EMG Feature Extraction Toolbox

Versión 1.4 (17,4 KB) por Jingwei Too

This toolbox offers 40 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC, and etc.) for Electromyography (EMG) signals applications.

https://jingweitoo.wordpress.com/

Seguir

4.4

(8)

4,2K Descargas

Actualizado 11 dic 2020

Ver licencia en GitHub

Jx-EMGT : Electromyography (EMG) Feature Extraction Toolbox

-------------------------------------------------------------------------------------------------------------------------------------------------------------------

* This toolbox offers 40 types of EMG features

* The < A_Main.m file > demos how the feature extraction methods can be applied using generated sample signal.

* The detailed of this Jx-EMGT toolbox can be found at https://github.com/JingweiToo/EMG-Feature-Extraction-Toolbox

Citar como

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.

Ver más estilos

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.

Ver más estilos

Compatibilidad con la versión de MATLAB

Se creó con R2018a

Compatible con cualquier versión

Compatibilidad con las plataformas

Windows macOS Linux

Etiquetas Añadir etiquetas

Agradecimientos

Inspiración para: Identify Arm Motions Using EMG Signals and Deep Learning.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Versión	Publicado	Notas de la versión
1.4	11 dic 2020	See release notes for this release on GitHub: https://github.com/JingweiToo/EMG-Feature-Extraction-Toolbox/releases/tag/1.4	Descargar

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.

MLA	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.
APA	Too, J., Rahim, A., & Mohd, N. (2019). Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction. International Journal of Advanced Computer Science and Applications, 10(6). The Science and Information Organization. Retrieved from https://doi.org/10.14569%2Fijacsa.2019.0100612
BibTeX	@article{Too_2019, doi = {10.14569/ijacsa.2019.0100612}, url = {https://doi.org/10.14569%2Fijacsa.2019.0100612}, year = 2019, publisher = {The Science and Information Organization}, volume = {10}, number = {6}, author = {Jingwei Too and Abdul Rahim and Norhashimah Mohd}, title = {Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction}, journal = {International Journal of Advanced Computer Science and Applications} }

MLA	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.
APA	Too, J., Abdullah, A., Saad, N. M., & Tee, W. (2019). EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization. Computation, 7(1), 12. MDPI AG. Retrieved from https://doi.org/10.3390%2Fcomputation7010012
BibTeX	@article{Too_2019, doi = {10.3390/computation7010012}, url = {https://doi.org/10.3390%2Fcomputation7010012}, year = 2019, month = {feb}, publisher = {{MDPI} {AG}}, volume = {7}, number = {1}, pages = {12}, author = {Jingwei Too and Abdul Abdullah and Norhashimah Mohd Saad and Weihown Tee}, title = {{EMG} Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization}, journal = {Computation} }