A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time
The proposed ML-DRSNet and the improved ML-DCNN algorithms substantially enhance both the accuracy and real-time performance in decomposing MUSTs, establishing a technical foundation for neuro-information-driven motor intention recognition and disease assessment.
Authors
- Qiuyuan Li (ORCID: https://orcid.org/0009-0003-6592-1994)
- Dan Guo (ORCID: https://orcid.org/0000-0002-0171-5599)
- Naifu Jiang (ORCID: https://orcid.org/0000-0001-7936-8446)
- Jinting Ma (ORCID: https://orcid.org/0009-0000-0133-7658)
- Naiwen Zhang (ORCID: https://orcid.org/0000-0002-5035-9180)
- Renxiang Wu
- Guanglin Li (ORCID: https://orcid.org/0000-0001-9016-2617)
- Jianjun Li (ORCID: https://orcid.org/0000-0003-3003-8344)
- Lifen Wang (ORCID: https://orcid.org/0000-0002-1628-0284)
- Jing Wei (ORCID: https://orcid.org/0000-0001-8710-7369)
- Li‐Hai Tan
Institutions
- Jinan University (CN)
- Shenzhen University (CN)
- Shenzhen Institute of Neuroscience (CN)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1186/s12984-025-01639-3
- Citations
- 2
- Primary Topic
- Muscle activation and electromyography studies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Natural Science Foundation of China
- Shenzhen University
- Basic and Applied Basic Research Foundation of Guangdong Province