UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.

López, JD; Litvak, V; Espinosa, JJ; Friston, K; Barnes, GR; (2014) Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM. Neuroimage , 84 pp. 476-487. 10.1016/j.neuroimage.2013.09.002. Green open access

[img]
Preview
PDF
1-s2.0-S1053811913009361-main.pdf

Download (1MB)

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 standardize 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 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.

Type: Article
Title: Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2013.09.002
Publisher version: http://dx.doi.org/10.1016/j.neuroimage.2013.09.002
Additional information: © 2013 The Authors. Published by Elsevier Inc. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: Bayesian model selection, Free energy, MEG/EEG inverse problem, Multiple Sparse Priors
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1407942
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item