pnpl.preprocessing.Epoch

pnpl.preprocessing.Epoch#

class pnpl.preprocessing.Epoch(step_name='epo', event_id=None, tmin=-0.05, tmax=0.95, stim_channel='STI101', min_duration=0.005, baseline=None)[source]#

Create epochs from continuous data.

This step is typically the last in a pipeline for epoched datasets.

Parameters:

  • event_id (Dict[str, int] | None) – Event ID dictionary (e.g., {‘digit/zero’: 10, …})

  • tmin (float) – Start time relative to event (default: -0.05)

  • tmax (float) – End time relative to event (default: 0.95)

  • stim_channel (str) – Stimulus channel name (default: ‘STI101’)

  • min_duration (float) – Minimum event duration (default: 0.005)

  • baseline (tuple | None) – Baseline correction interval (default: None)

  • step_name (str)

__init__(step_name='epo', event_id=None, tmin=-0.05, tmax=0.95, stim_channel='STI101', min_duration=0.005, baseline=None)#
Parameters:

  • step_name (str)

  • event_id (Dict[str, int] | None)

  • tmin (float)

  • tmax (float)

  • stim_channel (str)

  • min_duration (float)

  • baseline (tuple | None)

Return type:

None

Methods

__init__([step_name, event_id, tmin, tmax, ...])

apply(raw, context)

Note: This step modifies context to include epochs.

Attributes

baseline

event_id

min_duration

step_name

stim_channel

tmax

tmin