Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


In this paper, we propose a novel symmetrical EEG/fMRI fusion method which combines EEG and fMRI by means of a common generative model. We use a total variation (TV) prior to model the spatial distribution of the cortical current responses and hemodynamic response functions, and utilize spatially adaptive temporal priors to model their temporal shapes. The spatial adaptivity of the prior model allows for adaptation to the local characteristics of the estimated responses and leads to high estimation performance for the cortical current distribution and the hemodynamic response functions. We utilize a Bayesian formulation with a variational Bayesian framework and obtain a fully automatic fusion algorithm. Simulations with synthetic data and experiments with real data from a multimodal study on face perception demonstrate the performance of the proposed method.

Original languageEnglish (US)
Pages (from-to)113-132
Number of pages20
Issue number1
StatePublished - Mar 1 2011


  • M/EEG source localization
  • Multimodal fusion
  • Spatial adaptivity
  • Total variation
  • Variational Bayes

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


Dive into the research topics of 'Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors'. Together they form a unique fingerprint.

Cite this