mne.stc_to_label#

mne.stc_to_label(stc, src=None, smooth=True, connected=False, subjects_dir=None, verbose=None)[source]#

Compute a label from the non-zero sources in an stc object.

Parameters

stcSourceEstimate: The source estimates.
srcSourceSpaces | str | None: The source space over which the source estimates are defined. If it’s a string it should the subject name (e.g. fsaverage). Can be None if stc.subject is not None.
smoothbool: Fill in vertices on the cortical surface that are not in the source space based on the closest source space vertex (requires src to be a SourceSpace).
connectedbool: If True a list of connected labels will be returned in each hemisphere. The labels are ordered in decreasing order depending of the maximum value in the stc.
subjects_dirpath-like | None: The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.
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

labelslist of Label | list of list of Label: The generated labels. If connected is False, it returns a list of Labels (one per hemisphere). If no Label is available in a hemisphere, None is returned. If connected is True, it returns for each hemisphere a list of connected labels ordered in decreasing order depending of the maximum value in the stc. If no Label is available in an hemisphere, an empty list is returned.

Examples using `mne.stc_to_label`#

mne.split_label

mne.stc_near_sensors