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 | |
| 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
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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 journal › Conference article › Research › peer-review
TY - JOUR
T1 - Parameter and state estimation for a class of neural mass models
AU - Postoyan, Romain
AU - Chong, Michelle
AU - Nesic, Dragan
AU - Kuhlmann, Levin
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874263069&partnerID=8YFLogxK
U2 - 10.1109/CDC.2012.6427031
DO - 10.1109/CDC.2012.6427031
M3 - Conference article
SP - 2322
EP - 2327
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
SN - 0743-1546
M1 - 6427031
ER -