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A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data

conference contribution
posted on 2012-01-01, 00:00 authored by Imali HettiarachchiImali Hettiarachchi, Shady MohamedShady Mohamed, Saeid Nahavandi
The work presented in this paper focuses on fitting of a neural mass model to EEG data. Neurophysiology inspired mathematical models were developed for simulating brain's electrical activity imaged through Electroencephalography (EEG) more than three decades ago. At the present well informative models which even describe the functional integration of cortical regions also exists. However, a very limited amount of work is reported in literature on the subject of model fitting to actual EEG data. Here, we present a Bayesian approach for parameter estimation of the EEG model via a marginalized Markov Chain Monte Carlo (MCMC) approach.

History

Event

IEEE International Symposium on Biomedical Imaging (9th : 2012 : Barcelona, Spain)

Pagination

1539 - 1542

Publisher

IEEE

Location

Barcelona, Spain

Place of publication

Los Alamitos, Calif.

Start date

2012-05-02

End date

2012-05-05

ISSN

1945-7928

ISBN-13

9781457718571

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2012, IEEE

Title of proceedings

ISBI 2012 : From Nano to Macro : Proceedings of the 9th IEEE International Symposium on Biomedical Imaging