How to Design a Band-Pass Filter

Hello everyone,

I've been having issues to create a band pass filter to treat EMG signals. I want to create a band pass filter of bandwith 85 (10-95) and my sampling rate is 200 Hz. After that, I would like to plot the frequency spectrum of the signal to verify that I filtered it correctly. Could someone help me with that? I appreciate any kind of help.

Answer 1

[Mark Sherstan]

Check out the bandpass filter from the signal processing toolbox located here. I would imagine your function would look something like this (a similar question was asked here):

% signalEMG = Your data
filteredEMG = bandpass(signalEMG,[10 95],200)

Plot the results using a FFT (you may need to tweak a few of the values such as L depending on your data):

Fs = 200;
T = 1/Fs;
L = 1500;
t = (0:L-1)*T;

Y = fft(filteredEMG);

P2 = abs(Y/L);
P1 = P2(1:L/2+1);
P1(2:end-1) = 2*P1(2:end-1);
f = Fs*(0:(L/2))/L;

plot(f,P1) 
title('Single-Sided Amplitude Spectrum of X(t)')
xlabel('f (Hz)')
ylabel('|P1(f)|')

Answer 2

[Star Strider]

Try this:

EMG = rand(1,1E+4);                                         % EMG Signal
N = length(EMG);
Fs = 200;                                                   % Sampling Frequency (Hz)
t = linspace(0, N, N)/Fs;                                   % Time Vector (If One Has Not Been Supplied With Your EEG Record)
Fn = Fs/2;                                                  % Nyquist Frequency (Hz)
Wp = [10    95]/Fn;                                         % Passband Frequency Vector (Normalised)
Ws = [ 9    96]/Fn;                                         % Stopband Frequency Vector (Normalised)
% Wp = 3.5/Fn;
% Ws = 2.5/Fn;
Rp =   1;                                                   % Passband Ripple (dB)
Rs =  50;                                                   % Stopband Attenuation (dB)
[n,Wp] = ellipord(Wp,Ws,Rp,Rs);                             % Calculate Filter Order
[z,p,k] = ellip(n,Rp,Rs,Wp,'bandpass');                     % Default Here Is A Lowpass Filter
[sos,g] = zp2sos(z,p,k);                                    % Use Second-Order-Section Implementation For Stability
EMG_filtered = filtfilt(sos,g,EMG);                         % Filter Signal (Here: ‘x’)

figure
freqz(sos, 2^14, Fs)                                        % Bode Plot Of Filter
% set(subplot(2,1,1), 'XLim',[0 15])                          % Optional, Change Limits As Necessary
% set(subplot(2,1,2), 'XLim',[0 15])                          % Optional, Change Limits As Necessary% x = rand(1,1E+4);

nfft = 2^nextpow2(N);
FEMG_filtered = fft(EMG_filtered)/N;
Fv = linspace(0, 1, fix(N/2)+1)*Fn;                         % Frequency Vector
Iv = 1:length(Fv);                                          % Index Vector

figure
plot(Fv, abs(FEMG_filtered(Iv))*2)
grid

[Ben]

Star Strider, the output signal here is FEMG_filtered or EMG_Filtered? FEMG is a complex signal.Gracias, Ben

[Star Strider]

Ben — The filtered output is ‘EMG_filtered’. The Fourier transform of it is ‘FEMG_filtered’. The purpose of that is to demonstrate the effect of the filtering.