Extract Data Epochs

Epoch the continuous tutorial dataset around the square stimulus events, remove the pre-stimulus baseline, cut a shorter sub-epoch window, then select epochs by index and by event field.

Everything runs headless with no GUI window: every pop_* call below passes explicit arguments, so the dialogs never open.

Load the continuous tutorial dataset.

from pathlib import Path

import matplotlib

matplotlib.use("Agg")

import eegprep
from eegprep import (
    eeg_checkset,
    pop_epoch,
    pop_loadset,
    pop_rmbase,
    pop_select,
    pop_selectevent,
)

REPO_ROOT = Path(eegprep.__file__).resolve().parents[2]  # sphinx-gallery defines no __file__
input_file = REPO_ROOT / "sample_data" / "eeglab_data.set"

EEG = pop_loadset(input_file)
print("continuous:", EEG["data"].shape, "trials:", EEG["trials"])
print("event types:", sorted({str(event["type"]) for event in EEG["event"]}))

continuous: (32, 30504) trials: 1
event types: ['rt', 'square']

Extract epochs time locked to the square events (Tools > Extract epochs). pop_epoch returns the epoched dataset plus the history command the GUI and console record.

EEG, epoch_com = pop_epoch(EEG, ["square"], [-1, 2], newname="Square epochs", return_com=True)
print("epoched:", EEG["data"].shape, "trials:", EEG["trials"])
print("epoch range (s):", EEG["xmin"], EEG["xmax"])
print(epoch_com)

epoched: (32, 384, 80) trials: 80
epoch range (s): -1.0 1.9921875
EEG = pop_epoch( EEG, { 'square' }, [-1 2], 'newname', 'Square epochs');

Remove the pre-stimulus baseline (Tools > Remove epoch baseline). The range uses the units of EEG["times"], milliseconds for epoched data.

EEG, rmbase_com = pop_rmbase(EEG, [-200, 0], return_com=True)
print(rmbase_com)

EEG = pop_rmbase( EEG, [-200 0], []);

Cut a shorter sub-epoch window, -500 ms to 1000 ms, with pop_select (Edit > Select data). The time option is in seconds.

EEG, select_time_com = pop_select(EEG, time=[-0.5, 1.0], return_com=True)
print("sub-epoch:", EEG["data"].shape, "range (ms):", EEG["times"][0], EEG["times"][-1])
print(select_time_com)

sub-epoch: (32, 193, 80) range (ms): -500.0 1000.0
EEG = pop_select( EEG, 'time', [-0.5 1]);

Select epochs by index. Epoch selectors are EEGLAB-facing 1-based indices, so trial=[1, ..., 10] keeps the first ten epochs.

EEG_first10, select_trial_com = pop_select(EEG, trial=list(range(1, 11)), return_com=True)
print("first 10 epochs:", EEG_first10["data"].shape)
print(select_trial_com)

first 10 epochs: (32, 193, 10)
EEG = pop_select( EEG, 'trial', [1 2 3 4 5 6 7 8 9 10]);

Select epochs by event field (Edit > Select epochs or events). Keep the square events with position == 1 and drop epochs that no selected event refers to.

EEG_pos1, selectevent_com = pop_selectevent(
    EEG,
    type=["square"],
    position=1,
    deleteepochs="on",
    deleteevents="off",
    return_com=True,
)
EEG_pos1 = eeg_checkset(EEG_pos1)
print("position 1 epochs:", EEG_pos1["data"].shape, "trials:", EEG_pos1["trials"])
print(selectevent_com)

position 1 epochs: (32, 193, 21) trials: 21
EEG = pop_selectevent( EEG, 'type', {'square'}, 'position', 1, 'deleteepochs', 'on', 'deleteevents', 'off');