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=None, 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]¶ TimeFrequency Mixednorm estimate (TFMxNE).
Compute L1/L2 + L1 mixednorm solution on timefrequency dictionary. Works with evoked data [1] [2].
 Parameters
 evokedinstance of
Evoked
Evoked data to invert.
 forward
dict
Forward operator.
 noise_covinstance of
Covariance
Noise covariance to compute whitener.
 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 surfaceoriented source space and set to 1.0 for volumic or discrete source space.
 depth
None
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 tomne.forward.compute_depth_prior()
(see docstring for details and defaults). 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 or arraylike
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 arraylike
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].
 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_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.
 alpha
float
in [0, 100) orNone
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_ratio
float
in [0, 1] orNone
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_freq
int
ornumpy.inf
The duality gap is evaluated every dgap_freq iterations.
 rank
None
dict
 ‘info’  ‘full’ This controls the rank computation that can be read from the measurement info or estimated from the data. See
Notes
ofmne.compute_rank()
for details.The default is None.New in version 0.18.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 evokedinstance of
 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.
 stcinstance of
See also
References
 1
A. Gramfort, D. Strohmeier, J. Haueisen, M. Hamalainen, M. Kowalski “TimeFrequency MixedNorm Estimates: Sparse M/EEG imaging with nonstationary source activations”, Neuroimage, Volume 70, pp. 410422, 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 TimeFrequency Dictionaries”, Proceedings Information Processing in Medical Imaging Lecture Notes in Computer Science, Volume 6801/2011, pp. 600611, 2011. DOI: 10.1007/9783642220920_49
 3(1,2)
Y. Bekhti, D. Strohmeier, M. Jas, R. Badeau, A. Gramfort. “M/EEG source localization with multiscale timefrequency dictionaries”, 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016. DOI: 10.1109/PRNI.2016.7552337