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

Journal: Journal of neuroengineering and rehabilitation
PMID: 40340912

Abstract

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 not yet achieved satisfactory performance, due to high latency or low accuracy.

Authors

  • Jinting Ma
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
  • Lifen Wang
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
  • Renxiang Wu
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
  • Naiwen Zhang
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
  • Jing Wei
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
  • Jianjun Li
    Rehabilitation Clinic, Shenzhen University General Hospital, Shenzhen, Guangdong, China.
  • Qiuyuan Li
    Rehabilitation Clinic, Shenzhen University General Hospital, Shenzhen, Guangdong, China.
  • Lihai Tan
    Guangdong-Hongkong-Macau Institute of CNS Regeneration and Jinan University (Shenzhen), Shenzhen, Guangdong, China.
  • Guanglin Li
    Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
  • Naifu Jiang
    Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China. nf.jiang@siat.ac.cn.
  • Guo Dan
    School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China. danguo@szu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals