Hyperparameters estimation for the Bayesian localization of the EEG sources with TV priors

Antonio López*, Jesús M. Cortés, Domingo López-Oiler, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

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

Abstract

In this work we propose a new Bayesian method for the non-invasive localization of EEG sources. For this problem, most of the existing methods assume that the sources are distributed throughout the brain volume according to smooth 3D patterns. However, this assumption might fail in pathological conditions, such as in an epileptic brain, where it can occur that the neurophysiological generators are localized in a narrow region, highly compacted, what originates abrupt profiles of electrical activity. This new method incorporates a Total Variation (TV) prior which has been used before in image processing for edge detection and applies variational methods to approximate the probability distributions to estimate the unknown parameters and the sources. The procedure is tested and validated on synthetic EEG data.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Pages489-493
Number of pages5
StatePublished - Nov 27 2012
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: Aug 27 2012Aug 31 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
CountryRomania
CityBucharest
Period8/27/128/31/12

Keywords

  • Bayesian Inference
  • EEG Source Localization
  • Hyperparameters Estimation
  • TV Prior
  • Variational Methods

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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