function readEEGEciton(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('/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)
    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','-v7.3');
    

    %fprintf('end read %s%i %s\n',subject,number,datestr(datetime('now')));
end