mne.beamformer.tf_lcmv¶

mne.beamformer.
tf_lcmv
(epochs, forward, noise_covs, tmin, tmax, tstep, win_lengths, freq_bins, subtract_evoked=False, reg=0.05, label=None, pick_ori=None, n_jobs=1, rank='full', weight_norm='unitnoisegain', raw=None, verbose=None)[source]¶ Warning
DEPRECATED: tf_lcmv is deprecated and will be removed in 0.22, use LCMV with a covariances computed on bandpassed data or DICS instead
5D timefrequency beamforming based on LCMV.
Calculate source power in timefrequency windows using a spatial filter based on the Linearly Constrained Minimum Variance (LCMV) beamforming approach [1]. Bandpass filtered epochs are divided into time windows from which covariance is computed and used to create a beamformer spatial filter.
Note
This implementation has not been heavily tested so please report any issues or suggestions.
 Parameters
 epochs
Epochs
Single trial epochs. It is recommended to pass epochs that have been constructed with
preload=False
(i.e., not preloaded or read from disk) so that the parameterraw=None
can be used below, as this ensures the correctmne.io.Raw
instance is used for bandpass filtering. forward
dict
Forward operator.
 noise_covs
list
of instances ofCovariance
None
Noise covariance for each frequency bin. If provided, whitening will be done. Providing noise covariances is mandatory if you mix sensor types, e.g., gradiometers with magnetometers or EEG with MEG.
 tmin
float
Minimum time instant to consider.
 tmax
float
Maximum time instant to consider.
 tstep
float
Spacing between consecutive time windows, should be smaller than or equal to the shortest time window length.
 win_lengths
list
offloat
Time window lengths in seconds. One time window length should be provided for each frequency bin.
 freq_bins
list
oftuple
offloat
Start and end point of frequency bins of interest.
 subtract_evokedbool
If True, subtract the averaged evoked response prior to computing the tf source grid.
 reg
float
The regularization for the whitened data covariance.
 label
Label
None
Restricts the solution to a given label.
 pick_ori
None
 ‘normal’ If ‘normal’, rather than pooling the orientations by taking the norm, only the radial component is kept. 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.
 n_jobs
int
str
Number of jobs to run in parallel. Can be ‘cuda’ if
cupy
is installed properly. rank
int
None
 ‘full’ This controls the effective rank of the covariance matrix when computing the inverse. The rank can be set explicitly by specifying an integer value. If
None
, the rank will be automatically estimated. Since applying regularization will always make the covariance matrix full rank, the rank is estimated before regularization in this case. If ‘full’, the rank will be estimated after regularization and hence will mean using the full rank, unlessreg=0
is used. The default is'full'
. weight_norm‘unitnoisegain’ 
None
If ‘unitnoisegain’, the unitnoise gain minimum variance beamformer will be computed (BorgiottiKaplan beamformer) [2], if None, the unitgain LCMV beamformer [2] will be computed.
 rawinstance of
Raw
None
The raw instance used to construct the epochs. Must be provided unless epochs are constructed with
preload=False
. verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
 epochs
 Returns
 stcs
list
ofSourceEstimate
Source power at each time window. One SourceEstimate object is returned for each frequency bin.
 stcs
References