Generate sparse (n_dipoles) sources time courses from data_fun.
This function randomly selects n_dipoles vertices in the whole
cortex or one single vertex (randomly in or in the center of) each
label if labels is not None. It uses data_fun to generate
waveforms for each vertex.
SourceSpacesThe source space.
intNumber of dipoles to simulate.
arrayTime array.
callable()Function to generate the waveforms. The default is a 100 nAm, 10 Hz
sinusoid as 1e-7 * np.sin(20 * pi * t). The function should take
as input the array of time samples in seconds and return an array of
the same length containing the time courses.
None | list of LabelThe labels. The default is None, otherwise its size must be n_dipoles.
None | int | instance of RandomStateA seed for the NumPy random number generator (RNG). If None (default),
the seed will be obtained from the operating system
(see RandomState for details), meaning it will most
likely produce different output every time this function or method is run.
To achieve reproducible results, pass a value here to explicitly initialize
the RNG with a defined state.
strThe label location to choose. Can be ‘random’ (default) or ‘center’
to use mne.Label.center_of_mass(). Note that for ‘center’
mode the label values are used as weights.
New in version 0.13.
str | NoneThe subject the label is defined for.
Only used with location='center'.
New in version 0.13.
NoneThe path to the directory containing the FreeSurfer subjects
reconstructions. If None, defaults to the SUBJECTS_DIR environment
variable.
New in version 0.13.
strThe 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 version 0.13.
SourceEstimateThe generated source time courses.
See also
Notes
New in version 0.10.0.
mne.simulation.simulate_sparse_stc#Cortical Signal Suppression (CSS) for removal of cortical signals