CaltechAUTHORS
  A Caltech Library Service

A hybrid systems model for supervisory cognitive state identification and estimation in neural prosthetics

Hudson, N. and Burdick, J. W. (2008) A hybrid systems model for supervisory cognitive state identification and estimation in neural prosthetics. In: 47th IEEE Conference on Decision and Control. IEEE , Piscataway, NJ, pp. 2025-2031. ISBN 978-1-4244-3123-6. https://resolver.caltech.edu/CaltechAUTHORS:20190612-160343236

[img] PDF - Published Version
See Usage Policy.

371Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20190612-160343236

Abstract

This paper presents a method to identify a class of hybrid system models that arise in cognitive neural prosthetic medical devices that aim to help the severely handicapped. In such systems a “supervisory decoder” is required to classify the activity of multi-unit extracellular neural recordings into a discrete set of modes that model the evolution of the brain’s planning process. We introduce a Gibbs sampling method to identify the key parameters of a GLHMM, a hybrid dynamical system that combines a set of generalized linear models (GLM) for dynamics of neuronal signals with a hidden Markov model (HMM) that describes the discrete transitions between the brain’s cognitive or planning states. Multiple neural signals of mixed type, including local field potentials and spike arrival times, are integrated into the model using the GLM framework. The identified model can then be used as the basis for the supervisory decoding (or estimation) of the current cognitive or planning state. The identification algorithm is applied to extracellular neural recordings obtained from set of electrodes acutely implanted in the posterior parietal cortex of a rhesus monkey. The results demonstrate the ability to accurately decode changes in behavioral or cognitive state during reaching tasks, even when the model parameters are identified from small data sets. The GLHMM models and the associated identification methods are generally applicable beyond the neural application domain.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/CDC.2008.4739381DOIArticle
Additional Information:© 2008 IEEE. The authors would like to thank Hans Scherberger and Richard Andersen for providing the neural data used in the demonstration of Section V. This work has been supported by the National Institutes of Health and the Defense Advanced Research Projects Agency.
Funders:
Funding AgencyGrant Number
NIHUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Record Number:CaltechAUTHORS:20190612-160343236
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190612-160343236
Official Citation:N. Hudson and J. W. Burdick, "A hybrid systems model for supervisory cognitive state identification and estimation in neural prosthetics," 2008 47th IEEE Conference on Decision and Control, Cancun, 2008, pp. 2025-2031. doi: 10.1109/CDC.2008.4739381
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96354
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:13 Jun 2019 14:59
Last Modified:03 Oct 2019 21:21

Repository Staff Only: item control page