eelbrain.load.fiff.mne_epochs(ds, tmin=-0.1, tmax=None, baseline=None, i_start='i_start', raw=None, drop_bad_chs=True, picks=None, reject=None, tstop=None, decim=1, **kwargs)

Load epochs as mne.Epochs.

  • ds (Dataset) – Dataset containing a variable which defines epoch cues (i_start).

  • tmin (scalar) – First sample to include in the epochs in seconds (Default is -0.1).

  • tmax (scalar) – Last sample to include in the epochs in seconds (Default 0.6; use tstop instead to specify index exclusive of last sample).

  • baseline ((float, float) | None) – Time interval for baseline correction. (tmin, tmax) tuple in seconds, or None to use all the data (e.g., (None, 0) uses all the data from the beginning of the epoch up to t = 0). Set to None for no baseline correction (default).

  • i_start (str) – name of the variable containing the index of the events.

  • raw (None | mne Raw) – If None,[‘raw’] is used.

  • drop_bad_chs (bool) – Drop all channels in[‘bads’] form the Epochs. This argument is ignored if the picks argument is specified.

  • picksmne.Epochs parameters.

  • rejectmne.Epochs parameters.

  • tstop (scalar) – Alternative to tmax. While tmax specifies the last samples to include, tstop specifies the sample before which to stop (standard Python indexing convention). For example, at 100 Hz the epoch with tmin=-0.1, tmax=0.4 will have 51 samples, while the epoch specified with tmin=-0.1, tstop=0.4 will have 50 samples.

  • ...mne.Epochs parameters.