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, pick_ori=None, n_tfmxne_iter=1, 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
 ‘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.
 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.
 pcabool
If True the rank of the data is reduced to true dimension.
 debiasbool
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].
 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 a.k.a. 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.
 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.
 n_tfmxne_iter
int
Number of TFMxNE iterations. If > 1, iterative reweighting is applied.
 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. Hämäläinen, 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. Hämäläinen, 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