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The Journal of Neuroscience, November 1, 2002, 22(21):9465-9474

Restoration of Movement Using Functional Electrical Stimulation and Bayes' Theorem

Heather M. Seifert¹ and Andrew J. Fuglevand^{1, 2, 3}

Department of Physiology and Programs in ¹ Biomedical Engineering, ² Physiological Sciences, and ³ Neuroscience, University of Arizona, Tucson, Arizona 85721-0093

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.

Key words: bayesian statistics; electromyography; kinematics; neuroprosthetics; functional electrical stimulation; motor control

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Copyright © 2002 Society for Neuroscience  0270-6474/02/22219465-10$05.00/0

This article has been cited by other articles:

				C. V. Anderson and A. J. Fuglevand Probability-Based Prediction of Activity in Multiple Arm Muscles: Implications for Functional Electrical Stimulation J Neurophysiol, July 1, 2008; 100(1): 482 - 494. [Abstract] [Full Text] [PDF]

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