Bayesian smoothing of dipoles in Magneto-/Electro-encephalography

TL;DR

This paper introduces a Monte Carlo smoothing algorithm for dynamic multi-dipole estimation in M/EEG data, improving source localization accuracy, especially at source onset, with validation on simulated and epileptic patient data.

Contribution

It presents a novel Monte Carlo smoothing method for better off-line neural source estimation in M/EEG analysis, enhancing accuracy over existing filtering approaches.

Findings

Smoothing estimates are more accurate than filtering, especially at source onset.

The method improves localization of epileptic source onset.

Validated with real patient data showing practical benefits.

Abstract

We describe a novel method for dynamic estimation of multi-dipole states from Magneto/Electro-encephalography (M/EEG) time series. The new approach builds on the recent development of particle filters for M/EEG; these algorithms approximate, with samples and weights, the posterior distribution of the neural sources at time t given the data up to time t. However, for off-line inference purposes it is preferable to work with the smoothing distribution, i.e. the distribution for the neural sources at time t conditioned on the whole time series. In this study, we use a Monte Carlo algorithm to approximate the smoothing distribution for a time-varying set of current dipoles. We show, using numerical simulations, that the estimates provided by the smoothing distribution are more accurate than those provided by the filtering distribution, particularly at the appearance of the source. We validate the proposed algorithm using an experimental dataset recorded from an epileptic patient. Improved localization of the source onset can be particularly relevant in source modeling of epileptic patients, where the source onset brings information on the epileptogenic zone.