How to band pass filter the EMG signal in real time?
%#######################################
% In signal processing, the power spectrum of a continuous time signal describes
% the distribution of power into frequency components. composing that signal. According to Fourier analysis,
% any physical signal can be decomposed into a number of discrete frequencies,
% or a spectrum of frequencies over a continuous range.
% Get the full wave rectification of the EMG signal by measuring it’s
% absolute value.
% ######################################
clc;
clear;
close all;
% Parameters
sampling_frequency = 1000; % Sampling frequency in Hz
duration = 5; % Duration of signal acquisition in seconds
num_samples = sampling_frequency * duration; % Sampling_frequency = (No. of Samples) / Time Interval
% Initialize variables
time = (0:num_samples-1)/sampling_frequency;
frequency = linspace(0, sampling_frequency/2, num_samples/2);
x = zeros(1, num_samples);
a = arduino(); % Check port from device manager and pin is A0
% Create figure for real-time EMG signal
figure(‘Name’, ‘Real-Time EMG Signal Visualization’);
h_real_time = plot(time, x);
grid on;
title(‘Real-Time EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 3]); % Adjust ylim as needed
% Create figure for spectrogram
figure(‘Name’, ‘Real-Time Spectrogram EMG Signal Visualization’);
h_spectrogram = imagesc(‘XData’, [], ‘YData’, [], ‘CData’, []);
axis xy;
xlabel(‘Time (s)’);
ylabel(‘Frequency (Hz)’);
title(‘Spectrogram of EMG Signal’);
colorbar;
% Pre-allocate spectrogram variables
Nspec = 256; % Spectrogram window size
overlap = round(Nspec * 0.85); % 85% overlap
% Filter parameters
filter_order = 4; % Filter order
bandpass_low_cutoff = 10; % Bandpass low cutoff frequency in Hz
bandpass_high_cutoff = 500; % Bandpass high cutoff frequency in Hz
% Design Butterworth bandpass filter
assert(filter_order > 0 && round(filter_order) == filter_order, ‘Filter order must be a positive integer’);
Wn = [bandpass_low_cutoff, bandpass_high_cutoff] / (sampling_frequency / 2);
[b, a] = butter(filter_order, Wn, ‘bandpass’);
% Start loop to acquire signal
for i = 1:num_samples
% Read voltage
voltage = abs(readVoltage(a, ‘A0’));
% Store voltage
x(i) = voltage;
% Update real-time plot
set(h_real_time,’YData’,abs(x));
set(h_real_time,’XData’,time);
drawnow;
% Apply bandpass filtering
if i > filter_order
x_filtered = filtfilt(b, a, abs(x(1:i)));
% Calculate spectrogram for the current signal
if i > Nspec
[S, F, T] = spectrogram(x_filtered, hamming(Nspec), overlap, Nspec, sampling_frequency, ‘yaxis’);
% Update the spectrogram
set(h_spectrogram, ‘XData’, T, ‘YData’, F, ‘CData’, 10*log10(abs(S)));
end
end
drawnow;
end
How do I band pass this signal? And is it right to rectify it after I bandpass filter it?%#######################################
% In signal processing, the power spectrum of a continuous time signal describes
% the distribution of power into frequency components. composing that signal. According to Fourier analysis,
% any physical signal can be decomposed into a number of discrete frequencies,
% or a spectrum of frequencies over a continuous range.
% Get the full wave rectification of the EMG signal by measuring it’s
% absolute value.
