Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors

Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors. Luessi, M., Babacan, S. D., Molina, R., Booth, J. R., & Katsaggelos, A. K. NeuroImage, 55(1):113–132, mar, 2011.

Paper doi abstract bibtex

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. © 2010 Elsevier Inc.

@article{Martin2011,
abstract = {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. {\textcopyright} 2010 Elsevier Inc.},
author = {Luessi, Martin and Babacan, S. Derin and Molina, Rafael and Booth, James R. and Katsaggelos, Aggelos K.},
doi = {10.1016/j.neuroimage.2010.11.037},
issn = {10538119},
journal = {NeuroImage},
keywords = {M/EEG source localization,Multimodal fusion,Spatial adaptivity,Total variation,Variational Bayes},
month = {mar},
number = {1},
pages = {113--132},
pmid = {21130173},
title = {{Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811910014679},
volume = {55},
year = {2011}
}

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