mne.minimum_norm.cross_talk_function(inverse_operator, forward, labels, method='dSPM', lambda2=0.1111111111111111, signed=False, mode='mean', n_svd_comp=1, verbose=None)

Compute cross-talk functions (CTFs) for linear estimators

Compute cross-talk functions (CTF) in labels for a combination of inverse operator and forward solution. CTFs are computed for test sources that are perpendicular to cortical surface.


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’

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 invidual 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.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).


stc_ctf : SourceEstimate

The CTFs for the specified labels. If mode=’svd’: n_svd_comp components per label are created (i.e. n_svd_comp successive time points in mne_analyze) The last sample is the summed CTF across all labels.

Examples using mne.minimum_norm.cross_talk_function