mne.inverse_sparse.
gamma_map
(evoked, forward, noise_cov, alpha, loose='auto', depth=0.8, xyz_same_gamma=True, maxit=10000, tol=1e-06, update_mode=1, gammas=None, pca=True, return_residual=False, return_as_dipoles=False, verbose=None)[source]¶Hierarchical Bayes (Gamma-MAP) sparse source localization method.
Models each source time course using a zero-mean Gaussian prior with an unknown variance (gamma) parameter. During estimation, most gammas are driven to zero, resulting in a sparse source estimate, as in [1] and [2].
For fixed-orientation forward operators, a separate gamma is used for each source time course, while for free-orientation forward operators, the same gamma is used for the three source time courses at each source space point (separate gammas can be used in this case by using xyz_same_gamma=False).
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References
[1] | (1, 2) Wipf et al. Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization, Advances in Neural Information Process. Systems (2007) |
[2] | (1, 2) D. Wipf, S. Nagarajan “A unified Bayesian framework for MEG/EEG source imaging”, Neuroimage, Volume 44, Number 3, pp. 947-966, Feb. 2009. DOI: 10.1016/j.neuroimage.2008.02.059 |
mne.inverse_sparse.gamma_map
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