Bayesian EEG dipole source localization using SA-RJMCMC on realistic head model

Gokcen Yildiz; A Duru; Ahmet Ademoglu; Tamer Demiralp

doi:10.1109/IEMBS.2007.4353279

Bayesian EEG dipole source localization using SA-RJMCMC on realistic head model

Annu Int Conf IEEE Eng Med Biol Soc. 2007:2007:4268-72. doi: 10.1109/IEMBS.2007.4353279.

Authors

Gokcen Yildiz¹, A Duru, Ahmet Ademoglu, Tamer Demiralp

Affiliation

¹ Computer Engineering Department, Galatasaray University, 34357 Ortakoy-Istanbul, Turkey. gyildiz@gsu.edu.tr

PMID: 18002945
DOI: 10.1109/IEMBS.2007.4353279

Abstract

In this study, electroencephalography (EEG) inverse problem is formulated using Bayesian inference. The posterior probability distribution of current sources is sampled by Markov Chain Monte Carlo (MCMC) methods. Sampling algorithm is designed by combining Reversible Jump (RJ) which permits trans-dimensional iterations and Simulated Annealing (SA), a heuristic to escape from local optima. Two different approaches to EEG inverse problem, Equivalent Current Dipole (ECD) and Distributed Linear Imaging (DLI) are combined in terms of probability. EEG inverse problem is solved with this probabilistic approach using simulated data on a realistic head model. Localization errors are computed. Comparing to Multiple Signal Classification algorithm (MUSIC) and Low-Resolution Electromagnetic Tomography (LORETA), using MCMC methods with a Bayesian approach is useful for solving the EEG inverse problem.

MeSH terms

Algorithms*
Bayes Theorem
Electroencephalography / methods*
Head
Humans
Models, Biological*
Monte Carlo Method
Signal Processing, Computer-Assisted*
Software*
Tomography