Multiple dipolar sources localization for MEG using Bayesian particle filtering

Electromagnetic source localization is a technique that enables the study of neural dynamical activities on a millisecond timescale using Magnetoencephalography (MEG) or Electroencephalography (EEG) data. It aims to reveal neural activities in the brain cortical region which cannot be seen with imaging methods that operate on a slower timescale such as fMRI. In this paper, we model the problem under a Bayesian multi-target tracking framework. A multi-target detection and particle filtering algorithm is developed to estimate the dipolar source dynamics, and a minimum norm (MN) based estimation method is incorporated to construct the birth-death move for the dynamical number of dipolar sources. The algorithm is tested using both simulated and experimental data¹. The results demonstrate that the proposed algorithm performs better than that in previous works in terms of both localization accuracy and computational cost.