title: Bayesian EEG dipole source localization using SA-RJMCMC on realistic head model.
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.
authors.affiliation: Computer Engineering Department, Galatasaray University, 34357 Ortakoy-Istanbul, Turkey. gyildiz@gsu.edu.tr
authors.forename: Gokcen
authors.initials: G
authors.lastname: Yildiz
authors.forename: A
authors.initials: A
authors.lastname: Duru
authors.forename: Ahmet
authors.initials: A
authors.lastname: Ademoglu
authors.forename: Tamer
authors.initials: T
authors.lastname: Demiralp
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004569:Electroencephalography / Q000379:methods*; D006257:Head; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014054:Tomography