- inverse_operator : instance of InverseOperator
Inverse operator.
- forward : dict
Forward solution. Note: (Bad) channels not included in forward
solution will not be used in PSF computation.
- labels : list of Label
Labels for which PSFs shall be computed.
- method : ‘MNE’ | ‘dSPM’ | ‘sLORETA’ | ‘eLORETA’
Inverse method for which PSFs shall be computed
(for apply_inverse()
).
- lambda2 : float
The regularization parameter (for apply_inverse()
).
- pick_ori : None | “normal”
If “normal”, rather than pooling the orientations by taking the norm,
only the radial component is kept. This is only implemented
when working with loose orientations (for apply_inverse()
).
- mode : ‘mean’ | ‘sum’ | ‘svd’
PSFs can be computed for different summary measures with labels:
‘sum’ or ‘mean’: sum or means of sub-leadfields for labels
This corresponds to situations where labels can be assumed to be
homogeneously activated.
‘svd’: SVD components of sub-leadfields for labels
This is better suited for situations where activation patterns are
assumed to be more variable.
“sub-leadfields” are the parts of the forward solutions that belong to
vertices within individual labels.
- n_svd_comp : int
Number of SVD components for which PSFs will be computed and output
(irrelevant for ‘sum’ and ‘mean’). Explained variances within
sub-leadfields are shown in screen output.
- use_cps : None | bool (default True)
Whether to use cortical patch statistics to define normal
orientations. Only used when surf_ori and/or force_fixed are True.
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).