% ######################################
clc;
clear;
close all;
% Parameters
sampling_frequency = 1000; % Sampling frequency in Hz
duration = 5; % Duration of signal acquisition in seconds
num_samples = sampling_frequency * duration; % Sampling_frequency = (No. of Samples) / Time Interval
% Initialize variables
time = (0:num_samples-1)/sampling_frequency;
frequency = linspace(0, sampling_frequency/2, num_samples/2);
x = zeros(1, num_samples);
a = arduino(); % Check port from device manager and pin is A0
% Create figure for real-time EMG signal
figure(‘Name’, ‘Real-Time EMG Signal Visualization’);
h_real_time = plot(time, x);
grid on;
title(‘Real-Time EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 3]); % Adjust ylim as needed
% Create figure for spectrogram
figure(‘Name’, ‘Real-Time Spectrogram EMG Signal Visualization’);
h_spectrogram = imagesc(‘XData’, [], ‘YData’, [], ‘CData’, []);
axis xy;
xlabel(‘Time (s)’);
ylabel(‘Frequency (Hz)’);
title(‘Spectrogram of EMG Signal’);
colorbar;
% Pre-allocate spectrogram variables
Nspec = 256; % Spectrogram window size
overlap = round(Nspec * 0.85); % 85% overlap
% Filter parameters
filter_order = 4; % Filter order
bandpass_low_cutoff = 10; % Bandpass low cutoff frequency in Hz
bandpass_high_cutoff = 500; % Bandpass high cutoff frequency in Hz
% Design Butterworth bandpass filter
assert(filter_order > 0 && round(filter_order) == filter_order, ‘Filter order must be a positive integer’);
Wn = [bandpass_low_cutoff, bandpass_high_cutoff] / (sampling_frequency / 2);
[b, a] = butter(filter_order, Wn, ‘bandpass’);
% Start loop to acquire signal
for i = 1:num_samples
% Read voltage
voltage = abs(readVoltage(a, ‘A0’));
% Store voltage
x(i) = voltage;
% Update real-time plot
set(h_real_time,’YData’,abs(x));
set(h_real_time,’XData’,time);
drawnow;
% Apply bandpass filtering
if i > filter_order
x_filtered = filtfilt(b, a, abs(x(1:i)));
% Calculate spectrogram for the current signal
if i > Nspec
[S, F, T] = spectrogram(x_filtered, hamming(Nspec), overlap, Nspec, sampling_frequency, ‘yaxis’);
% Update the spectrogram
set(h_spectrogram, ‘XData’, T, ‘YData’, F, ‘CData’, 10*log10(abs(S)));
end
end
drawnow;
end
How do I band pass this signal? And is it right to rectify it after I bandpass filter it? %#######################################
% In signal processing, the power spectrum of a continuous time signal describes
% the distribution of power into frequency components. composing that signal. According to Fourier analysis,
% any physical signal can be decomposed into a number of discrete frequencies,
% or a spectrum of frequencies over a continuous range.
% Get the full wave rectification of the EMG signal by measuring it’s
% absolute value.
% ######################################
clc;
clear;
close all;
% Parameters
sampling_frequency = 1000; % Sampling frequency in Hz
duration = 5; % Duration of signal acquisition in seconds
num_samples = sampling_frequency * duration; % Sampling_frequency = (No. of Samples) / Time Interval
% Initialize variables
time = (0:num_samples-1)/sampling_frequency;
frequency = linspace(0, sampling_frequency/2, num_samples/2);
x = zeros(1, num_samples);
a = arduino(); % Check port from device manager and pin is A0
% Create figure for real-time EMG signal
figure(‘Name’, ‘Real-Time EMG Signal Visualization’);
h_real_time = plot(time, x);
grid on;
title(‘Real-Time EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 3]); % Adjust ylim as needed
% Create figure for spectrogram
figure(‘Name’, ‘Real-Time Spectrogram EMG Signal Visualization’);
h_spectrogram = imagesc(‘XData’, [], ‘YData’, [], ‘CData’, []);
axis xy;
xlabel(‘Time (s)’);
ylabel(‘Frequency (Hz)’);
title(‘Spectrogram of EMG Signal’);
colorbar;
% Pre-allocate spectrogram variables
Nspec = 256; % Spectrogram window size
overlap = round(Nspec * 0.85); % 85% overlap
% Filter parameters
filter_order = 4; % Filter order
bandpass_low_cutoff = 10; % Bandpass low cutoff frequency in Hz
bandpass_high_cutoff = 500; % Bandpass high cutoff frequency in Hz
% Design Butterworth bandpass filter
assert(filter_order > 0 && round(filter_order) == filter_order, ‘Filter order must be a positive integer’);
Wn = [bandpass_low_cutoff, bandpass_high_cutoff] / (sampling_frequency / 2);
[b, a] = butter(filter_order, Wn, ‘bandpass’);
% Start loop to acquire signal
for i = 1:num_samples
% Read voltage
voltage = abs(readVoltage(a, ‘A0’));
% Store voltage
x(i) = voltage;
% Update real-time plot
set(h_real_time,’YData’,abs(x));
set(h_real_time,’XData’,time);
drawnow;
% Apply bandpass filtering
if i > filter_order
x_filtered = filtfilt(b, a, abs(x(1:i)));
% Calculate spectrogram for the current signal
if i > Nspec
[S, F, T] = spectrogram(x_filtered, hamming(Nspec), overlap, Nspec, sampling_frequency, ‘yaxis’);
% Update the spectrogram
set(h_spectrogram, ‘XData’, T, ‘YData’, F, ‘CData’, 10*log10(abs(S)));
end
end
drawnow;
end
How do I band pass this signal? And is it right to rectify it after I bandpass filter it? bandpass filter, emg signal MATLAB Answers — New Questions
