Multiple sparse priors for the M/EEG inverse problem

  title={Multiple sparse priors for the M/EEG inverse problem},
  author={Karl J. Friston and Lee M. Harrison and Jean Daunizeau and Stefan J. Kiebel and Christophe Phillips and Nelson J. Trujillo-Barreto and Richard N. A. Henson and Guillaume Flandin and J{\'e}r{\'e}mie Mattout},
  volume={39 3},
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… CONTINUE READING
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