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, name=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 : tuple(tmin, tmax) | None

Time interval for baseline correction. Tmin/tmax 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). baseline=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.

picks, reject

mne.Epochs parameters.

tstop : scalar

Alternative to tmax: While tmax specifies the last samples to include, tstop can be used to specify the epoch time excluding the last time point (i.e., standard Python/Eelbrain 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.