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=1e06, update_mode=1, gammas=None, pca=True, return_residual=False, return_as_dipoles=False, rank=None, pick_ori=None, verbose=None)[source]¶ Hierarchical Bayes (GammaMAP) sparse source localization method.
Models each source time course using a zeromean 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 fixedorientation forward operators, a separate gamma is used for each source time course, while for freeorientation 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
 ‘auto’ dict
Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. Can be:
 float between 0 and 1 (inclusive)
If 0, then the solution is computed with fixed orientation. If 1, it corresponds to free orientations.
'auto'
(default)Uses 0.2 for surface source spaces (unless
fixed
is True) and 1.0 for other source spaces (volume or mixed).
 dict
Mapping from the key for a given source space type (surface, volume, discrete) to the loose value. Useful mostly for mixed source spaces.
 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 adict
containing keyword arguments to pass tomne.forward.compute_depth_prior()
(see docstring for details and defaults). This is effectively ignored whenmethod='eLORETA'
.Changed in version 0.20: Depth bias ignored for
method='eLORETA'
. xyz_same_gammabool
Use same gamma for xyz current components at each source space point. Recommended for freeorientation 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.
 pick_ori
None
 “normal”  “vector” Options:
None
Pooling is performed by taking the norm of loose/free orientations. In case of a fixed source space no norm is computed leading to signed source activity.
"normal"
Only the normal to the cortical surface is kept. This is only implemented when working with loose orientations.
"vector"
No pooling of the orientations is done, and the vector result will be returned in the form of a
mne.VectorSourceEstimate
object.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only.
 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
David P. Wipf, Rey Ramírez, Jason Palmer, Scott Makeig, and Bhaskar D. Rao. Analysis of empirical bayesian methods for neuroelectromagnetic source localization. In Bernhard Schölkopf, John C. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, 1505–1512. MIT Press, 2007. URL: http://papers.nips.cc/paper/3089analysisofempiricalbayesianmethodsforneuroelectromagneticsourcelocalization.pdf.
 2
David Wipf and Srikantan Nagarajan. A unified Bayesian framework for MEG/EEG source imaging. NeuroImage, 44(3):947–966, 2009. doi:10.1016/j.neuroimage.2008.02.059.