- 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 parameter raw=None
can be used
below, as this ensures the correct mne.io.Raw
instance is
used for band-pass filtering.
- forward : dict
Forward operator.
- noise_covs : list of instances of Covariance | 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 of float
Time window lengths in seconds. One time window length should be
provided for each frequency bin.
- freq_bins : list of tuple of float
Start and end point of frequency bins of interest.
- subtract_evoked : bool
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, unless reg=0
is used.
The default is 'full'
.
- weight_norm : ‘unit-noise-gain’ | None
If ‘unit-noise-gain’, the unit-noise gain minimum variance beamformer
will be computed (Borgiotti-Kaplan beamformer) [2],
if None, the unit-gain LCMV beamformer [2] will be computed.
- raw : instance of Raw | None
The raw instance used to construct the epochs.
Must be provided unless epochs are constructed with
preload=False
.
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).