Symmetrical EEG/fMRI fusion with spatially adaptive priors using variational distribution approximation

Martin Luessi*, S. Derin Babacan, Rafael Molina, James R. Booth, Aggelos K. Katsaggelos

*Corresponding author for this work

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

1 Scopus citations

Abstract

In this paper, we propose a symmetrical EEG/fMRI fusion algorithm which combines EEG and fMRI by means of a common generative model. The use of a total variation (TV) prior as well as spatially adaptive temporal priors enables adaptation to the local characteristics of the estimated responses. We utilize an approximate variational Bayesian framework and obtain a fully automatic fusion algorithm. Simulation results demonstrate that the proposed algorithm outperforms existing EEG/fMRI fusion methods.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages638-641
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • EEG
  • Total variation (TV)
  • Variational Bayesian methods
  • fMRI

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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