Multiple sparse priors for the M/EEG inverse problem
| Autor: | Jean Daunizeau, Guillaume Flandin, Jérémie Mattout, Richard N. Henson, Karl J. Friston, Christophe Phillips, Nelson J. Trujillo-Barreto, Lee M. Harrison, Stefan J. Kiebel |
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| Rok vydání: | 2008 |
| Předmět: |
Cognitive Neuroscience
Bayesian probability Inference Bayes' theorem Prior probability Expectation–maximization algorithm Image Processing Computer-Assisted Humans sort Computer Simulation Evoked Potentials Mathematics Likelihood Functions Models Statistical business.industry Model selection Distributed element model Magnetoencephalography Reproducibility of Results Bayes Theorem Electroencephalography Pattern recognition Neurology Artificial intelligence business Algorithms Software |
| Zdroj: | NeuroImage. 39:1104-1120 |
| ISSN: | 1053-8119 |
| DOI: | 10.1016/j.neuroimage.2007.09.048 |
| Popis: | This paper describes an application of hierarchical or empirical Bayes to the distributed source reconstruction problem in electro- and magnetoencephalography (EEG and MEG). The key contribution is the automatic selection of multiple cortical sources with compact spatial support that are specified in terms of empirical priors. This obviates the need to use priors with a specific form (e.g., smoothness or minimum norm) or with spatial structure (e.g., priors based on depth constraints or functional magnetic resonance imaging results). Furthermore, the inversion scheme allows for a sparse solution for distributed sources, of the sort enforced by equivalent current dipole (ECD) models. This means the approach automatically selects either a sparse or a distributed model, depending on the data. The scheme is compared with conventional applications of Bayesian solutions to quantify the improvement in performance. |
| Databáze: | OpenAIRE |
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