mne.inverse_sparse.mixed_norm(evoked, forward, noise_cov, alpha, loose=0.2, 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, verbose=None)

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

Compute L1/L2 mixed-norm solution or L0.5/L2 mixed-norm solution on evoked data.

References: Gramfort A., Kowalski M. and Hamalainen, M., Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Physics in Medicine and Biology, 2012

Strohmeier D., Haueisen J., and Gramfort A., Improved MEG/EEG source localization with reweighted mixed-norms, 4th International Workshop on Pattern Recognition in Neuroimaging, Tuebingen, 2014


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

Regularization parameter.

loose : float in [0, 1]

Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. If loose is 0 or None then the solution is computed with fixed orientation. If loose is 1, it corresponds to free orientations.

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 or 1d array of length n_sources or a SourceEstimate e.g. obtained with wMNE or 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 interations 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.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).


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


Examples using mne.inverse_sparse.mixed_norm