eelbrain.datasets.get_mne_sample(tmin=-0.1, tmax=0.4, baseline=(None, 0), sns=False, src=None, sub="modality=='A'", ori='free', snr=2, method='dSPM', rm=False, stc=False, hpf=0)

Load events and epochs from the MNE sample data

tmin : scalar

Relative time of the first sample of the epoch.

tmax : scalar

Relative time of the last sample of the epoch.

baseline : {None, tuple of 2 {scalar, None}}

Period for baseline correction.

sns : bool | str

Add sensor space data as NDVar as ds['meg'] (default False). Set to 'grad' to load gradiometer data.

src : False | ‘ico’ | ‘vol’

Add source space data as NDVar as ds['src'] (default False).

sub : str | list | None

Expression for subset of events to load. For a very small dataset use e.g. [0,1].

ori : ‘free’ | ‘fixed’ | ‘vector’

Orientation of sources.

snr : scalar

MNE inverse parameter.

method : str

MNE inverse parameter.

rm : bool

Pretend to be a repeated measures dataset (adds ‘subject’ variable).

stc : bool

Add mne SourceEstimate for source space data as ds['stc'] (default False).

hpf : scalar

High pass filter cutoff.

ds : Dataset

Dataset with epochs from the MNE sample dataset in ds['epochs'].