A Bayesian Particle Filtering Method For Brain Source Localisation

TL;DR

This paper introduces a Bayesian particle filtering approach for localizing multiple neural sources in the brain using MEG data, improving accuracy and efficiency over existing methods.

Contribution

It develops a novel Bayesian particle filtering algorithm with adaptive ROI estimation for dynamic brain source localization, handling unknown dipole numbers.

Findings

Improved dipole number estimation accuracy

Enhanced localization precision

Reduced computational cost

Abstract

In this paper, we explore the multiple source localisation problem in the cerebral cortex using magnetoencephalography (MEG) data. We model neural currents as point-wise dipolar sources which dynamically evolve over time, then model dipole dynamics using a probabilistic state space model in which dipole locations are strictly constrained to lie within the cortex. Based on the proposed models, we develop a Bayesian particle filtering algorithm for localisation of both known and unknown numbers of dipoles. The algorithm consists of a region of interest (ROI) estimation step for initial dipole number estimation, a Gibbs multiple particle filter (GMPF) step for individual dipole state estimation, and a selection criterion step for selecting the final estimates. The estimated results from the ROI estimation are used to adaptively adjust particle filter's sample size to reduce the overall computational cost. The proposed models and the algorithm are tested in numerical experiments. Results are compared with existing particle filtering methods. The numerical results show that the proposed methods can achieve improved performance metrics in terms of dipole number estimation and dipole localisation.