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='unitnoisegain', 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’  ‘maxpower’  ‘vector’ For forward solutions with fixed orientation, None (default) must be used and a scalar beamformer is computed. For freeorientation forward solutions, a vector beamformer is computed and:
 None
Pools the orientations by taking the norm.
 ‘normal’
Keeps only the radial component.
 ‘maxpower’
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‘unitnoisegain’  ‘nai’ 
None
If ‘unitnoisegain’, the unitnoise gain minimum variance beamformer will be computed (BorgiottiKaplan beamformer) [2], if ‘nai’, the Neural Activity Index [1] will be computed, if None, the unitgain LCMV beamformer [2] will be computed.
 reduce_rankbool
If True, the rank of the denominator of the beamformer formula (i.e., during pseudoinversion) will be reduced by one for each spatial location. Setting
reduce_rank=True
is 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). 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). This is effectively ignored whenmethod='eLORETA'
.Changed in version 0.20: Depth bias ignored for
method='eLORETA'
.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’‘unitnoisegain’ ‘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