %s%i %s\n',subject,number,datestr(datetime('now')));
-    end
-end