diff --git a/usedMcode/classifyAccordingToEMG.m b/usedMcode/classifyAccordingToEMG.m
index a3e3d67..048f540 100644
--- a/usedMcode/classifyAccordingToEMG.m
+++ b/usedMcode/classifyAccordingToEMG.m
@@ -1,8 +1,8 @@
-function [classification]=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG, classes,shift,frequency,threshold,pause)
+function [classification]=classifyAccordingToEMG(sizeTrainingData, trainingDataEMG, classes,shift,frequency,threshold,pause)
 % classifyAccordingToEMG data classified as -1 should be taken out
-    classification=zeros([size(trainingDataEEG,1),1]);
+    classification=zeros([sizeTrainingData,1]);
     oldclass=0;
-    for i=1:size(trainingDataEEG,1)
+    for i=1:sizeTrainingData
         if mean(trainingDataEMG(i,:)) > threshold % is movement
             class=mode(classes(max(round(shift*frequency*i-frequency),1):min(round(i*shift*frequency-1),end)));
             if class <= 5 % task is to move
diff --git a/usedMcode/generateTrainingData.m b/usedMcode/generateTrainingData.m
index 5d94a48..3f9fda5 100644
--- a/usedMcode/generateTrainingData.m
+++ b/usedMcode/generateTrainingData.m
@@ -1,20 +1,26 @@
-function [trainingDataEEG,trainingDataEMG,kinPerSec] = generateTrainingData(signal,kin,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq)
+function [trainingDataEEG,trainingDataEEGlf,trainingDataEMG,kinPerSec] = generateTrainingData(signal,kin,windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq)
     bci_sf=params.SamplingRate.NumericValue;
     signalWindowEMG=bci_sf*windowEMG;
     signalWindowEEG=bci_sf*windowEEG;
     tempEEG=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shift+1),maxEEGFreq-minEEGFreq+1]);
+    tempEEGlf=zeros([32,fix(floor(size(signal,1)/signalWindowEEG-1)*windowEEG/shift+1),maxEEGFreq-minEEGFreq+1]);
     tempEMG=zeros(fix([size(signal,2)-3-32,fix(size(signal,1)/signalWindowEMG-1)*windowEMG/shift+1]));
     
     %Filter around 50Hz and below 2 Hz
     [A,B]= butter(2,[48 52]/(bci_sf/2),'stop');
     [C,D]= butter(2,1/(bci_sf/2),'high');
     [E,F]= butter(2,[148 152]/(bci_sf/2),'stop');
+    
+    % filter for low Frequencies
+    [V,W]= butter(2,0.01/(bci_sf/2),'high');
+    [X,Y]= butter(2,1/(bci_sf/2),'low');
 
     parfor i=1:32 %filter single channel, w/o EMG, HEOG, Synchro and 0s
         tempEEG(i,:,:)=shiftingPburg(filtfilt(double(E),double(F),filtfilt(double(C),double(D),filtfilt(double(A),double(B),double(signal(:,i))))),bci_sf,windowEEG,shift,pburgOrder,minEEGFreq,maxEEGFreq);
-        %filtfilt stat filter
-    end % TODO: 8-30Hz, pburg bei kurzen Fenstern (Ordnung: Je nach SamplingRate, bei 2500: 32-50, Alpha, Beta peaks sollten sichtbar sein)
+        tempEEGlf(i,:,:)=filtfilt(double(X),double(Y),filtfilt(double(V),double(W),double(signal(:,i))))
+    end
     trainingDataEEG=permute(tempEEG,[2 1 3]);
+    trainingDataEEGlf=permute(tempEEGlf,[2 1 3]);
     
     parfor i=33:size(signal,2)-3
         tempEMG(i-32,:,:)=waveformLength(filtfilt(double(E),double(F),filtfilt(double(C),double(D),filtfilt(double(A),double(B),double(signal(:,i))))),bci_sf,windowEMG,shift);
diff --git a/usedMcode/readEEG.m b/usedMcode/readEEG.m
index ca13e5a..d29f41e 100644
--- a/usedMcode/readEEG.m
+++ b/usedMcode/readEEG.m
@@ -7,6 +7,7 @@
     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]);
@@ -14,7 +15,7 @@
         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(:,1:4),windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
+            [trainingDataEEGcell{i},trainingDataEEGlfCell{i},trainingDataEMGcell{i},kin{i}]=generateTrainingData(sig,tmpKin(:,1:4),windowEMG,windowEEG,shift,params,pburgOrder,minEEGFreq,maxEEGFreq);
             classesCell{i}=stat.StimulusCode;
             %fprintf('%ith file processed\n',i)
         end
@@ -23,12 +24,13 @@
         clear sig
 
         trainingDataEEG=cell2mat(trainingDataEEGcell);
+        trainingDataEEGlf=cell2mat(trainingDataEEGlfCell);
         trainingDataEMG=cell2mat(trainingDataEMGcell);
         classesMat=cell2mat(classesCell);
         kinMat=cell2mat(kin);
         clear trainingDataEEGcell trainingDataEMGcell classesCell kin
 
-        classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,bci_sf,threshold,pause);
+        classificationWithPause=classifyAccordingToEMG(size(trainingDataEEG,1), trainingDataEMG,classesMat,shift,bci_sf,threshold,pause);
         clear classesMat
 
         smoothClassification=zeros(size(classificationWithPause));
@@ -39,12 +41,13 @@
         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','trainingDataEMG','classification','kinematics','-v7.3');
+        save(savePath,'trainingDataEEG','trainingDataEEGlf','trainingDataEMG','classification','kinematics','-v7.3');
 
 
         %fprintf('finished reading %s%i %s\n',subject,number,datestr(datetime('now')));