基于Matlab的脑电波信号处理

做脑电波信号处理滴嘿嘿。

Matlab addicted
Codes
%FEATURE EXTRACTER
function [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];
end
end
for 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];
end
end
%%%%% 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];
end
end
%%%%%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 power fdelta(l) = sum(Pxx(find(f<a)));
ftheta(l) = sum(Pxx(find(f<b&f>a)));
falpha(l) = sum(Pxx(find(f<d&f>b)));
fbeta(l) = sum(Pxx(find(f<e&f>d)));
fgama(l)= sum(Pxx(find(f>e))); % gama wave included for additional work
end
features = [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 - Gamma
[featurestrain] = EEGfeature(trainfilename, m);
% modification to EEGfeaturemod function for varying testing training ratio
[featurestest] = EEGfeature(testfilename,n);
training = [featurestrain(:,p) featurestrain(:,q)];
% modify how many features to extract here
sample = [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','linear'as default
[classall, 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 = 2:1:4
[clas, er]= EEGclassification('ha11train',50,'ha11test', 10, p,q);
if class == 0; % appending newly generated classificaton and error
class = clas;
else class = [class clas];
end
if err ==0;
err = er;
else err = [err er];
end
end
p = 2;
for q = 3:4
[clas, er]= EEGclassification('ha11train',50,'ha11test',10, p,q);
class = [class clas];err = [err er]; % appending newly generated classificaton and error end
p=3;q=4;
[clas, er,classall, errall]= EEGclassification('ha11train',50,'ha11test', 10, p,q);
class = [class clas classall];err = [err er errall];
results = [class;err]; % displaying the results in a table
display (results);
%------------------ using 2 features out of 5
...。