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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 NahavandiThe 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.
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Event
IEEE International Symposium on Biomedical Imaging (9th : 2012 : Barcelona, Spain)Pagination
1539 - 1542Publisher
IEEELocation
Barcelona, SpainPlace of publication
Los Alamitos, Calif.Publisher DOI
Start date
2012-05-02End date
2012-05-05ISSN
1945-7928ISBN-13
9781457718571Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
ISBI 2012 : From Nano to Macro : Proceedings of the 9th IEEE International Symposium on Biomedical ImagingUsage metrics
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