This toolbox offers 17 types of EMG features
(1) Enhanced Mean absolute value (EMAV)
(2) Enhanced Wavelength (EWL)
(3) Mean Absolute Value (MAV)
(4) Slope Sign Change (SSC)
(5) Zero Crossing (ZC)
(6) Waveform Length (WL)
(7) Root Mean Square (RMS)
(8) Average Amplitude Change (AAC)
(9) Difference Absolute Standard Deviation Value (DASDV)
(10) Log Detector (LD)
(11) Modified Mean Absolute Value (MMAV)
(12) Modified Mean Absolute Value 2 (MMAV2)
(13) Myopulse Percentage Rate (MYOP)
(14) Simple Square Integral (SSI)
(15) Variance of EMG (VAR)
(16) Willison Amplitude (WA)
(17) Maximum Fractal Length (MFL)
The "Main" demos how the feature extraction methods can be applied by using the generated sample signal.
 J. Too, A. R. Abdullah, N. Mohd Saad, and W. Tee, “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization,” Computation, vol. 7, no. 1, 2019.
 J. Too, A. R. Abdullah, and N. Mohd Saad, “Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 6, 2019.
Jingwei Too (2019). EMG Feature Extraction Toolbox (https://www.mathworks.com/matlabcentral/fileexchange/71514-emg-feature-extraction-toolbox), MATLAB Central File Exchange. Retrieved .
Dear Polo Joachín,
You need to read some related paper before you started. To use it, you need an EMG signal.
In the "main" file, you just replace the "X" with your EMG signal.
How do I use it. I am new.
Dear Marcus Schneider, Thank you for the information. I have updated the program.
Unfortunately, the calculation of zero crossings and sign slope changes are wrong.
Add MFL feature
Add another 10 feature extraction methods
(1) The SSC and ZC have been corrected.