• DocumentCode
    81577
  • Title

    Improving Myoelectric Control for Amputees through Transcranial Direct Current Stimulation

  • Author

    Lizhi Pan ; Dingguo Zhang ; Xinjun Sheng ; Xiangyang Zhu

  • Author_Institution
    State Key Lab. of Mech. Syst. & Vibration, Shanghai Jiao Tong Univ., Shanghai, China
  • Volume
    62
  • Issue
    8
  • fYear
    2015
  • fDate
    Aug. 2015
  • Firstpage
    1927
  • Lastpage
    1936
  • Abstract
    Most prosthetic myoelectric control studies have shown good performance for unimpaired subjects. However, performance is generally unacceptable for amputees. The primary problem is the poor quality of electromyography (EMG) signals of amputees compared with healthy individuals. To improve clinical performance of myoelectric control, this study explored transcranial direct current stimulation (tDCS) to modulate brain activity and enhance EMG quality. We tested six unilateral transradial amputees by applying active and sham anodal tDCS separately on two different days. Surface EMG signals were acquired from the affected and intact sides for 11 hand and wrist motions in the pre-tDCS and post-tDCS sessions. Autoregression coefficients and linear discriminant analysis classifiers were used to process the EMG data for pattern recognition of the 11 motions. For the affected side, active anodal tDCS significantly reduced the average classification error rate (CER) by 10.1%, while sham tDCS had no such effect. For the intact side, the average CER did not change on the day of sham tDCS but increased on the day of active tDCS. These results demonstrated that tDCS could modulate brain function and improve EMG-based classification performance for amputees. It has great potential in dramatically reducing the length of learning process of amputees for effectively using myoelectrically controlled multifunctional prostheses.
  • Keywords
    biomechanics; electromyography; medical control systems; neurophysiology; pattern recognition; prosthetics; regression analysis; EMG quality enhancement; EMG-based classification performance; active anodal tDCS; autoregression coefficients; brain activity; classification error rate; electromyography; hand motion; linear discriminant analysis classifier; multifunctional prosthesis; pattern recognition; post-tDCS session; pre-tDCS session; prosthetic myoelectric control study; sham anodal tDCS; surface EMG signal; transcranial direct current stimulation; unilateral transradial amputee; wrist motion; Analysis of variance; DC motors; Electrodes; Electromyography; Muscles; Silicon; Training; Electromyography (EMG); Myoelectric control; Pattern recognition; Transcranial direct current stimulation (tDCS); Transradial amputee; myoelectric control; pattern recognition; transcranial direct current stimulation (tDCS); transradial amputee;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2015.2407491
  • Filename
    7050334