Need to break down EEG signals into 4 frequency bands

Question

san chir 2016-5-20

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评论： Star Strider 2020-7-21

normal.mat

Hi all, I'm having a bit of a trouble breaking down an EEG signal into these bands, i dont have a wavelet toolbox, would i need it?

 theta = 4 - 7.9 Hz
 Lower Alpha = 7.9 - 10 Hz
 Upper Alpha = 10 - 13 Hz % edited
 Lower Beta = 13 - 17.9 Hz
 Upper Beta = 18 - 24.9 Hz

I have attached the signal EEG recording with this, it has 22 arrays, the eeg channels are from 2 - 15 and the sampling frequency is 128.

thank you!

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Venkatesh chowdary 2019-4-30

Hello sir do you have the code to decompose the eeg signal into 4 parts.If you do have plz send methe code...

shweta nashikar 2019-9-1

Am fresher sir, Please send all frequency band like alpha beta gamma theta delta, separate matlab coding and caculate the parameter like standard deviation,variance,entropy etc

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Answer 1

Star Strider 2016-5-20

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在 MATLAB Online 中打开

You can design stable filters that do what you want relatively easily. Here is one:

Fs = 128;                                               % Sampling Frequency
Fn = Fs/2;                                              % Nyquist Frequency
Wp = [4  7.9]/Fn;                                       % Theta Passband
Ws = Wp .* [0.6  1.05];                                 % Bandstop Frequencies
Rp = 10;                                                % Passband Ripple
Rs = 40;                                                % Stopband Ripple
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);                         % Determine Optimal Order
[b,a] = cheby2(n,Rs,Ws);                                % Transfer Function Coefficients
[sos_theta,g_theta] = tf2sos(b,a);                      % Second-Order-Section For Stability
figure(1)
freqz(sos_theta, 4096, Fs);                             % Filter Bode Plot

You can also put them in a loop:

Wpm = [4  7.9;  7.9  10;  10.1  12.9;  13  17.9;  18  27.9];    % Passband Matrix
Fs = 128;                                                       % Sampling Frequency
Fn = Fs/2;                                                      % Nyquist Frequency
Rp = 10;                                                        % Passband Ripple
Rs = 40;                                                        % Stopband Ripple
for k1 = 1:size(Wpm,1)
    Wp = Wpm(k1,:)/Fn;
    Ws = Wp .* [0.6  1.05];
    [n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);                             % Determine Optimal Order
    [b,a] = cheby2(n,Rs,Ws);                                    % Transfer Function Coefficients
    [sos{k1},g{k1}] = tf2sos(b,a);                              % Second-Order-Section For Stability
end

Please check the ‘Wpm’ entries, since ‘Upper Alpha’ and ‘Lower Alpha’ were the same in your list. I changed ‘Upper Alpha’ to what I guess they should be. The cell arrays correspond to the appropriate second-order-section filter matrices.

I looked at these filters with the freqz function, and I’m happy with them. Don’t worry about the high ‘Rp’ value, since the Cnebyshev Type II filter has a flat passband.

Use the filtfilt function to do the actual filtering. I didn’t specifically load your data and filter it. I just designed the filters. I will filter your data if you’re getting anomalous results and need further help.

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kalarmago 2020-4-2

Dear @Star Strider, I thank you in advace my questions (I am new in signal processing).

I started to work with EEG, I tried to apply your filters, but they give NAN values, so I am processing with the classic bandpass().

What is the advantage of using your filter over bandpass() ?
I think, before to band pass, the signal could be denoised, isn’t it? Which do you suggest?
In which part of processing is useful Fourier Transform or Wavelets T ? I so confused about filtering.

Star Strider 2020-7-21

kalarmago —

1. The bandpass function was introduced in R2018a, two years after I wrote this! (I would currently use ellip and its friends.)

2. There are several ways to denoise a signal. For broadband noise, wdenoise or sgolayfilt are likely best, in my experience at least.

3. I have no idea what you are asking!

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Answer 2

Todd Leonhardt 2016-5-20

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编辑：Todd Leonhardt 2016-5-20

If you have the Signal Processing Toolbox, then you can design 4 bandpass filters, one for each band, using the Filter Design and Analysis Tool (FDATool): http://www.mathworks.com/help/signal/examples/introduction-to-the-filter-design-and-analysis-tool-fdatool.html

You can also do this with a script using various MATLAB functions instead of the GUI FDATool. But if you are unfamiliar with filter design, the GUI makes it easier.

Then you can apply those FIR filters to generate 4 output signals, one for each band.

There are many other ways you could solve this problem as well. This is one approach which should be relatively easy and effective.