Parameter and state estimation for a class of neural mass models

Romain Postoyan, Michelle Chong, Dragan Nesic, Levin Kuhlmann

Research output: Contribution to journalConference articleResearchpeer-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 languageEnglish
Article number6427031
Pages (from-to)2322-2327
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
DOIs
Publication statusPublished - 1 Dec 2012
EventIEEE Conference on Decision and Control 2012 - Grand Wailea, Maui, United States of America
Duration: 10 Dec 201213 Dec 2012
Conference number: 51st
http://www.ieeecss.org/CAB/conferences/cdc2012/

Cite this

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Parameter and state estimation for a class of neural mass models. / Postoyan, Romain; Chong, Michelle; Nesic, Dragan; Kuhlmann, Levin.

In: Proceedings of the IEEE Conference on Decision and Control, 01.12.2012, p. 2322-2327.

Research output: Contribution to journalConference articleResearchpeer-review

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AU - Kuhlmann, Levin

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