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

doi:10.1109/NNSP.2002.1030025

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 proceeding › Conference contribution

54 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 language	English (US)
Title of host publication	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	139-148
Number of pages	10
Volume	2002-January
ISBN (Print)	0780376161
DOIs	https://doi.org/10.1109/NNSP.2002.1030025
State	Published - 2002
Externally published	Yes
Event	12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002 - Martigny, Switzerland Duration: Sep 6 2002 → …

Other

Other	12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002
Country/Territory	Switzerland
City	Martigny
Period	9/6/02 → …

ASJC Scopus subject areas

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

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10.1109/NNSP.2002.1030025

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

Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns. / Sanchez, Justin C.; Kim, Sung Phil; Erdogmus, D.; Rao, Y. N.; Principe, J. C.; Wessberg, J.; Nicolelis, M.

Neural Networks for Signal Processing - Proceedings of the IEEE Workshop. Vol. 2002-January Institute of Electrical and Electronics Engineers Inc., 2002. p. 139-148 1030025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sanchez, JC, Kim, SP, Erdogmus, D, Rao, YN, Principe, JC, 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, 1030025, Institute of Electrical and Electronics Engineers Inc., pp. 139-148, 12th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2002, Martigny, Switzerland, 9/6/02. https://doi.org/10.1109/NNSP.2002.1030025

Sanchez JC, Kim SP, Erdogmus D, Rao YN, Principe JC, Wessberg J et al. 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. Institute of Electrical and Electronics Engineers Inc. 2002. p. 139-148. 1030025 https://doi.org/10.1109/NNSP.2002.1030025

Sanchez, Justin C. ; Kim, Sung Phil ; Erdogmus, D. ; Rao, Y. N. ; Principe, J. C. ; Wessberg, J. ; Nicolelis, M. / Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns. Neural Networks for Signal Processing - Proceedings of the IEEE Workshop. Vol. 2002-January Institute of Electrical and Electronics Engineers Inc., 2002. pp. 139-148

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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.",

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

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