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, pick_ori=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.

forwarddict

Forward operator.

noise_covinstance of Covariance

Noise covariance to compute whitener.

alphafloat

Regularization parameter (noise variance).

loosefloat | ‘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.

depthNone | 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 to mne.forward.compute_depth_prior() (see docstring for details and defaults). This is effectively ignored when method='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 free-orientation forward solutions.

maxitint

Maximum number of iterations.

tolfloat

Tolerance parameter for convergence.

update_modeint

Update mode, 1: MacKay update (default), 2: Modified MacKay update.

gammasarray, 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.

rankNone | dict | ‘info’ | ‘full’

This controls the rank computation that can be read from the measurement info or estimated from the data. See Notes of mne.compute_rank() for details.The default is None.

New in version 0.18.

pick_oriNone | “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, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

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.

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/3089-analysis-of-empirical-bayesian-methods-for-neuroelectromagnetic-source-localization.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.