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)¶
Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE).
- evokedinstance of
listof instances of
Evoked data to invert.
- noise_covinstance of
Noise covariance to compute whitener.
Regularization parameter. 0 means no regularization, 100 would give 0 active dipole.
float| ‘auto’ |
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.
Uses 0.2 for surface source spaces (unless
fixedis True) and 1.0 for other source spaces (volume or mixed).
Mapping from the key for a given source space type (surface, volume, discrete) to the loose value. Useful mostly for mixed source spaces.
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
dictcontaining keyword arguments to pass to
mne.forward.compute_depth_prior()(see docstring for details and defaults). This is effectively ignored when
Changed in version 0.20: Depth bias ignored for
Maximum number of iterations.
Size of active set increment. If None, no active set strategy is used.
Remove coefficient amplitude bias due to L1 penalty.
- time_pcabool or
If True the rank of the concatenated epochs is reduced to its true dimension. If is ‘int’ the rank is limited to this value.
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).
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.
The number of MxNE iterations. If > 1, iterative reweighting is applied.
If True, the residual is returned as an Evoked instance.
If True, the sources are returned as a list of Dipole instances.
The duality gap is evaluated every dgap_freq iterations. Ignored if solver is ‘cd’.
dict| ‘info’ | ‘full’
This controls the rank computation that can be read from the measurement info or estimated from the data. See
mne.compute_rank()for details.The default is None.
New in version 0.18.
None| “normal” | “vector”
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.
Only the normal to the cortical surface is kept. This is only implemented when working with loose orientations.
No pooling of the orientations is done, and the vector result will be returned in the form of a
- evokedinstance of
A. Gramfort, M. Kowalski, M. Hämäläinen, “Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods”, Physics in Medicine and Biology, 2012. https://doi.org/10.1088/0031-9155/57/7/1937
D. Strohmeier, Y. Bekhti, J. Haueisen, A. Gramfort, “The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction”, IEEE Transactions of Medical Imaging, Volume 35 (10), pp. 2218-2228, 2016.