Bayesian M/EEG source reconstruction with spatio-temporal priors

• Published in: NeuroImage (Orlando, Fla.) Vol. 39; no. 1; pp. 318 - 335
• Main Authors: Trujillo-Barreto, Nelson J., Aubert-Vázquez, Eduardo, Penny, William D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 2008; Elsevier Limited
• Subjects: Algorithms; Bayes Theorem; Bayesian models; Brain Mapping - methods; Computer Simulation; Diagnosis, Computer-Assisted - methods; Electroencephalography - methods; Ensemble learning; Estimates; Evoked Potentials, Visual - physiology; Face; Generators; GLM; Humans; M/EEG source localization; Magnetoencephalography - methods; Models, Neurological; Pattern Recognition, Automated - methods; Pattern Recognition, Visual - physiology; Random variables; Spatio-temporal priors; Tomography; Variational Bayes; Bayesian models; GLM; M/EEG source localization; Variational Bayes; Ensemble learning; Spatio-temporal priors

This article proposes a Bayesian spatio-temporal model for source reconstruction of M/EEG data. The usual two-level probabilistic model implicit in most distributed source solutions is extended by adding a third level which describes the temporal evolution of neuronal current sources using time-doma...