Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors.
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.
Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Road, Evanston IL 60208, USA. m-luessi@northwestern.edu
Martin
M
Luessi
S Derin
SD
Babacan
Rafael
R
Molina
James R
JR
Booth
Aggelos K
AK
Katsaggelos
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*