Probabilistic initiation and termination for MEG multiple dipole localization using sequential Monte Carlo methods

Xi Chen, Simo Sarkka, Simon Godsill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The paper considers an electromagnetic inverse problem of localizing dipolar neural current sources on brain cortex using magnetoencephalography (MEG) or electroencephalography (EEG) data. We aim to localize the unknown and time-varying number of dipolar current sources using data from multiple MEG coil sensors. In this work, we model the problem in a Bayesian framework, we propose a linear prior detection method as well as a probabilistic approach for target number estimation, and target state initiation/termination. We then use a sequential Monte Carlo (SMC) algorithm to numerically estimate location and moment of the dipolar current sources. We apply the algorithm in both simulated and measured data. Results show that the proposed approach is able to estimate and localize the unknown and time-varying number of dipoles in simulated data with reasonable tracking accuracy and efficiency.

Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Information Fusion, FUSION 2013
PublisherIEEE
Pages580-587
Number of pages8
ISBN (Electronic)978-1-4799-0284-2
ISBN (Print)9786058631113
Publication statusPublished - 21 Oct 2013
Event16th International Conference of Information Fusion, FUSION 2013 - Istanbul, Turkey
Duration: 9 Jul 201312 Jul 2013

Publication series

NameProceedings of the 16th International Conference on Information Fusion, FUSION 2013

Conference

Conference16th International Conference of Information Fusion, FUSION 2013
CountryTurkey
CityIstanbul
Period9/07/1312/07/13

Keywords

  • Bayesian
  • Dipole
  • Localization
  • MEG/EEG
  • SMC

ASJC Scopus subject areas

  • Information Systems

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