Spatial filtering improves EMG classification accuracy following targeted muscle reinnervation

He Huang, Ping Zhou*, Guanglin Li, Todd Kuiken

*Corresponding author for this work

Research output: Contribution to journalArticle

34 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1849-1857
Number of pages9
JournalAnnals of Biomedical Engineering
Volume37
Issue number9
DOIs
StatePublished - Sep 2009

Keywords

  • Electromyography
  • Neural-machine interface
  • Spatial filter
  • Targeted muscle reinnervation

ASJC Scopus subject areas

  • Biomedical Engineering

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