Neural Decoding of Single and Multi-finger Movements Based on ML

Hyun Chul Shin, M. Schieber, Nitish Thakor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a non-human primate during finger movements. The method is based on the maximum likelihood (ML) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement. Based on the ML criterion, we choose finger movements to maximize the likelihood. With as few as 20-25 randomly selected neurons, the proposed method decoded single finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand.

Original languageEnglish (US)
Title of host publication13th International Conference on Biomedical Engineering - ICBME 2008
Pages448-451
Number of pages4
DOIs
StatePublished - Dec 1 2009
Event13th International Conference on Biomedical Engineering, ICBME 2008 - , Singapore
Duration: Dec 3 2008Dec 6 2008

Publication series

NameIFMBE Proceedings
Volume23
ISSN (Print)1680-0737

Other

Other13th International Conference on Biomedical Engineering, ICBME 2008
CountrySingapore
Period12/3/0812/6/08

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

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  • Cite this

    Shin, H. C., Schieber, M., & Thakor, N. (2009). Neural Decoding of Single and Multi-finger Movements Based on ML. In 13th International Conference on Biomedical Engineering - ICBME 2008 (pp. 448-451). (IFMBE Proceedings; Vol. 23). https://doi.org/10.1007/978-3-540-92841-6_110