Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG

• Published in: NeuroImage (Orlando, Fla.) Vol. 39; no. 2; pp. 728 - 741
• Main Authors: Kiebel, Stefan J., Daunizeau, Jean, Phillips, Christophe, Friston, Karl J.
• Format: Journal Article Web Resource
• Language: English
• Published: United States Elsevier Inc 15.01.2008; Elsevier Limited; Academic Press Inc Elsevier Science
• Subjects: Algorithms; Approximation; Bayes Theorem; EEG; Electroencephalography; Electroencephalography - statistics & numerical data; Equivalent current dipole; Evoked Potentials - physiology; Evoked Potentials, Auditory - physiology; Human health sciences; Humans; Magnetoencephalography - statistics & numerical data; Markov Chains; MEG; Models, Statistical; Neurosciences & behavior; Neurosciences & comportement; Physiology; Radiologie, médecine & imagerie nucléaire; Radiology, nuclear medicine & imaging; Reproducibility of Results; Sciences de la santé humaine; Sciences sociales & comportementales, psychologie; Sensors; Social & behavioral sciences, psychology; Software; Variational Bayes; Equivalent current dipole; Variational Bayes; EEG; MEG

In magneto- and electroencephalography (M/EEG), spatial modelling of sensor data is necessary to make inferences about underlying brain activity. Most source reconstruction techniques belong to one of two approaches: point source models, which explain the data with a small number of equivalent curre...