mne.inverse_sparse.tf_mixed_norm

mne.inverse_sparse.tf_mixed_norm(evoked, forward, noise_cov, loose='auto', depth=0.8, maxit=3000, tol=0.0001, weights=None, weights_min=0.0, pca=True, debias=True, wsize=64, tstep=4, window=0.02, return_residual=False, return_as_dipoles=False, alpha=None, l1_ratio=None, dgap_freq=10, rank=None, verbose=None)[source]

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

Compute L1/L2 + L1 mixed-norm solution on time-frequency dictionary. Works with evoked data [1] [2].

Parameters
evokedinstance of Evoked

Evoked data to invert.

forwarddict

Forward operator.

noise_covinstance of Covariance

Noise covariance to compute whitener.

loosefloat 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.

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).

maxitint

Maximum number of iterations.

tolfloat

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 or array-like

Length of the STFT window in samples (must be a multiple of 4). If an array is passed, multiple TF dictionaries are used (each having its own wsize and tstep) and each entry of wsize must be a multiple of 4. See [3].

tstep: int or array-like

Step between successive windows in samples (must be a multiple of 2, a divider of wsize and smaller than wsize/2) (default: wsize/2). If an array is passed, multiple TF dictionaries are used (each having its own wsize and tstep), and each entry of tstep must be a multiple of 2 and divide the corresponding entry of wsize. See [3].

windowfloat 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_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.

alphafloat in [0, 100) or None

Overall regularization parameter. If alpha and l1_ratio are not None, alpha_space and alpha_time are overridden by alpha * alpha_max * (1. - l1_ratio) and alpha * alpha_max * l1_ratio. 0 means no regularization, 100 would give 0 active dipole.

l1_ratiofloat in [0, 1] or None

Proportion of temporal regularization. If l1_ratio and alpha are not None, alpha_space and alpha_time are overridden by alpha * alpha_max * (1. - l1_ratio) and alpha * alpha_max * l1_ratio. 0 means no time regularization aka MxNE.

dgap_freqint or numpy.inf

The duality gap is evaluated every dgap_freq iterations.

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.

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.

See also

mixed_norm

References

1

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, pp. 410-422, 15 April 2013. DOI: 10.1016/j.neuroimage.2012.12.051

2

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, Volume 6801/2011, pp. 600-611, 2011. DOI: 10.1007/978-3-642-22092-0_49

3(1,2)

Y. Bekhti, D. Strohmeier, M. Jas, R. Badeau, A. Gramfort. “M/EEG source localization with multiscale time-frequency dictionaries”, 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016. DOI: 10.1109/PRNI.2016.7552337

Examples using mne.inverse_sparse.tf_mixed_norm