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 data. The results demonstrate that the proposed algorithm performs better than that in previous works in terms of both localization accuracy and computational cost.
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