Alberto Sorrentino (Warwick)

Bayesian filtering for estimation of brain activity in magnetoencephalography

Magnetoencephalography (MEG) is a sophisticated technique measuring the tiny magnetic fields produced by the brain activity. Relative to other functional neuroimaging techniques MEG recordings feature an outstanding temporal sampling resolution, in principle allowing for a study of the neural dynamics on a millisecond-by-millisecond time scale, but the spatial localization of neural currents from MEG data turns out to be an ill-posed inverse problem, i.e. a problem which has infinitely many solutions. To mitigate ill-posedness, a variety of parametric models of the neural currents are proposed in the burgeoning neuroimaging literature. In particular, under suitable approximations the problem of estimating brain activity from MEG data can be re-phrased as a Bayesian filtering problem with an unknown and time-varying number of sources.

In this talk I will first illustrate a statistical model of source localisation for MEG data which builds directly on the well-established Physics of the electro-magnetic brain field. The focus of the talk will then be to describe the application of a recently developed class of sequential Monte Carlo methods (particle filters) for estimation of the model parameters using empirical MEG data.