Decoding dexterous finger movements in a neural prosthesis model approaching real-world conditions

Joshua Egan, Justin Baker, Paul A. House, Bradley Greger

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Dexterous finger movements can be decoded from neuronal action potentials acquired from a nonhuman primate using a chronically implanted Utah Electrode Array. We have developed an algorithm that can, after training, detect and classify individual and combined finger movements without any a priori knowledge of the data, task, or behavior. The algorithm is based on changes in the firing rates of individual neurons that are tuned for one or more finger movement types. Nine different movement types, which consisted of individual flexions, individual extensions, and combined flexions of the thumb, index finger, and middle finger, were decoded. The algorithm performed reliably on data recorded continuously during movement tasks, including a no-movement state, with an overall average sensitivity and specificity that were both >92%. These results demonstrate a viable algorithm for decoding dexterous finger movements under conditions similar to those required for a real-world neural prosthetic application.

Original languageEnglish (US)
Article number6259887
Pages (from-to)836-844
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume20
Issue number6
DOIs
StatePublished - Nov 26 2012
Externally publishedYes

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Keywords

  • Action potential decode
  • brain-computer interface (BCI)
  • microelectrode array
  • nonhuman primate
  • primary motor cortex

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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