eelbrain.load.fiff.mne_epochs¶
-
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
.- Parameters
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, orNone
to use all the data (e.g.,(None, 0)
uses all the data from the beginning of the epoch up tot = 0
). Set toNone
for no baseline correction (default).i_start (str) – name of the variable containing the index of the events.
raw (None | mne Raw) – If None, ds.info[‘raw’] is used.
drop_bad_chs (bool) – Drop all channels in raw.info[‘bads’] form the Epochs. This argument is ignored if the picks argument is specified.
picks –
mne.Epochs
parameters.reject –
mne.Epochs
parameters.tstop (scalar) – Alternative to
tmax
: Whiletmax
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 withtmin=-0.1, tmax=0.4
will have 51 samples, while the epoch specified withtmin=-0.1, tstop=0.4
will have 50 samples... –
mne.Epochs
parameters.