mne.simulation.simulate_evoked

mne.simulation.simulate_evoked(fwd, stc, info, cov=None, nave=30, iir_filter=None, random_state=None, use_cps=True, verbose=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 version 0.15.0.

iir_filterNone | array

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

random_stateNone | int | instance of RandomState

A 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.

use_cpsbool

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

New in version 0.15.

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.

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 version 0.10.0.

Examples using mne.simulation.simulate_evoked

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

Generate simulated evoked data

Generate simulated evoked data

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

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