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='unit-noise-gain', raw=None, verbose=None)¶
5D time-frequency beamforming based on LCMV.
Calculate source power in time-frequency windows using a spatial filter based on the Linearly Constrained Minimum Variance (LCMV) beamforming approach . Band-pass filtered epochs are divided into time windows from which covariance is computed and used to create a beamformer spatial filter.
This implementation has not been heavily tested so please report any issues or suggestions.
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 parameter
raw=Nonecan be used below, as this ensures the correct
mne.io.Rawinstance is used for band-pass filtering.
listof instances of
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.
Minimum time instant to consider.
Maximum time instant to consider.
Spacing between consecutive time windows, should be smaller than or equal to the shortest time window length.
Time window lengths in seconds. One time window length should be provided for each frequency bin.
Start and end point of frequency bins of interest.
If True, subtract the averaged evoked response prior to computing the tf source grid.
The regularization for the whitened data covariance.
Restricts the solution to a given label.
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.
Number of jobs to run in parallel. Can be ‘cuda’ if
cupyis installed properly.
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, unless
reg=0is used. The default is
- weight_norm‘unit-noise-gain’ |
- rawinstance of
The raw instance used to construct the epochs. Must be provided unless epochs are constructed with