mne.inverse_sparse.tf_mixed_norm

mne.inverse_sparse.tf_mixed_norm(evoked, forward, noise_cov, alpha_space, alpha_time, loose=0.2, depth=0.8, maxit=3000, tol=0.0001, weights=None, weights_min=None, pca=True, debias=True, wsize=64, tstep=4, window=0.02, return_residual=False, verbose=None)

Time-Frequency Mixed-norm estimate (TF-MxNE)

Compute L1/L2 + L1 mixed-norm solution on time frequency dictionary. Works with evoked data.

References:

A. Gramfort, D. Strohmeier, J. Haueisen, M. Hamalainen, M. Kowalski Time-Frequency Mixed-Norm Estimates: Sparse M/EEG imaging with non-stationary source activations Neuroimage, Volume 70, 15 April 2013, Pages 410-422, ISSN 1053-8119, DOI: 10.1016/j.neuroimage.2012.12.051.

A. Gramfort, D. Strohmeier, J. Haueisen, M. Hamalainen, M. Kowalski Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries Proceedings Information Processing in Medical Imaging Lecture Notes in Computer Science, 2011, Volume 6801/2011, 600-611, DOI: 10.1007/978-3-642-22092-0_49 https://doi.org/10.1007/978-3-642-22092-0_49

Parameters:

evoked : instance of Evoked

Evoked data to invert.

forward : dict

Forward operator.

noise_cov : instance of Covariance

Noise covariance to compute whitener.

alpha_space : float in [0, 100]

Regularization parameter for spatial sparsity. If larger than 100, then no source will be active.

alpha_time : float in [0, 100]

Regularization parameter for temporal sparsity. It set to 0, no temporal regularization is applied. It this case, TF-MxNE is equivalent to MxNE with L21 norm.

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.

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.

pca: bool :

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

debias: bool :

Remove coefficient amplitude bias due to L1 penalty.

wsize: int :

Length of the STFT window in samples (must be a multiple of 4).

tstep: int :

Step between successive windows in samples (must be a multiple of 2, a divider of wsize and smaller than wsize/2) (default: wsize/2).

window : float or (float, float)

Length of time window used to take care of edge artifacts in seconds. It can be one float or float if the values are different for left and right window length.

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).

Returns:

stc : instance of SourceEstimate

Source time courses.

residual : instance of Evoked

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

See also

mixed_norm