mne.beamformer.make_lcmv

mne.beamformer.make_lcmv(info, forward, data_cov, reg=0.05, noise_cov=None, label=None, pick_ori=None, rank=None, weight_norm='unit-noise-gain', reduce_rank=False, verbose=None)[source]

Compute LCMV spatial filter.

Parameters:

info : dict

The measurement info to specify the channels to include. Bad channels in info[‘bads’] are not used.

forward : dict

Forward operator.

data_cov : Covariance

The data covariance.

reg : float

The regularization for the whitened data covariance.

noise_cov : Covariance

The noise covariance. If provided, whitening will be done. Providing a noise covariance is mandatory if you mix sensor types, e.g. gradiometers with magnetometers or EEG with MEG.

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. If None, the solution depends on the forward model: if the orientation is fixed, a scalar beamformer is computed. If the forward model has free orientation, a vector beamformer is computed, combining the output for all source orientations.

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.

weight_norm : ‘unit-noise-gain’ | ‘nai’ | None

If ‘unit-noise-gain’, the unit-noise gain minimum variance beamformer will be computed (Borgiotti-Kaplan beamformer) [R6565], if ‘nai’, the Neural Activity Index [R6465] will be computed, if None, the unit-gain LCMV beamformer [R6565] will be computed.

reduce_rank : bool

If True, the rank of the leadfield will be reduced by 1 for each spatial location. Setting reduce_rank to True is typically necessary if you use a single sphere model for MEG.

verbose : bool, str, int, or None

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

Returns:

filters | dict

Beamformer weights.

Notes

The original reference is [R6465].

References

[R6465](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
[R6565](1, 2, 3) Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media