EMG data comparison problem

I have some sEMG data sets that I want to compare. How can I do this with Matlab?

%% Analysis of EMG data

%An EMG signal has a frequency between 0 to 500 Hz; but the usable

%frequency is between 50 and 150 Hz.

% EMG signal is produced using an instrument called the electromyograph that records electric activity

% produced by muscles.

% The amplitude for surface EMG signals is within the range of 0 to 10 mV.

% sEMG (surace EMG) signals tend to have a DC bias, therefore a high pass

% filter is applied to a cutoff frequency of 50Hz. This high pass filter

% removes any DC bias.

% The root mean-square (RMS): calculated by finding the square root of: squaring each data point,

%summing the squares, dividing the sum by the number of observations.

% RMS value of a signal is a measure of the power of the signal and it

% reflects on the activity in the motor unit during contraction.

% This makes RMS useful in producing waveforms.

% Amplitude of an EMG signal is random with Gaussian Distribution/

% RMS and MA are two parameters used to measure the amplitude.

% MA short for: Mean Absolute value: is a mean of the absolute value of

% EMG signal

% MA is a measure of the area under the rectified EMG.

% In order to calculate MA remove all of the negative phases of the raw EMG

% This is known as: half-wave rectification.

% The MA and RMS provide an estimate of the amplitude of the raw EMG signal.

clear,clc

load('A1_Ball_high_t2.mat')

t = t2(:,1);

y1 = t2(:,2);

N = length(y1);% find the length of the data per second

ls = size(y1); %% size

f = 1/N;% find the sampling rate or frequency

%Note: sampling frequency should be at least twice the incoming signal.

fs = 2000;

T = 1/fs % period between each sample

t1 = (0 : N-1) *T;

t = (0:1:length(y1)-1)/fs; % sampling period

% nyquist frequency is half of the sampling rate of a signal. Also known as:

% folding frequency.

Nyquist = fs/2;

figure;

%

subplot (3,1,1), plot(t,y1,'b');

title ('EMG signal of single muscle 25 years old patient ');

xlabel ('time (sec)');

ylabel ('Amplitute (V)');

grid on;

Y= abs(fft(y1));

Y(1) = [];

power = abs(Y(1:N/2)).^2;

nyquist = 350;

freq = (1:N/2)/(N/2)*nyquist;

subplot(212), plot(freq,power), grid on

xlabel('Sample number (in Frequency)')

ylabel('Power spectrumen');

title({'Single-sided Power spectrum' ...

' (Frequency in shown on a log scale)'});

axis tight

%%% RMS of the signal

rms_y1 = sqrt(mean(y1.^2));

msgbox(strcat('RMS of EMG signal is = ',mat2str(rms_y1), ''));

rms_emg = rms (y1);

%%%%% MA of the signal

ma_y1 = abs(mean(y1));

msgbox(strcat('MA of EMG signal is = ',mat2str(ma_y1), ''));

%remove any DC offset of the signal

% use the detrend function.

% It subtracts the mean or a best-fit line from data.

y2 = detrend(y1); %% y2 is the singal after DC offset removed.

figure;

rec_y = abs(y2);

plot (rec_y);

xlabel('Sample number (in Frequency)')

ylabel('Rectified EMG signal');

title({'Rectified EMG signal' ...

' (Frequency shown on a log scale)'});

figure;

xdft = fft(y1);

xdft = xdft(1:N/2+1);

psdx = (1/(fs*N)) * abs(xdft).^2;

psdx(2:end-1) = 2*psdx(2:end-1);

freq = 0:fs/length(y1):fs/2;

plot(freq,10*log10(psdx))

grid on

title(' Power spectrum FFT')

xlabel('Frequency (Hz)')

ylabel('Power/Frequency (dB/Hz)')

%amplitude of the entire signal

amplitude = max(y2(:)) - min(y2(:))

%%% DFT to describe the signal in the frequency

NFFT = 2 ^ nextpow2(N);

% The reason I used this function above:

%The execution time for fft depends on the length of the transform.

% It is fastest for powers of two. It is almost as fast for lengths that

%have only small prime factors. It is typically several times slower for

%lengths that are prime or which have large prime factors.

Y = fft(y1, NFFT) / N;

f = (fs / 2 * linspace(0, 1, NFFT / 2+1))'; % Vector containing frequencies in Hz

amp = ( 2 * abs(Y(1: NFFT / 2+1))) % Vector containing corresponding amplitudes

figure;

plot (f, amp);

title ('plot single-sided amplitude spectrum of the EMG signal')

xlabel ('frequency (Hz)')

ylabel ('|y(f)|')

grid on;

References:

Ali H. Al-Timemy, Rami N. Khushaba, Guido Bugmann, and Javier Escudero, " Improving the Performance against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees", IEEE Transactions on Neural Systems and Rehabilitation Engineering, Accepted for Publication, 2015.

http://biomedicalsignalandimage.blogspot.com/2016/02/matlab-code-to-study-emg-signal.html

Answer 1

[Francisco Romero]

Hi José,

I saw to this question by looking for other things.

You may find this link useful:

https://es.mathworks.com/help/signal/ug/measuring-signal-similarities.html