mne.beamformer.make_lcmv¶
-
mne.beamformer.
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)[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_covinstance of
Covariance
The data covariance.
- reg
float
The regularization for the whitened data covariance.
- noise_covinstance of
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.
- labelinstance of
Label
Restricts the LCMV solution to a given label.
- pick_ori
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:
- None
Pools the orientations by taking the norm.
- ‘normal’
Keeps only the radial component.
- ‘max-power’
Selects orientations that maximize output source power at each location.
- ‘vector’
Keeps the currents for each direction separate
- rank
None
|dict
| ‘info’ | ‘full’ This controls the rank computation that can be read from the measurement info or estimated from the data. See
Notes
ofmne.compute_rank()
for details.The default is “info”.- 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.
- reduce_rankbool
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.
- depth
None
|float
|dict
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 tomne.forward.compute_depth_prior()
(see docstring for details and defaults).New in version 0.18.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
- info
- Returns
- filtersinstance of
Beamformer
Dictionary containing filter weights from LCMV beamformer. Contains the following keys:
- ‘weights’array
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.
- ‘ch_names’list
Channels used to compute the beamformer.
- ‘proj’array
Projections used to compute the beamformer.
- ‘is_ssp’bool
If True, projections were applied prior to filter computation.
- ‘vertices’list
Vertices for which the filter weights were computed.
- ‘is_free_ori’bool
If True, the filter was computed with free source orientation.
- ‘src_type’str
Type of source space.
- filtersinstance of
Notes
The original reference is [1].
References
- 1(1,2)
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)
Sekihara & Nagarajan. Adaptive spatial filters for electromagnetic brain imaging (2008) Springer Science & Business Media