mne.source_space.compute_distance_to_sensors#

mne.source_space.compute_distance_to_sensors(src, info, picks=None, trans=None, verbose=None)[source]#

Compute distances between vertices and sensors.

Parameters:

srcinstance of SourceSpaces: The object with vertex positions for which to compute distances to sensors.
infomne.Info | None: The mne.Info object with information about the sensors and methods of measurement. Must contain sensor positions to which distances shall be computed.
picksstr | list | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
transstr | dict | instance of Transform: If str, the path to the head<->MRI transform *-trans.fif file produced during coregistration. Can also be 'fsaverage' to use the built-in fsaverage transformation.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

deptharray of shape (n_vertices, n_channels): The Euclidean distances of source space vertices with respect to sensors.

Examples using `mne.source_space.compute_distance_to_sensors`#