make_lcmv(info, forward, data_cov, reg=0.05, noise_cov=None, label=None, pick_ori=None, rank='info', weight_norm='unit-noise-gain', reduce_rank=False, depth=None, verbose=None)¶
Compute LCMV spatial filter.
The measurement info to specify the channels to include. Bad channels in info[‘bads’] are not used.
- data_covinstance of
The data covariance.
The regularization for the whitened data covariance.
- noise_covinstance of
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.
- labelinstance of
Restricts the LCMV solution to a given label.
None| ‘normal’ | ‘max-power’ | ‘vector’
For forward solutions with fixed orientation, None (default) must be used and a scalar beamformer is computed. For free-orientation forward solutions, a vector beamformer is computed and:
Pools the orientations by taking the norm.
Keeps only the radial component.
Selects orientations that maximize output source power at each location.
Keeps the currents for each direction separate
dict| ‘info’ | ‘full’
This controls the rank computation that can be read from the measurement info or estimated from the data. See
mne.compute_rank()for details.The default is “info”.
- weight_norm‘unit-noise-gain’ | ‘nai’ |
If ‘unit-noise-gain’, the unit-noise gain minimum variance beamformer will be computed (Borgiotti-Kaplan beamformer) , if ‘nai’, the Neural Activity Index  will be computed, if None, the unit-gain LCMV beamformer  will be computed.
If True, the rank of the denominator of the beamformer formula (i.e., during pseudo-inversion) will be reduced by one for each spatial location. Setting
reduce_rank=Trueis typically necessary if you use a single sphere model with MEG data.
Changed in version 0.20: Support for reducing rank in all modes (previously only supported
pick='max_power'with weight normalization).
How to weight (or normalize) the forward using a depth prior. If float (default 0.8), it acts as the depth weighting exponent (
exp) to use, which must be between 0 and 1. None is equivalent to 0, meaning no depth weighting is performed. It can also be a dict containing keyword arguments to pass to
mne.forward.compute_depth_prior()(see docstring for details and defaults). This is effectively ignored when
Changed in version 0.20: Depth bias ignored for
New in version 0.18.
- filtersinstance of
Dictionary containing filter weights from LCMV beamformer. Contains the following keys:
The filter weights of the beamformer.
- ‘data_cov’instance of Covariance
The data covariance matrix used to compute the beamformer.
- ‘noise_cov’instance of Covariance | None
The noise covariance matrix used to compute the beamformer.
- ‘whitener’None | array
Whitening matrix, provided if whitening was applied to the covariance matrix and leadfield during computation of the beamformer weights.
- ‘weight_norm’‘unit-noise-gain’| ‘nai’ | None
Type of weight normalization used to compute the filter weights.
- ‘pick_ori’None | ‘normal’
Orientation selection used in filter computation.
Channels used to compute the beamformer.
Projections used to compute the beamformer.
If True, projections were applied prior to filter computation.
Vertices for which the filter weights were computed.
If True, the filter was computed with free source orientation.
Type of source space.
- filtersinstance of
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
Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media