conpy.connectivity.dics_connectivity#

conpy.connectivity.dics_connectivity(vertex_pairs, fwd, data_csd, reg=0.05, coh_metric='absolute', n_angles=50, block_size=10000, n_jobs=1, verbose=None)[source]#

Compute spectral connectivity using a DICS beamformer.

Calculates the connectivity between the given vertex pairs using a DICS beamformer [1] [2]. Connectivity is defined in terms of coherence:

C = Sxy^2 [Sxx * Syy]^-1

Where Sxy is the cross-spectral density (CSD) between dipoles x and y, Sxx is the power spectral density (PSD) at dipole x and Syy is the PSD at dipole y.

Parameters:

vertex_pairspair of lists (vert_from_idx, vert_to_idx): Vertex pairs between which connectivity is calculated. The pairs are specified using two lists: the first list contains, for each pair, the index of the first vertex. The second list contains, for each pair, the index of the second vertex.
fwdinstance of Forward: Subject’s forward solution, possibly restricted to only include vertices that are close to the sensors. For ‘canonical’ mode, the orientation needs to be tangential or free.
data_csdinstance of CrossSpectralDensity: The cross spectral density of the data.
regfloat: Tikhonov regularization parameter to control for trade-off between spatial resolution and noise sensitivity. Defaults to 0.05.
coh_metric‘absolute’ | ‘imaginary’: The coherence metric to use. Either the square of absolute coherence (‘absolute’) or the square of the imaginary part of the coherence (‘imaginary’). Defaults to ‘absolute’.
n_anglesint: Number of angles to try when optimizing dipole orientations. Defaults to 50.
block_sizeint: Number of pairs to process in a single batch. Beware of memory requirements, which are n_jobs * block_size. Defaults to 10000.
n_jobsint: Number of blocks to process simultaneously. Defaults to 1.
verbosebool | str | int | None: If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:

connectivityinstance of Connectivity: The adjacency matrix.