mne.inverse_sparse.mixed_norm¶

mne.inverse_sparse.
mixed_norm
(evoked, forward, noise_cov, alpha, loose='auto', depth=0.8, maxit=3000, tol=0.0001, active_set_size=10, pca=None, debias=True, time_pca=True, weights=None, weights_min=0.0, solver='auto', n_mxne_iter=1, return_residual=False, return_as_dipoles=False, dgap_freq=10, rank=None, verbose=None)[source]¶ Mixednorm estimate (MxNE) and iterative reweighted MxNE (irMxNE).
Compute L1/L2 mixednorm solution [1] or L0.5/L2 [2] mixednorm solution on evoked data.
 Parameters
 evokedinstance of
Evoked
orlist
of instances ofEvoked
Evoked data to invert.
 forward
dict
Forward operator.
 noise_covinstance of
Covariance
Noise covariance to compute whitener.
 alpha
float
inrange
[0, 100) Regularization parameter. 0 means no regularization, 100 would give 0 active dipole.
 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.
 active_set_size
int
None
Size of active set increment. If None, no active set strategy is used.
 pcabool
If True the rank of the data is reduced to true dimension.
 debiasbool
Remove coefficient amplitude bias due to L1 penalty.
 time_pcabool 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 iterations 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_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.
 dgap_freq
int
ornumpy.inf
The duality gap is evaluated every dgap_freq iterations. Ignored if solver is ‘cd’.
 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
 stc
SourceEstimate
list
ofSourceEstimate
Source time courses for each evoked data passed as input.
 residualinstance of
Evoked
The residual a.k.a. data not explained by the sources. Only returned if return_residual is True.
 stc
See also
References
 1
A. Gramfort, M. Kowalski, M. Hamalainen, “Mixednorm estimates for the M/EEG inverse problem using accelerated gradient methods”, Physics in Medicine and Biology, 2012. https://doi.org/10.1088/00319155/57/7/1937
 2
D. Strohmeier, Y. Bekhti, J. Haueisen, A. Gramfort, “The Iterative Reweighted MixedNorm Estimate for SpatioTemporal MEG/EEG Source Reconstruction”, IEEE Transactions of Medical Imaging, Volume 35 (10), pp. 22182228, 2016.