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, 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, pick_ori=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
 ‘auto’ dict
Value that weights the source variances of the dipole components that are parallel (tangential) to the cortical surface. Can be:
 float between 0 and 1 (inclusive)
If 0, then the solution is computed with fixed orientation. If 1, it corresponds to free orientations.
'auto'
(default)Uses 0.2 for surface source spaces (unless
fixed
is True) and 1.0 for other source spaces (volume or mixed).
 dict
Mapping from the key for a given source space type (surface, volume, discrete) to the loose value. Useful mostly for mixed source spaces.
 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 adict
containing keyword arguments to pass tomne.forward.compute_depth_prior()
(see docstring for details and defaults). This is effectively ignored whenmethod='eLORETA'
.Changed in version 0.20: Depth bias ignored for
method='eLORETA'
. 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.
 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
 ‘info’  ‘full’ dict
This controls the rank computation that can be read from the measurement info or estimated from the data.
None
The rank will be estimated from the data after proper scaling of different channel types.
'info'
The rank is inferred from
info
. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.'full'
The rank is assumed to be full, i.e. equal to the number of good channels. If a
Covariance
is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.dict
Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.
This parameter must be a dictionary whose keys correspond to channel types in the data (e.g.
'meg'
,'mag'
,'grad'
,'eeg'
), and whose values are integers representing the respective ranks. For example,{'mag': 90, 'eeg': 45}
will assume a rank of90
and45
for magnetometer data and EEG data, respectively.The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.
The default is
None
.New in version 0.18.
 pick_ori
None
 “normal”  “vector” Options:
None
Pooling is performed by taking the norm of loose/free orientations. In case of a fixed source space no norm is computed leading to signed source activity.
"normal"
Only the normal to the cortical surface is kept. This is only implemented when working with loose orientations.
"vector"
No pooling of the orientations is done, and the vector result will be returned in the form of a
mne.VectorSourceEstimate
object.
 verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keywordargument only.
 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. Hämäläinen, “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.