mne.beamformer.lcmv

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

Warning

DEPRECATED: This function is deprecated and will be removed in 0.17, please use make_lcmv() and apply_lcmv() instead.

Linearly Constrained Minimum Variance (LCMV) beamformer.

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

Parameters:
evoked : Evoked

Evoked data to invert.

forward : dict

Forward operator.

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.

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. 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) [2], if ‘nai’, the Neural Activity Index [1] will be computed, if None, the unit-gain LCMV beamformer [2] will be computed.

max_ori_out: ‘abs’ | ‘signed’

Specify in case of pick_ori=’max-power’.

New in version 0.15.0.

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 sphere model for MEG as in this case the actual rank is 2 not 3.

New in version 0.15.0.

verbose : bool, str, int, or None

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

Returns:
stc : SourceEstimate | VolSourceEstimate

Source time courses.

Notes

The original reference is [1].

References

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