mne.beamformer.
lcmv
(evoked, forward, noise_cov=None, data_cov=None, reg=0.05, label=None, pick_ori=None, picks=None, rank=None, weight_norm='unit-noise-gain', max_ori_out='abs', reduce_rank=False, verbose=None)[source]¶Linearly Constrained Minimum Variance (LCMV) beamformer.
Compute Linearly Constrained Minimum Variance (LCMV) beamformer on evoked data.
Parameters: | evoked : Evoked
forward : dict
noise_cov : Covariance
data_cov : Covariance
reg : float
label : Label
pick_ori : None | ‘normal’ | ‘max-power’
picks : array-like of int
rank : None | int | dict
weight_norm : ‘unit-noise-gain’ | ‘nai’ | None max_ori_out: ‘abs’ | ‘signed’
reduce_rank : bool
verbose : bool, str, int, or None
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Returns: | stc : SourceEstimate | VolSourceEstimate
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See also
Notes
The original reference is [R5253].
References
[R5253] | (1, 2, 3) Van Veen et al. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. Biomedical Engineering (1997) vol. 44 (9) pp. 867–880 |
[R5353] | (1, 2, 3) Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media |
mne.beamformer.lcmv
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