Parameter and state estimation for a class of neural mass models

Romain Postoyan; Michelle Chong; Dragan Nesic; Levin Kuhlmann

doi:10.1109/CDC.2012.6427031

Parameter and state estimation for a class of neural mass models

Romain Postoyan, Michelle Chong, Dragan Nesic, Levin Kuhlmann

Department of Data Science & AI

Research output: Contribution to journal › Conference article › Research › peer-review

5 Citations (Scopus)

Abstract

We present an adaptive observer which asymptotically reconstructs the parameters and states of a model of interconnected cortical columns. Our study is motivated by the fact that the considered model is able to realistically reproduce patterns seen on (intracranial) electroencephalograms (EEG) by varying its parameters. Therefore, by estimating its parameters and states, we could gain a better understanding of the mechanisms underlying neurological phenomena such as seizures, which might lead to the prediction of the onsets of epileptic seizures. Simulations are performed to illustrate our results.

Original language	English
Article number	6427031
Pages (from-to)	2322-2327
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/CDC.2012.6427031
Publication status	Published - 1 Dec 2012
Event	IEEE Conference on Decision and Control 2012 - Grand Wailea, Maui, United States of America Duration: 10 Dec 2012 → 13 Dec 2012 Conference number: 51st http://www.ieeecss.org/CAB/conferences/cdc2012/

Cite this

Postoyan, R., Chong, M., Nesic, D., & Kuhlmann, L. (2012). Parameter and state estimation for a class of neural mass models. Proceedings of the IEEE Conference on Decision and Control, 2322-2327. [6427031]. https://doi.org/10.1109/CDC.2012.6427031

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Access to Document

10.1109/CDC.2012.6427031

Link to publication in Scopus