title: Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.
abstract: The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm.
authors.affiliation: Departamento de Ingeniería Electrónica, Universidad de Antioquia, Medellín, Colombia. Electronic address: josedavid@udea.edu.co.
authors.forename: J D
authors.initials: JD
authors.lastname: López
authors.forename: V
authors.initials: V
authors.lastname: Litvak
authors.forename: J J
authors.initials: JJ
authors.lastname: Espinosa
authors.forename: K
authors.initials: K
authors.lastname: Friston
authors.forename: G R
authors.initials: GR
authors.lastname: Barnes
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bayesian model selection; Free energy; MEG/EEG inverse problem; Multiple Sparse Priors
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