SPM8 exploits hierarchical or empirical Bayes to solve the distributed source reconstruction problem in electro- and magnetoencephalography (EEG and MEG). This rests on 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.

K.J. Friston, L. Harrison, J. Daunizeau, S.J. Kiebel, C. Phillips, N. Trujillo-Bareto, R.N.A. Henson, G. Flandin, and J. Mattout. Multiple sparse priors for the M/EEG inverse problem. NeuroImage, 39(3):1104-1120, 2008.
 
These descriptions of the new features are taken from the SPM8 Release Notes
 
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