基于Matlab的脑电波信号处理

做脑电波信号处理滴嘿嘿。Matlab addictedCodes%FEATURE EXTRACTERfunction features = EEGfeaturetrainmod(filename,m)a = 4;b = 7;d = 12;e = 30;signals = 0;for index = 1:9; % read in the first ten EEG data because the files are numbered as ha11test01 rather than ha11test1. s = filename 0 num2str(index) .dat; signal = tread_wfdb(s); if signals = 0; signals = signal; else signals = signals signal; endendfor index = 10:1:m/2; % read in the rest of the EEG training data s = filename num2str(index) .dat; signal = tread_wfdb(s); if signals = 0; signals = signal; else signals = signals signal; endend% modification just for varying the training testing ratio -for index = 25:1:25+m/2; % read in the rest of the EEG training data s = filename num2str(index) .dat; signal = tread_wfdb(s); if signals = 0; signals = signal; else signals = signals signal; endend%end of modification just for varying the training testing ratio- for l = 1:m % exrating features (power of each kind of EEG wave forms)Pxx,f=pwelch(signals(:,l)-mean(signals(:,l), , , , 200); % relative powerfdelta(l) = sum(Pxx(find(fa);ftheta(l) = sum(Pxx(find(fa);falpha(l) = sum(Pxx(find(fb);fbeta(l) = sum(Pxx(find(fd);fgama(l)= sum(Pxx(find(fe); % gama wave included for additional workendfeatures = fdelta; ftheta; falpha; fbeta a; fgama;features = features;end %CLASSIFIER%(Has three similar classification modifation: EEGclassification, EEGclassificationmod and EEGclassificationmod1 saved and used in the running file for additional works) function class, err, classall, errall= EEGclassification(trainfilename, m, testfilename,n, p,q)% p - waveform 1, q - wave form two o wave form three% 1 - delta 2 - theta 3 - alpha 4 beta 5 - Gammafeaturestrain = EEGfeature(trainfilename, m);% modification to EEGfeaturemod function for varying testing training ratiofeaturestest = EEGfeature(testfilename,n);training = featurestrain(:,p) featurestrain(:,q);% modify how many features to extract heresample = featurestest(:,p) featurestest(:,q);group = ones(m/2,1);2*ones(m/2,1); traininga = featurestrain;samplea = featurestest; class, err, POSTERIOR, logp, coeff= classify(sample, training, group, quadratic); %mahalanobis,quadratic,linearas defaultclassall, errall= classify(samplea, traininga, group, quadratic); display(class);display(err); % running file%- using 2 features out of 4 comparison -class = 0; err = 0;p = 1;for q = 2:1:4clas, er= EEGclassification(ha11train,50,ha11test, 10, p,q);if class = 0; % appending newly generated classificaton and error class = clas;else class = class clas;endif err =0; err = er;else err = err er;endendp = 2;for q = 3:4clas, er= EEGclassification(ha11train,50,ha11test,10, p,q);class = class clas;err = err er; % appending newly generated classificaton and errorendp=3;q=4;clas, er,classall, errall= EEGclassification(ha11train,50,ha11test, 10, p,q);clas