## MEG source localization under multiple constraints: an extended Bayesian framework (2006)

Venue: | NeuroImage |

Citations: | 27 - 4 self |

### BibTeX

@ARTICLE{Mattout06megsource,

author = {Jérémie Mattout and Christophe Phillips and B William D. Penny and Michael D. Rugg and Karl J. Friston A},

title = {MEG source localization under multiple constraints: an extended Bayesian framework},

journal = {NeuroImage},

year = {2006},

pages = {753--767}

}

### OpenURL

### Abstract

To use Electroencephalography (EEG) and Magnetoencephalography (MEG) as functional brain 3D imaging techniques, identifiable distributed source models are required. The reconstruction of EEG/ MEG sources rests on inverting these models and is ill-posed because the solution does not depend continuously on the data and there is no unique solution in the absence of prior information or constraints. We have described a general framework that can account for several priors in a common inverse solution. An empirical Bayesian framework based on hierarchical linear models was proposed for the analysis of