Parameter and state estimation for a class of neural mass models

Romain Postoyan, Michelle Chong, Dragan Nesic, Levin Kuhlmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)


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
Title of host publication51st IEEE Conference on Decision and Control, CDC 2012
Number of pages6
Publication statusPublished - 1 Dec 2012
Externally publishedYes
EventIEEE Conference on Decision and Control 2012 - Grand Wailea, Maui, United States of America
Duration: 10 Dec 201213 Dec 2012
Conference number: 51st (Proceedings)


ConferenceIEEE Conference on Decision and Control 2012
Abbreviated titleCDC 2012
Country/TerritoryUnited States of America
Internet address

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