- inverse_operator : instance of InverseOperator
Inverse operator.
- forward : dict
Forward solution. Note: (Bad) channels not included in forward
solution will not be used in CTF computation.
- labels : list of Label
Labels for which CTFs shall be computed.
- method : ‘MNE’ | ‘dSPM’ | ‘sLORETA’ | ‘eLORETA’
Inverse method for which CTFs shall be computed.
- lambda2 : float
The regularization parameter.
- signed : bool
If True, CTFs will be written as signed source estimates. If False,
absolute (unsigned) values will be written
- mode : ‘mean’ | ‘sum’ | ‘svd’
CTFs can be computed for different summary measures with labels:
‘sum’ or ‘mean’: sum or means of sub-inverses for labels
This corresponds to situations where labels can be assumed to be
homogeneously activated.
‘svd’: SVD components of sub-inverses for labels
This is better suited for situations where activation patterns are
assumed to be more variable. “sub-inverse” is the part of the inverse
matrix that belongs to vertices within individual labels.
- n_svd_comp : int
Number of SVD components for which CTFs will be computed and output
(irrelevant for ‘sum’ and ‘mean’). Explained variances within
sub-inverses 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).