Restoration of movement using functional electrical stimulation and Bayes' theorem

Heather M. Seifert, Andrew J. Fuglevand

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Various computational approaches have been applied to predict aspects of animal behavior from the recorded activity of populations of neurons. Here we invert this process to predict the requisite neuromuscular activity associated with specified motor behaviors. A probabilistic method based on Bayes' theorem was used to predict the patterns of muscular activity needed to produce various types of desired finger movements. The profiles of predicted activity were then used to drive frequency-modulated muscle stimulators to evoke multijoint finger movements. Comparison of movements generated by electrical stimulation with desired movements yielded root mean squared errors between ∼18 and 26%. This reasonable correspondence between desired and evoked movements suggests that this approach might serve as a useful strategy to control neuroprosthetic systems that aim to restore movement to paralyzed individuals.

Original languageEnglish (US)
Pages (from-to)9465-9474
Number of pages10
JournalJournal of Neuroscience
Volume22
Issue number21
DOIs
StatePublished - Nov 1 2002

Keywords

  • Bayesian statistics
  • Electromyography
  • Functional electrical stimulation
  • Kinematics
  • Motor control
  • Neuroprosthetics

ASJC Scopus subject areas

  • Neuroscience(all)

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