2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA
Author:
Affiliation:
1. School of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, Shandong, China
Funder
Shandong Provincial Natural Science Foundation
Taishan Scholar Project of Shandong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7333/10398877/10526297.pdf?arnumber=10526297
Reference29 articles.
1. Multichannel Blind Source Separation Using Convolution Kernel Compensation
2. Gradient Convolution Kernel Compensation Applied to Surface Electromyograms
3. A Novel Framework Based on FastICA for High Density Surface EMG Decomposition
4. Automatic Implementation of Progressive FastICA Peel-Off for High Density Surface EMG Decomposition
5. Fast and robust fixed-point algorithms for independent component analysis
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