A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time

Background The swift and accurate identification of motor unit spike trains (MUSTs) from surface electromyography (sEMG) is essential for enabling real-time control in neural interfaces. However, the existing sEMG decomposition methods, including blind source separation (BSS) and deep learning, have...

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Veröffentlicht in:	Journal of neuroengineering and rehabilitation Jg. 22; H. 1; S. 106 - 19
Hauptverfasser:	Ma, Jinting, Wang, Lifen, Wu, Renxiang, Zhang, Naiwen, Wei, Jing, Li, Jianjun, Li, Qiuyuan, Tan, Lihai, Li, Guanglin, Jiang, Naifu, Dan, Guo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 08.05.2025 BioMed Central Ltd BMC
Schlagworte:	Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Deep Learning Deep residual shrinkage network Electromyography Electromyography - methods High-definition television High-density surface electromyography Humans Machine learning Methods Motor unit spike trains Multi-label learning Muscle, Skeletal - physiology Neural Networks, Computer Neurology Neurophysiology Neurosciences Real-time decomposition Rehabilitation Medicine Signal processing Signal Processing, Computer-Assisted Specific gravity Massachusetts China Motor unit spike trains Multi-label learning Real-time decomposition High-density surface electromyography Deep residual shrinkage network
ISSN:	1743-0003, 1743-0003
Online-Zugang:	Volltext
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