Annals of Biomedical Engineering

, Volume 37, Issue 9, pp 1849–1857

Spatial Filtering Improves EMG Classification Accuracy Following Targeted Muscle Reinnervation

Article

DOI: 10.1007/s10439-009-9737-7

Cite this article as:
Huang, H., Zhou, P., Li, G. et al. Ann Biomed Eng (2009) 37: 1849. doi:10.1007/s10439-009-9737-7

Abstract

The combination of targeted muscle reinnervation (TMR) and pattern classification of electromyography (EMG) has shown great promise for multifunctional myoelectric prosthesis control. In this study, we hypothesized that surface EMG recordings with high spatial resolution over reinnervated muscles could capture focal muscle activity and improve the classification accuracy of identifying intended movements. To test this hypothesis, TMR subjects with transhumeral or shoulder disarticulation amputations were recruited. Spatial filters such as single differential filters, double differential filters, and various two-dimensional, high-order spatial filters were used, and the classification accuracies for fifteen different movements were calculated. Compared with monopolar recordings, spatially localized EMG signals produced increased accuracy in identifying the TMR patients’ movement intents, especially for hand movements. When the number of EMG signals was constrained to 12, the double differential filters gave 5–15% higher classification accuracies than the filters with lower spatial resolution, but resulted in comparable accuracies to the filters with higher spatial resolution. These results suggest that double differential EMG recordings may further improve the TMR-based neural interface for robust, multifunctional control of artificial arms.

Keywords

Electromyography Targeted muscle reinnervation Spatial filter Neural–machine interface 

Copyright information

© Biomedical Engineering Society 2009

Authors and Affiliations

  • He Huang
    • 1
    • 2
  • Ping Zhou
    • 1
    • 3
  • Guanglin Li
    • 1
    • 3
    • 5
  • Todd Kuiken
    • 1
    • 3
    • 4
  1. 1.Neural Engineering Center for Artificial LimbsRehabilitation Institute of ChicagoChicagoUSA
  2. 2.Department of Electrical, Computer, and Biomedical EngineeringUniversity of Rhode IslandKingstonUSA
  3. 3.Department of Physical Medicine and RehabilitationNorthwestern UniversityChicagoUSA
  4. 4.Institute of Biomedical EngineeringNorthwestern UniversityChicagoUSA
  5. 5.Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenP.R. China

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