mne.read_labels_from_annot#
- mne.read_labels_from_annot(subject, parc='aparc', hemi='both', surf_name='white', annot_fname=None, regexp=None, subjects_dir=None, sort=True, verbose=None)[source]#
Read labels from a FreeSurfer annotation file.
Note: Only cortical labels will be returned.
- Parameters:
- subject
str
The FreeSurfer subject name.
- parc
str
The parcellation to use, e.g.,
'aparc'
or'aparc.a2009s'
.- hemi
str
The hemisphere from which to read the parcellation, can be
'lh'
,'rh'
, or'both'
.- surf_name
str
Surface used to obtain vertex locations, e.g.,
'white'
,'pial'
.- annot_fnamepath-like |
None
Filename of the
.annot
file. If not None, only this file is read and the argumentsparc
andhemi
are ignored.- regexp
str
Regular expression or substring to select particular labels from the parcellation. E.g.
'superior'
will return all labels in which this substring is contained.- subjects_dirpath-like |
None
The path to the directory containing the FreeSurfer subjects reconstructions. If
None
, defaults to theSUBJECTS_DIR
environment variable.- sort
bool
If true, labels will be sorted by name before being returned.
New in version 0.21.0.
- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- subject
- Returns:
See also
Examples using mne.read_labels_from_annot
#
Corrupt known signal with point spread
Compare simulated and estimated source activity
Simulate raw data using subject anatomy
Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals
Generate a functional label from source estimates
Compute MNE inverse solution on evoked data with a mixed source space
Visualize source leakage among labels using a circular graph