mne.beamformer.
lcmv_raw
(raw, forward, noise_cov, data_cov, reg=0.05, label=None, start=None, stop=None, picks=None, pick_ori=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 raw data. NOTE : This implementation has not been heavily tested so please report any issue or suggestions.
Parameters: | raw : mne.io.Raw
forward : dict
noise_cov : Covariance
data_cov : Covariance
reg : float
label : Label
start : int
stop : int
picks : array-like of int
pick_ori : None | ‘normal’ | ‘max-power’
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 [R6061].
References
[R6061] | (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 |
[R6161] | (1, 2, 3) Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media |