This documentation is for development version 0.18.dev0.

mne.inverse_sparse.mixed_norm

mne.inverse_sparse.mixed_norm(evoked, forward, noise_cov, alpha, loose='auto', depth=0.8, maxit=3000, tol=0.0001, active_set_size=10, pca=True, debias=True, time_pca=True, weights=None, weights_min=None, solver='auto', n_mxne_iter=1, return_residual=False, return_as_dipoles=False, dgap_freq=10, verbose=None)[source]

Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE).

Compute L1/L2 mixed-norm solution [1] or L0.5/L2 [2] mixed-norm solution on evoked data.

Parameters:
evoked : instance of Evoked or list of instances of Evoked

Evoked data to invert.

forward : dict

Forward operator.

noise_cov : instance of Covariance

Noise covariance to compute whitener.

alpha : float in range [0, 100)

Regularization parameter. 0 means no regularization, 100 would give 0 active dipole.

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 in [0, 1]

Depth weighting coefficients. If None, no depth weighting is performed.

maxit : int

Maximum number of iterations.

tol : float

Tolerance parameter.

active_set_size : int | None

Size of active set increment. If None, no active set strategy is used.

pca : bool

If True the rank of the data is reduced to true dimension.

debias : bool

Remove coefficient amplitude bias due to L1 penalty.

time_pca : bool or int

If True the rank of the concatenated epochs is reduced to its true dimension. If is ‘int’ the rank is limited to this value.

weights : None | array | SourceEstimate

Weight for penalty in mixed_norm. Can be None, a 1d array with shape (n_sources,), or a SourceEstimate (e.g. obtained with wMNE, dSPM, or fMRI).

weights_min : float

Do not consider in the estimation sources for which weights is less than weights_min.

solver : ‘prox’ | ‘cd’ | ‘bcd’ | ‘auto’

The algorithm to use for the optimization. ‘prox’ stands for proximal iterations using the FISTA algorithm, ‘cd’ uses coordinate descent, and ‘bcd’ applies block coordinate descent. ‘cd’ is only available for fixed orientation.

n_mxne_iter : int

The number of MxNE iterations. If > 1, iterative reweighting is applied.

return_residual : bool

If True, the residual is returned as an Evoked instance.

return_as_dipoles : bool

If True, the sources are returned as a list of Dipole instances.

dgap_freq : int or np.inf

The duality gap is evaluated every dgap_freq iterations. Ignored if solver is ‘cd’.

verbose : bool, str, int, or None

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

Returns:
stc : SourceEstimate | list of SourceEstimate

Source time courses for each evoked data passed as input.

residual : instance of Evoked

The residual a.k.a. data not explained by the sources. Only returned if return_residual is True.

See also

tf_mixed_norm

References

[1](1, 2) A. Gramfort, M. Kowalski, M. Hamalainen, “Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods”, Physics in Medicine and Biology, 2012. https://doi.org/10.1088/0031-9155/57/7/1937
[2](1, 2) D. Strohmeier, Y. Bekhti, J. Haueisen, A. Gramfort, “The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction”, IEEE Transactions of Medical Imaging, Volume 35 (10), pp. 2218-2228, 2016.