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Paper No. 12-10

Download 12-10

A Sorrentino, AM Johansen, JAD Aston, TE Nichols and WS Kendall

Dynamic filtering of Static Dipoles in MagnetoEncephaloGraphy

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 reects 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.

Keywords and phrases:
Magnetoencephalography, Multi-object tracking, Particle filtering, Resample-Move.


We consider the problem of estimating neural activity from measurements

of the magnetic _elds 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 reects

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 _lter and an arti_cial 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 _lter. An application to complex real

data derived from a somatosensory experiment is presented. Assessment of

model _t via marginal likelihood showed a clear preference for the proposed

model and the associated reconstructions show better localisation.

AMS 2000 subject classi_cations: Primary 60G35; secondary 62M05;92C55.

Keywords and phrases: Magnetoencephalography, Multi-object tracking,

Particle _ltering, Resample-Move.