EMG Feature Extraction Toolbox
This toolbox offers 40 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC, and etc.) for Electromyography (EMG) signals applications.
You are now following this Submission
- You will see updates in your followed content feed
- You may receive emails, depending on your communication preferences
Cite As
Too, Jingwei, et al. “Classification of Hand Movements Based on Discrete Wavelet Transform and Enhanced Feature Extraction.” International Journal of Advanced Computer Science and Applications, vol. 10, no. 6, The Science and Information Organization, 2019, doi:10.14569/ijacsa.2019.0100612.
Too, Jingwei, et al. “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.” Computation, vol. 7, no. 1, MDPI AG, Feb. 2019, p. 12, doi:10.3390/computation7010012.
Acknowledgements
Inspired: Identify Arm Motions Using EMG Signals and Deep Learning.
General Information
- Version 1.4.0 (17.4 KB)
-
View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.4.0 | See release notes for this release on GitHub: https://github.com/JingweiToo/EMG-Feature-Extraction-Toolbox/releases/tag/1.4 |
