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

Return type