Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • Author(s): Zheng B; Li Y; Xu G; Wang G; Zheng Y
  • Source:
    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Neural Syst Rehabil Eng] 2024; Vol. 32, pp. 1994-2004. Date of Electronic Publication: 2024 May 27.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: IEEE Country of Publication: United States NLM ID: 101097023 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-0210 (Electronic) Linking ISSN: 15344320 NLM ISO Abbreviation: IEEE Trans Neural Syst Rehabil Eng Subsets: MEDLINE
    • Publication Information:
      Original Publication: Piscataway, NJ : IEEE, c2001-
    • Subject Terms:
      Forearm*/physiology ; Electromyography*/methods ; Fingers*/physiology ; Motor Neurons*/physiology ; Algorithms*; Humans ; Male ; Rotation ; Young Adult ; Adult ; Female ; Muscle, Skeletal/physiology ; Action Potentials/physiology ; Brain-Computer Interfaces ; Reproducibility of Results ; Muscle Contraction/physiology ; Movement/physiology
    • Abstract:
      Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( [Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.
    • Publication Date:
      Date Created: 20240517 Date Completed: 20240527 Latest Revision: 20240527
    • Publication Date:
      20240527
    • Accession Number:
      10.1109/TNSRE.2024.3402545
    • Accession Number:
      38758613