Dynamic filtering of static dipoles in magnetoencephalography

TL;DR

This paper introduces a dynamic filtering approach for magnetoencephalography that models neural currents as evolving dipoles with fixed locations, improving localization accuracy over previous methods.

Contribution

The paper develops a novel sequential Monte Carlo algorithm for dynamic dipole filtering that avoids nonphysical artifacts and enhances localization precision.

Findings

Average localization error halved compared to bootstrap filter

Model fit assessed via marginal likelihood shows clear improvement

Better localization in real somatosensory data

Abstract

We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem and model the neural current as a collection of evolving current dipoles, which appear and disappear, but whose locations are constant throughout their lifetime. This fully reflects the physiological interpretation of the model. In order to conduct inference under this proposed model, it was necessary to develop an algorithm based around state-of-the-art sequential Monte Carlo methods employing carefully designed importance distributions. Previous work employed a bootstrap filter and an artificial dynamic structure where dipoles performed a random walk in space, yielding nonphysical artefacts in the reconstructions; such artefacts are not observed when using the proposed model. The algorithm is validated with simulated data, in which it provided an average localisation error which is approximately half that of the bootstrap filter. An application to complex real data derived from a somatosensory experiment is presented. Assessment of model fit via marginal likelihood showed a clear preference for the proposed model and the associated reconstructions show better localisation.