mne.inverse_sparse.mixed_norm¶

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)[source]¶

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

Compute L1/L2 mixed-norm solution [R45] or L0.5/L2 [R46] 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 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, 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 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() 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

[R45]

(1, 2) 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. https://doi.org/10.1088/0031-9155/57/7/1937

[R46]

(1, 2) 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.

Examples using mne.inverse_sparse.mixed_norm¶

Compute sparse inverse solution with mixed norm: MxNE and irMxNE