mne.minimum_norm.cross_talk_function

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

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.

Parameters
inverse_operatorinstance of InverseOperator

Inverse operator.

forwarddict

Forward solution. Note: (Bad) channels not included in forward solution will not be used in CTF computation.

labelslist of Label

Labels for which CTFs shall be computed.

method‘MNE’ | ‘dSPM’ | ‘sLORETA’ | ‘eLORETA’

Inverse method for which CTFs shall be computed.

lambda2float

The regularization parameter.

signedbool

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_compint

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_cpsNone | bool (default True)

Whether to use cortical patch statistics to define normal orientations. Only used when surf_ori and/or force_fixed are True.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
stc_ctfSourceEstimate

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