mne.inverse_sparse.gamma_map¶
-
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, rank=None, 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).
- Parameters
- evokedinstance of
Evoked
Evoked data to invert.
- forward
dict
Forward operator.
- noise_covinstance of
Covariance
Noise covariance to compute whitener.
- alpha
float
Regularization parameter (noise variance).
- loose
float
in [0, 1] | ‘auto’ Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. If loose is 0 then the solution is computed with fixed orientation. If loose is 1, it corresponds to free orientations. The default value (‘auto’) is set to 0.2 for surface-oriented source space and set to 1.0 for volumic or discrete source space.
- depth
None
|float
|dict
How to weight (or normalize) the forward using a depth prior. If float (default 0.8), it acts as the depth weighting exponent (
exp
) to use, which must be between 0 and 1. None is equivalent to 0, meaning no depth weighting is performed. It can also be a dict containing keyword arguments to pass tomne.forward.compute_depth_prior()
(see docstring for details and defaults).- xyz_same_gammabool
Use same gamma for xyz current components at each source space point. Recommended for free-orientation forward solutions.
- maxit
int
Maximum number of iterations.
- tol
float
Tolerance parameter for convergence.
- update_mode
int
Update mode, 1: MacKay update (default), 2: Modified MacKay update.
- gammas
array
, shape=(n_sources,) Initial values for posterior variances (gammas). If None, a variance of 1.0 is used.
- pcabool
If True the rank of the data is reduced to the true dimension.
- return_residualbool
If True, the residual is returned as an Evoked instance.
- return_as_dipolesbool
If True, the sources are returned as a list of Dipole instances.
- rank
None
|dict
| ‘info’ | ‘full’ This controls the rank computation that can be read from the measurement info or estimated from the data. See
Notes
ofmne.compute_rank()
for details.The default is None.New in version 0.18.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
- evokedinstance of
- Returns
- stcinstance of
SourceEstimate
Source time courses.
- residualinstance of
Evoked
The residual a.k.a. data not explained by the sources. Only returned if return_residual is True.
- stcinstance of
References
- 1
Wipf et al. Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization, Advances in Neural Information Process. Systems (2007)
- 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