Fast robust subject-independent magnetoencephalographic source localization using an artificial neural network.

Abstract:

:We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software.

journal_name

Hum Brain Mapp

journal_title

Human brain mapping

authors

Jun SC,Pearlmutter BA

doi

10.1002/hbm.20068

keywords:

subject

Has Abstract

pub_date

2005-01-01 00:00:00

pages

21-34

issue

1

eissn

1065-9471

issn

1097-0193

journal_volume

24

pub_type

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