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

Published

Conference Paper

© 2002 IEEE. 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.

Full Text

Duke Authors

Cited Authors

  • Sanchez, JC; Kim, SP; Erdogmus, D; Rao, YN; Principe, JC; Wessberg, J; Nicolelis, M

Published Date

  • January 1, 2002

Published In

  • Neural Networks for Signal Processing Proceedings of the Ieee Workshop

Volume / Issue

  • 2002-January /

Start / End Page

  • 139 - 148

International Standard Book Number 10 (ISBN-10)

  • 0780376161

Digital Object Identifier (DOI)

  • 10.1109/NNSP.2002.1030025

Citation Source

  • Scopus