Modeling the Relation from Motor Cortical Neuronal Firing to Hand Movements Using Competitive Linear Filters and a MLP

Sung Phil Kim, Justin C. Sanchez, Deniz Erdogmus, Yadunandana N. Rao, Jose C. Principe, Miguel Nicolelis

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

3 Scopus citations

Abstract

Recent research has demonstrated that linear models are able to estimate hand positions using populations of action potentials collected in the pre-motor and motor cortical areas of a primate's brain. One of the applications of this result is to restore movement in patients suffering from paralysis. To implement this technology in real-time, reliable and accurate signal processing models that produce sufficiently small error in the estimated hand positions are required. In this paper, we propose the hybrid model approach that combines competitive linear filters with a neural network. The mapping performance of our approach is compared with a single Wiener filter during reaching movements. Our approach demonstrates more accurate estimations.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages66-70
Number of pages5
Volume1
StatePublished - Sep 24 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period7/20/037/24/03

ASJC Scopus subject areas

  • Software

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    Kim, S. P., Sanchez, J. C., Erdogmus, D., Rao, Y. N., Principe, J. C., & Nicolelis, M. (2003). Modeling the Relation from Motor Cortical Neuronal Firing to Hand Movements Using Competitive Linear Filters and a MLP. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 66-70)