Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns

Justin C. Sanchez, Sung Phil Kim, D. Erdogmus, Y. N. Rao, J. C. Principe, J. Wessberg, M. Nicolelis

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

52 Scopus citations

Abstract

Linear and nonlinear (TDNN) models have been shown to estimate hand position 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 discovery is to restore movement in patients suffering from paralysis. For real-time implementation of this technology, reliable and accurate signal processing models that produce small error variance in the estimated positions are required. In this paper, we compare the mapping performance of the FIR filter, gamma filter and recurrent neural network (RNN) in the peaks of reaching movements. Each approach has strengths and weaknesses that are compared experimentally. The RNN approach shows very accurate peak position estimations with small error variance.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages139-148
Number of pages10
Volume2002-January
ISBN (Print)0780376161
DOIs
StatePublished - 2002
Externally publishedYes
Event12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002 - Martigny, Switzerland
Duration: Sep 6 2002 → …

Other

Other12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002
CountrySwitzerland
CityMartigny
Period9/6/02 → …

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Software
  • Computer Networks and Communications
  • Signal Processing

Cite this

Sanchez, J. C., Kim, S. P., Erdogmus, D., Rao, Y. N., Principe, J. C., Wessberg, J., & Nicolelis, M. (2002). Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns. In Neural Networks for Signal Processing - Proceedings of the IEEE Workshop (Vol. 2002-January, pp. 139-148). [1030025] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NNSP.2002.1030025