Abstract
Kalman filters have been used to decode neural signals and estimate hand kinematics in many studies. However, most prior work assumes a linear system model, an assumption that is almost certainly violated by neural systems. In this paper, we show that adding nonlinearities to the decoding algorithm improves the accuracy of tracking hand movements using neural signal acquired via a 32-channel micro-electrocorticographic (μECoG) grid placed over the arm and hand representations in the motor cortex. Experimental comparisons indicate that a Kalman filter with a fifth order polynomial generative model relating the hand kinematics signals to the neural signals improved the mean-square tracking performance in the hand movements over a conventional Kalman filter employing a linear system model. This finding is in accord with the current neurophysiological understanding of the decoded signals.
| Original language | English (US) |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4683-4687 |
| Number of pages | 5 |
| ISBN (Print) | 9781479928927 |
| DOIs | |
| State | Published - 2014 |
| Event | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy Duration: May 4 2014 → May 9 2014 |
Other
| Other | 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 |
|---|---|
| Country | Italy |
| City | Florence |
| Period | 5/4/14 → 5/9/14 |
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Keywords
- Brain-Computer Interface
- Neural decoding
- Nonlinear Kalman Filter
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering
Cite this
Neural decoding using a nonlinear generative model for brain-computer interface. / Dantas, Henrique; Kellis, Spencer; Mathews, V. John; Greger, Bradley.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4683-4687 6854490.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neural decoding using a nonlinear generative model for brain-computer interface
AU - Dantas, Henrique
AU - Kellis, Spencer
AU - Mathews, V. John
AU - Greger, Bradley
PY - 2014
Y1 - 2014
N2 - Kalman filters have been used to decode neural signals and estimate hand kinematics in many studies. However, most prior work assumes a linear system model, an assumption that is almost certainly violated by neural systems. In this paper, we show that adding nonlinearities to the decoding algorithm improves the accuracy of tracking hand movements using neural signal acquired via a 32-channel micro-electrocorticographic (μECoG) grid placed over the arm and hand representations in the motor cortex. Experimental comparisons indicate that a Kalman filter with a fifth order polynomial generative model relating the hand kinematics signals to the neural signals improved the mean-square tracking performance in the hand movements over a conventional Kalman filter employing a linear system model. This finding is in accord with the current neurophysiological understanding of the decoded signals.
AB - Kalman filters have been used to decode neural signals and estimate hand kinematics in many studies. However, most prior work assumes a linear system model, an assumption that is almost certainly violated by neural systems. In this paper, we show that adding nonlinearities to the decoding algorithm improves the accuracy of tracking hand movements using neural signal acquired via a 32-channel micro-electrocorticographic (μECoG) grid placed over the arm and hand representations in the motor cortex. Experimental comparisons indicate that a Kalman filter with a fifth order polynomial generative model relating the hand kinematics signals to the neural signals improved the mean-square tracking performance in the hand movements over a conventional Kalman filter employing a linear system model. This finding is in accord with the current neurophysiological understanding of the decoded signals.
KW - Brain-Computer Interface
KW - Neural decoding
KW - Nonlinear Kalman Filter
UR - http://www.scopus.com/inward/record.url?scp=84905270901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905270901&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854490
DO - 10.1109/ICASSP.2014.6854490
M3 - Conference contribution
AN - SCOPUS:84905270901
SN - 9781479928927
SP - 4683
EP - 4687
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -