A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data
Hettiarachchi, Imali, Mohamed, Shady and Nahavandi, Saeid 2012, A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data, in ISBI 2012 : From Nano to Macro : Proceedings of the 9th IEEE International Symposium on Biomedical Imaging, IEEE, Los Alamitos, Calif., pp. 1539-1542.
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.
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences