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)[source]#

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.

Examples using `mne.simulation.simulate_evoked`#

Source localization with equivalent current dipole (ECD) fit

Corrupt known signal with point spread

Generate simulated evoked data

Cortical Signal Suppression (CSS) for removal of cortical signals

mne.simulation.simulate_evoked#

Examples using mne.simulation.simulate_evoked#

Examples using `mne.simulation.simulate_evoked`#