Jx-EMGT : Electromyography ( EMG ) Feature Extraction Toolbox

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"Toward Talent Scientist: Sharing and Learning Together" --- Jingwei Too

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Introduction

• This toolbox offers 40 types of EMG features
• The A_Main file demos how the feature extraction methods can be applied using generated sample signal.

Input

• X : signal ( 1 x samples )
• opts : parameter settings ( some methods have parameters: refer here )

Output

• feat : feature vector ( you may use other name like f1 or etc. )

Usage

The main function jfemg is adopted to perform feature extraction. You may switch the method by changing the 'mav' to other abbreviations

• If you wish to extract mean absolute value ( MAV ) then you may write

feat = jfemg('mav', X);

• If you want to extract enhanced wavelenght ( EWL ) then you may write

feat = jfemg('ewl', X);

Example 1 : Extract 5 normal features ( without parameter )

% Generate a sample random signal X
fs = 1000;            % Sampling frequency 
Ts = 1 / fs;          % Period
t  = 0 : Ts : 0.25; 
X  = 0.01 * (cos(2 * pi * fs * t) + randn(1, length(t)));

% Plot sample signal
plot(t,X);  grid on
xlabel('Number of samples');
ylabel('Amplitude');

% Enhanced Mean Absolute Value
f1 = jfemg('emav', X); 
% Average Amplitude Change
f2 = jfemg('aac', X); 
% Waveform Length
f3 = jfemg('wl', X); 
% Maximum Fractal Length 
f4 = jfemg('mfl', X); 
% Root Mean Square
f5 = jfemg('rms', X); 

% Feature vector
feat = [f1, f2, f3, f4, f5];

% Display features
disp(feat)

Example 2 : Extract 3 features with parameter

% Generate a sample random signal X
fs = 1000;            % Sampling frequency 
Ts = 1 / fs;          % Period
t  = 0 : Ts : 0.25; 
X  = 0.01 * (cos(2 * pi * fs * t) + randn(1, length(t)));

% Zeros Crossing
opts.thres = 0.01;
f1 = jfemg('zc', X, opts); 
% Slope Sign Change
opts.thres = 0.01;
f2 = jfemg('ssc', X, opts);
% Temporal Moment
opts.order = 3;
f3 = jfemg('tm', X, opts);

% Feature vector
feat = [f1, f2, f3];

% Display features
disp(feat)

List of available feature extraction methods

• Some methods contain parameter to be adjusted. If you do not set the parameter then the feature will be extracted using default setting
• For convenience, you may extract the feature with parameter using default setting as following. That is, you don't have to set the opts

feat = jfemg('zc', X);

• You can use opts to set the parameter
  • thres : threshold
  • order : the number of orders

No.	Abbreviation	Name	Parameter ( default )
40	'emav'	Enhanced Mean absolute value	-
39	'ewl'	Enhanced Wavelength	-
38	'fzc'	New Zero Crossing	-
37	'asm'	Absolute Value of Summation of exp root	-
36	'ass'	Absolute Value of Summation of Square Root	-
35	'msr'	Mean Value of Square Root	-
34	'ltkeo'	Log Teager Kaiser Energy Operator	-
33	'lcov'	Log Coefficient of Variation	-
32	'card'	Cardinality	opts.thres = 0.01
31	'ldasdv'	Log Difference Absolute Standard Deviation	-
30	'ldamv'	Log Difference Absolute Mean Value	-
29	'dvarv'	Difference Variance Value	-
28	'vo'	V-Order	opts.order = 2
27	'tm'	Temporal Moment	opts.order = 3
26	'damv'	Difference Absolute Mean Value	-
25	'ar'	Auto-Regressive Model	opts.order = 4
24	'mad'	Mean Absolute Deviation	-
23	'iqr'	Interquartile Range	-
22	'skew'	Skewness	-
21	'kurt'	Kurtosis	-
20	'cov'	Coefficient of Variation	-
19	'sd'	Standard Deviation	-
18	'var'	Variance	-
17	'ae'	Average Energy	-
16	'iemg'	Integrated EMG	-
15	'mav'	Mean Absolute Value	-
14	'ssc'	Slope Sign Change	opts.thres = 0.01
13	'zc'	Zero Crossing	opts.thres = 0.01
12	'wl'	Waveform Length	-
11	'rms'	Root Mean Square	-
10	'aac'	Average Amplitude Change	-
09	'dasdv'	Difference Absolute Standard Deviation Value	-
08	'ld'	Log Detector	-
07	'mmav'	Modified Mean Absolute Value	-
06	'mmav2'	Modified Mean Absolute Value 2	-
05	'myop'	Myopulse Percentage Rate	opts.thres = 0.016
04	'ssi'	Simple Square Integral	-
03	'vare'	Variance of EMG	-
02	'wa'	Willison Amplitude	opts.thres = 0.01
01	'mfl'	Maximum Fractal Length	-

Cite As

@article{too2019classification,
  title={Classification of hand movements based on discrete wavelet transform and enhanced feature extraction},
  author={Too, Jingwei and Abdullah, Abdul Rahim and Saad, Norhashimah Mohd},
  journal={International Journal of Advanced Computer Science and Applications},
  volume={10},
  number={6},
  pages={83--89},
  year={2019}
}


@article{too2019emg,
  title={EMG feature selection and classification using a Pbest-guide binary particle swarm optimization},
  author={Too, Jingwei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Tee, Weihown},
  journal={Computation},
  volume={7},
  number={1},
  pages={12},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}