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=True, debias=True, time_pca=True, weights=None, weights_min=None, solver='auto', n_mxne_iter=1, return_residual=False, return_as_dipoles=False, verbose=None)[source]¶Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE).
Compute L1/L2 mixed-norm solution [R144145] or L0.5/L2 [R145145] mixed-norm solution on evoked data.
Parameters: | evoked : instance of Evoked or list of instances of Evoked
forward : dict
noise_cov : instance of Covariance
alpha : float
loose : float in [0, 1] | ‘auto’
depth: None | float in [0, 1]
maxit : int
tol : float
active_set_size : int | None
pca : bool
debias : bool
time_pca : bool or int
weights : None | array | SourceEstimate
weights_min : float
solver : ‘prox’ | ‘cd’ | ‘bcd’ | ‘auto’
n_mxne_iter : int
return_residual : bool
return_as_dipoles : bool
verbose : bool, str, int, or None
|
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Returns: | stc : SourceEstimate | list of SourceEstimate
residual : instance of Evoked
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See also
References
[R144145] | (1, 2) A. Gramfort, M. Kowalski, M. Hamalainen, “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 |
[R145145] | (1, 2) 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. |
mne.inverse_sparse.mixed_norm
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