mne.inverse_sparse.
mixed_norm
(evoked, forward, noise_cov, alpha, loose=0.2, 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, verbose=None)¶Mixednorm estimate (MxNE) and iterative reweighted MxNE (irMxNE)
Compute L1/L2 mixednorm solution or L0.5/L2 mixednorm solution on evoked data.
References: Gramfort A., Kowalski M. and Hamalainen, M., 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
Strohmeier D., Haueisen J., and Gramfort A., Improved MEG/EEG source localization with reweighted mixednorms, 4th International Workshop on Pattern Recognition in Neuroimaging, Tuebingen, 2014
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]
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
verbose : bool, str, int, or None


Returns:  stc : SourceEstimate  list of SourceEstimate
residual : instance of Evoked

See also