mne.beamformer.lcmv(evoked, forward, noise_cov, data_cov, reg=0.05, label=None, pick_ori=None, picks=None, rank=None, verbose=None)[source]

Linearly Constrained Minimum Variance (LCMV) beamformer.

Compute Linearly Constrained Minimum Variance (LCMV) beamformer on evoked data.


This implementation has not been heavily tested so please report any issue or suggestions.


evoked : Evoked

Evoked data to invert

forward : dict

Forward operator

noise_cov : Covariance

The noise covariance

data_cov : Covariance

The data covariance

reg : float

The regularization for the whitened data covariance.

label : Label

Restricts the LCMV solution to a given label

pick_ori : None | ‘normal’ | ‘max-power’

If ‘normal’, rather than pooling the orientations by taking the norm, only the radial component is kept. If ‘max-power’, the source orientation that maximizes output source power is chosen.

picks : array-like of int

Channel indices to use for beamforming (if None all channels are used except bad channels).

rank : None | int | dict

Specified rank of the noise covariance matrix. If None, the rank is detected automatically. If int, the rank is specified for the MEG channels. A dictionary with entries ‘eeg’ and/or ‘meg’ can be used to specify the rank for each modality.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).


stc : SourceEstimate | VolSourceEstimate

Source time courses

See also

lcmv_raw, lcmv_epochs


The original reference is: 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

The reference for finding the max-power orientation is: Sekihara et al. Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction. Biomedical Engineering (2004) vol. 51 (10) pp. 1726–34