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

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.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Martin Luessi, S. Derin Babacan, Rafael Molina, James R. Booth, Aggelos K. Katsaggelos
• Keywords: Adaptive Temporal Priors, Common Generative Model, Fusion Algorithm, ICASSP 2010, Signal Processing