How to decompose and verify EEG data to bands.

版本 1.0.0 (18.6 MB) 作者: Mahsa Hassankashi
There is EEG signal (one channel). It has been decomposed to gamma, beta, alpha, theta, delta and code makes multiple verifications plots.
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There is EEG signal (one channel). It has been decomposed to gamma, beta, alpha, theta, delta and code makes multiple verifications plots.
wavedec example: [ c , l ] = wavedec( x , n , wname ) returns the wavelet decomposition of the 1-D signal x at level n using the wavelet wname . The output decomposition structure consists of the wavelet decomposition vector c and the bookkeeping vector l , which contains the number of coefficients by level.
waverec performs a multilevel one-dimensional wavelet reconstruction using either a specific wavelet ('wname', see wfilters) or specific reconstruction filters (Lo_R and Hi_R). waverec is the inverse function of wavedec in the sense that the abstract statement waverec(wavedec(X,N,'wname'),'wname') returns X.
EEG_data_C3_4 = csvread('A-eeg.csv');
V = EEG_data_C3_4(:,1);
N=length(EEG_data_C3_4(:,1));
yy1=bandpass(V,[4 30],128);
waveletFunction = 'db8';
[C,L] = wavedec(V,8,waveletFunction); % Denoise EOG from EEG
cD1 = detcoef(C,L,1);
cD2 = detcoef(C,L,2);
cD3 = detcoef(C,L,3);
cD4 = detcoef(C,L,4);
cD5 = detcoef(C,L,5); %GAMMA
cD6 = detcoef(C,L,6); %BETA
cD7 = detcoef(C,L,7); %ALPHA
cD8 = detcoef(C,L,8); %THETA
cA8 = appcoef(C,L,waveletFunction,5); %DELTA
D1 = wrcoef('d',C,L,waveletFunction,1); %NOISY
A1 = wrcoef('a',C,L,waveletFunction,1); %Level 1 Approximation
D2 = wrcoef('d',C,L,waveletFunction,2); %NOISY
A2 = wrcoef('a',C,L,waveletFunction,2); %Level 2 Approximation
D3 = wrcoef('d',C,L,waveletFunction,3); %NOISY
A3 = wrcoef('a',C,L,waveletFunction,3); %Level 3 Approximation
D4 = wrcoef('d',C,L,waveletFunction,4); %NOISY
A4 = wrcoef('a',C,L,waveletFunction,4); %Level 4 Approximation
D5 = wrcoef('d',C,L,waveletFunction,5); %GAMMA
A5 = wrcoef('a',C,L,waveletFunction,5); %Level 5 Approximation
D6 = wrcoef('d',C,L,waveletFunction,6); %BETA
A6 = wrcoef('a',C,L,waveletFunction,6); %Level 6 Approximation
D7 = wrcoef('d',C,L,waveletFunction,7); %ALPHA
A7 = wrcoef('a',C,L,waveletFunction,7); %Level 7 Approximation
D8 = wrcoef('d',C,L,waveletFunction,8); %THETA 4-7
A8 = wrcoef('a',C,L,waveletFunction,8); %DELTA 1-4 Level 8 Approximation
figure;
subplot(2,1,1);plot(V);title('Original'); axis off
subplot(2,1,2);plot(A8);title('Level 8 Approximation');axis off
sgtitle('wrcoef Approximation vs Original eeg');
fs=128;
fmin = 30; % minimum passband frequency in Hz (High Gamma) 30
fmax =50; % maximum passband frequency in Hz (High Gamma) 50
Rs = 20; % stopband attenuation in dB 20
Rp = 1; % passband ripples in dB 1
% for High gamma band
[order, Wn] = ellipord([fmin/(fs/2), fmax/(fs/2)], [(fmin-1)/(fs/2), (fmax+1)/(fs/2)], Rp, Rs);
[B,A] = ellip(order, Rp, Rs, [fmin/(fs/2), fmax/(fs/2)]);
%filtering of entire data into High Gamma band
data_hgamma = filtfilt(B,A,V);
figure;
subplot(3,1,1);plot(D6);title('wavelet'); axis off
subplot(3,1,2);plot(data_hgamma);title('ellipord'); axis off
sgtitle('waverec gamma vs ellipord gamma');
dLen = length(D7);
dF = fs/dLen;
f = dF*(0:dLen-1)';
A0 = waverec(C,L,'db8');
err = max(abs(V-A0));
disp(err);
Cnew = C;
tmp = cumsum(L);
Cnew(tmp(end-2)+1:tmp(end-1)) = 0;
Rec_signal=waverec(Cnew,L,'db8');
figure;
subplot(4,1,1);plot(V,'k');title('Original'); axis off
subplot(4,1,2); plot(Rec_signal,'r','linewidth',2);title('Reconstruct');axis off
sgtitle('waverec reconstruct wavelet');

引用格式

Mahsa Hassankashi (2024). How to decompose and verify EEG data to bands. (https://www.mathworks.com/matlabcentral/fileexchange/97072-how-to-decompose-and-verify-eeg-data-to-bands), MATLAB Central File Exchange. 检索时间: .

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Verify Decomposition

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1.0.0