Bayesian approach for reconstruction of moving brain dipoles

Petia Georgieva, Nidhal Bouaynaya, Lyudmila Mihaylova, Filipe Silva

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

2 Scopus citations

Abstract

EEG source reconstruction is a challenging task and several methods have been applied to this ill-posed inverse problem. Most of the reconstruction techniques rely on imaging models, where the neural activity is described by a dense set of current dipoles. On the other hand, the point source models, which employ a small number of equivalent current dipoles, has received less attention. While both approaches (imaging versus current dipoles) have their own issues, the main advantage of the dipole models is that they approximate summaries of evoked responses or helpful first approximations. In this paper, we use a recursive Bayesian estimation technique, known as Particle Filter (PF), to simultaneously reconstruct the spatial locations within the head and the corresponding waveforms of the most active dipoles that originated the EEG sensor data. Normally, in EEG source reconstruction, fixed dipole locations are assumed. The proposed PF framework presents a shift in the current paradigm by estimating moving EEG sources, which may vary from one location to another in the brain reflecting the underlying brain activity. Our computer simulations, based on generated and real EEG data, show that the proposed PF approach estimates the dynamic EEG sources with high fidelity.

Original languageEnglish (US)
Title of host publicationImage Analysis and Recognition - 10th International Conference, ICIAR 2013, Proceedings
Pages565-572
Number of pages8
DOIs
StatePublished - Sep 26 2013
Event10th International Conference on Image Analysis and Recognition, ICIAR 2013 - Povoa do Varzim, Portugal
Duration: Jun 26 2013Jun 28 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7950 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Image Analysis and Recognition, ICIAR 2013
CountryPortugal
CityPovoa do Varzim
Period6/26/136/28/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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