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

Yildiz G., Duru A. D., Ademoglu A., Demiralp T.

29th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society, Lyon, Fransa, 22 - 26 Ağustos 2007, ss.4268-4269 identifier identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/iembs.2007.4353279
  • Basıldığı Şehir: Lyon
  • Basıldığı Ülke: Fransa
  • Sayfa Sayıları: ss.4268-4269
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.