mne.simulation.select_source_in_label

mne.simulation.select_source_in_label(src, label, location='random', subject=None, subjects_dir=None, surf='sphere', *, rng=None, random_state=None)

Select source positions using a label.

Parameters:

srclist of dict

The source space.

labelLabel

The label.

locationstr

The label location to choose. Can be ‘random’ (default) or ‘center’ to use mne.Label.center_of_mass() (restricting to vertices both in the label and in the source space). Note that for ‘center’ mode the label values are used as weights.

New in v0.13.

subjectstr | None

The subject the label is defined for. Only used with location='center'.

New in v0.13.

subjects_dirpath-like | None

The path to the directory containing the FreeSurfer subjects reconstructions. If None, defaults to the SUBJECTS_DIR environment variable.

New in v0.13.

surfstr

The surface to use for Euclidean distance center of mass finding. The default here is “sphere”, which finds the center of mass on the spherical surface to help avoid potential issues with cortical folding.

New in v0.13.

rngNone | int | instance of Generator | RandomState

The random number generator (RNG). If None (default), a new numpy.random.Generator seeded from entropy is used. Pass an int or a numpy.random.Generator for reproducible results, or a legacy RandomState to control the random-number stream or for interoperability with third-party code such as scikit-learn that does not accept generators. An integer seed uses numpy.random.default_rng() and therefore produces a different stream than the same integer passed to a legacy random_state or seed parameter.

New in v1.13.

random_stateNone | int | instance of RandomState

Supported for compatibility. New code should use rng. If None, NumPy’s global RandomState is used.

Returns:

lh_vertnolist

Selected source coefficients on the left hemisphere.

rh_vertnolist

Selected source coefficients on the right hemisphere.