mne.beamformer.lcmv

mne.beamformer.lcmv(evoked, forward, noise_cov, data_cov, reg=0.01, label=None, pick_ori=None, picks=None, rank=None, verbose=None)

Linearly Constrained Minimum Variance (LCMV) beamformer.

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

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

Parameters:

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).
Returns:

stc : SourceEstimate | VolSourceEstimate

Source time courses

See also

lcmv_raw, lcmv_epochs

Notes

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