eelbrain.load.mne.variable_length_mne_epochs

eelbrain.load.mne.variable_length_mne_epochs(events, tmin, tmax=None, baseline=None, allow_truncation=False, tstop=None, picks=None, decim=1, **kwargs)

Load mne Epochs where each epoch has a different length

Parameters:

  • events (Dataset) – Dataset containing events and an mne.io.Raw data object, as returned by eelbrain.load.mne.events().

  • tmin (float | Sequence[float] | str) – First sample to include in each epoch in seconds, relative to event time. Can be str referencing a variable in events.

  • tmax (float | Sequence[float] | str) – Last sample to include in each epoch in seconds, relative to event time. Can be str referencing a variable in events.

  • baseline (Tuple[float | None, float | None] | 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).

  • allow_truncation (bool) – If a tmin or tmax value falls outside the data available in raw, automatically truncate the epoch (by default this raises a ValueError).

  • tstop (float | Sequence[float]) – 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.

  • picks (Any)

  • decim (int)

Return type:

List[Epochs]