diff --git a/text/TODO.txt b/text/TODO.txt
index 0fce01c..7d15296 100644
--- a/text/TODO.txt
+++ b/text/TODO.txt
@@ -4,14 +4,14 @@
 
 pBurgOrder bestimmen
 
-filter -> filtfilt EMG
-
 Matching Bewegung EEG
   - Passt nicht zusammen - insgesamt ~40s zu wenig kin 
     + synchro Trigger (ch40)
     + upsample_kin (in code)
+    + (solved?)
   - durch ridge-Regression (EMG dann EEG als feature)
   - als Maß correlation
 
+check classification (improved?)
 
 ohne gridsearch vergleichen mit gridsearch
diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index 3d40917..8451bd0 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -3,7 +3,7 @@
 %pathToFile='/home/jph/Uni/masterarbeit/Block1_ReachingMovements/';
 
 maxFile=5;
-threshold=7500; %EMG is classified as movement
+threshold=10000; %EMG is classified as movement
 
 windowEMG=0.2; %try 1s
 windowEEG=1;
diff --git a/usedMcode/classifyAccordingToEMG.m b/usedMcode/classifyAccordingToEMG.m
index 4937188..5482ab0 100644
--- a/usedMcode/classifyAccordingToEMG.m
+++ b/usedMcode/classifyAccordingToEMG.m
@@ -11,7 +11,7 @@
                         classification(j)=-1; %code for pause
                     end
                     for j=max(fix(i-0.5/shift),1):i % half a second before executed movement there should be activity (esp. EEG)
-                        classification(j)=-1; %if -1 there is a pause of 1s befor movement onset
+                        classification(j)=class; %if -1 there is a pause of 1s befor movement onset
                     end
                     oldclass=class;
                 end
diff --git a/usedMcode/readEEG.m b/usedMcode/readEEG.m
index 7a7eace..d1c02e6 100644
--- a/usedMcode/readEEG.m
+++ b/usedMcode/readEEG.m
@@ -14,6 +14,7 @@
         classesCell{i}=stat.StimulusCode;
         %fprintf('%ith file processed\n',i)
     end
+    bci_sf=params.SamplingRate.NumericValue;
 
     clear sig
 
@@ -21,14 +22,14 @@
     trainingDataEMG=cell2mat(trainingDataEMGcell);
     classesMat=cell2mat(classesCell);
     kinMat=cell2mat(kin);
-    %clear trainingDataEEGcell trainingDataEMGcell classesCell kin
+    clear trainingDataEEGcell trainingDataEMGcell classesCell kin
 
-    classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
+    classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,bci_sf,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))));       
+        smoothClassification(i)=round(mode(classificationWithPause(max(i-fix(bci_sf/500),1):min(i+fix(bci_sf/500),end))));       
     end
 
     clear classificationWithPause