mne.beamformer.lcmv

mne.beamformer.lcmv(evoked, forward, noise_cov=None, data_cov=None, reg=0.05, label=None, pick_ori=None, picks=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 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.

picks : array-like of int

Channel indices to use for beamforming (if None all channels are used except bad channels). picks is deprecated and will be removed in 0.16, use pick_channels or pick_types instead.

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) [R5353], if ‘nai’, the Neural Activity Index [R5253] will be computed, if None, the unit-gain LCMV beamformer [R5353] will be computed.

max_ori_out: ‘abs’ | ‘signed’

Specify in case of pick_ori=’max-power’. If ‘abs’, the absolute value of the source space time series will be returned, if ‘signed’, the signed source space time series will be returned. ‘abs’ is deprecated and will be removed in 0.16. Set max_ori_out to ‘signed’ to remove this warning.

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 [R5253].

References

[R5253](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
[R5353](1, 2, 3) Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media

Examples using mne.beamformer.lcmv