Restoration of movement using functional electrical stimulation and Bayes' theorem.
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 approximately 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.
Seifert
Heather M
HM
Department of Physiology, Physiological Sciences, and Neuroscience, University of Arizona, Tucson, Arizona 85721-0093, USA.
Fuglevand
Andrew J
AJ
D000465:Algorithms; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D004558:Electric Stimulation / Q000295:instrumentation / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D005384:Finger Joint / Q000502:physiology; D005385:Fingers / Q000502:physiology; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D008839:Microelectrodes; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D011237:Predictive Value of Tests; D016059:Range of Motion, Articular / Q000502:physiology; D012016:Reference Values; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014953:Wrist / Q000502:physiology