mne.inverse_sparse.
tf_mixed_norm
(evoked, forward, noise_cov, alpha_space, alpha_time, loose='auto', depth=0.8, maxit=3000, tol=0.0001, weights=None, weights_min=None, pca=True, debias=True, wsize=64, tstep=4, window=0.02, return_residual=False, return_as_dipoles=False, verbose=None)[source]¶Time-Frequency Mixed-norm estimate (TF-MxNE).
Compute L1/L2 + L1 mixed-norm solution on time-frequency dictionary. Works with evoked data [R148149] [R149149].
Parameters: | evoked : instance of Evoked
forward : dict
noise_cov : instance of Covariance
alpha_space : float in [0, 100]
alpha_time : float in [0, 100]
loose : float in [0, 1] | ‘auto’
depth: None | float in [0, 1]
maxit : int
tol : float
weights: None | array | SourceEstimate
weights_min: float
pca: bool
debias: bool
wsize: int
tstep: int
window : float or (float, float)
return_residual : bool
return_as_dipoles : bool
verbose : bool, str, int, or None
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Returns: | stc : instance of SourceEstimate
residual : instance of Evoked
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See also
References
[R148149] | (1, 2) A. Gramfort, D. Strohmeier, J. Haueisen, M. Hamalainen, M. Kowalski “Time-Frequency Mixed-Norm Estimates: Sparse M/EEG imaging with non-stationary source activations”, Neuroimage, Volume 70, pp. 410-422, 15 April 2013. DOI: 10.1016/j.neuroimage.2012.12.051 |
[R149149] | (1, 2) A. Gramfort, D. Strohmeier, J. Haueisen, M. Hamalainen, M. Kowalski “Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries”, Proceedings Information Processing in Medical Imaging Lecture Notes in Computer Science, Volume 6801/2011, pp. 600-611, 2011. DOI: 10.1007/978-3-642-22092-0_49 |
mne.inverse_sparse.tf_mixed_norm
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