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 | |
| 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 | Switzerland |
| City | Martigny |
| Period | 9/6/02 → … |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Artificial Intelligence
- Software
- Computer Networks and Communications
- Signal Processing
Cite this
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
}
TY - GEN
T1 - Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns
AU - Sanchez, Justin C.
AU - Kim, Sung Phil
AU - Erdogmus, D.
AU - Rao, Y. N.
AU - Principe, J. C.
AU - Wessberg, J.
AU - Nicolelis, M.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84953727634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953727634&partnerID=8YFLogxK
U2 - 10.1109/NNSP.2002.1030025
DO - 10.1109/NNSP.2002.1030025
M3 - Conference contribution
SN - 0780376161
VL - 2002-January
SP - 139
EP - 148
BT - Neural Networks for Signal Processing - Proceedings of the IEEE Workshop
PB - Institute of Electrical and Electronics Engineers Inc.
ER -