mne.simulation.simulate_evoked

mne.simulation.simulate_evoked(fwd, stc, info, cov=None, nave=30, iir_filter=None, use_cps=True, verbose=None, *, rng=None, random_state=None)

Generate noisy evoked data.

Note

No projections from info will be present in the output evoked. You can use e.g. evoked.add_proj or evoked.set_eeg_reference to add them afterward as necessary.

Parameters:

fwdinstance of Forward

A forward solution.

stcSourceEstimate object

The source time courses.

infomne.Info

The mne.Info object with information about the sensors and methods of measurement. Used to generate the evoked.

covCovariance object | None

The noise covariance. If None, no noise is added.

naveint

Number of averaged epochs (defaults to 30).

New in v0.15.0.

iir_filterNone | array

IIR filter coefficients (denominator) e.g. [1, -1, 0.2].

use_cpsbool

Whether to use cortical patch statistics to define normal orientations for surfaces (default True).

New in v0.15.

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.

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:

evokedEvoked object

The simulated evoked data.

See also

simulate_raw

simulate_stc

simulate_sparse_stc

Notes

To make the equivalence between snr and nave, when the snr is given instead of nave:

nave = (1 / 10 ** ((actual_snr - snr)) / 20) ** 2

where actual_snr is the snr to the generated noise before scaling.

New in v0.10.0.

Examples using mne.simulation.simulate_evoked

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

Generate simulated evoked data

Generate simulated evoked data

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Corrupt known signal with point spread

Corrupt known signal with point spread