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_cov : Covariance
The data covariance.
- reg : float
The regularization for the whitened data covariance.
- noise_cov : 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.
- label : 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 | int | dict
Specified rank of the noise covariance matrix. If None, the rank is
detected automatically. If int, the rank is specified for the MEG
channels. A dictionary with entries ‘eeg’ and/or ‘meg’ can be used
to specify the rank for each modality.
- 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_rank : bool
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.
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
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