Using Sparse Autoencoders to Perform Blind Source Separation of High-Density Myoelectric Signal
Author:
Affiliation:
1. University of Campinas,Neural Engineering Research Laboratory, Center for Biomedical Engineering,Campinas,SP,Brazil
2. University of Campinas,School of Applied Sciences,Limeira,SP,Brazil
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10285838/10285858/10285987.pdf?arnumber=10285987
Reference17 articles.
1. A Surface EMG Generation Model With Multilayer Cylindrical Description of the Volume Conductor
2. Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation
3. Simulation system of spinal cord motor nuclei and associated nerves and muscles, in a Web-based architecture
4. Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging
5. Multichannel Blind Source Separation Using Convolution Kernel Compensation
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