eelbrain.pipeline.ContinuousEpoch

class eelbrain.pipeline.ContinuousEpoch(task=None, sel=None, pad_start=0.1, pad_end=1.0, split=10, samplingrate=None)

Epoch spanning multiple events for continuous analysis

A ContinuousEpoch will extract a continuous segment of data from the first event to the last event. pad_start and pad_stop determine how much extra time to include before the first event and after the last event (to allow using the data surrounding these events for estimating TRFs with negative and positive lags). split controls whether to break up the data into multiple segments when there are long pauses between successive events.

When using Pipeline.load_epochs(), each row of the returned Dataset will contain the events in the epoch alongside the data.

Parameters:

  • task (str) – Task from which to load data. Can be omitted if the experiment has only a single task.

  • sel (str) – Expression which evaluates in the events Dataset to the index of the events included in this Epoch specification (default is all events).

  • pad_start (float) – Time to add before the first event (in seconds, default 0.100).

  • pad_end (float) – Time to add after the last event (in seconds, default 1).

  • split (float) – Split into several continuous epochs whenever time between used data (event times ± pad) is larger than split (default 10). For example, in an experiment with many 2 s long trials which are grouped into 2 blocks with a break of 50 s, this would result in two epochs, one for each block.

  • samplingrate (float) – Target samplingrate. Needs to divide data samplingrate evenly (e.g. 200 for data sampled at 1000 Hz; by default, use the raw data samplingrate).