Show EOG artifact timing

Compute the distribution of timing for EOG artifacts.

# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt
import numpy as np

import mne
from mne import io
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()

Set parameters

meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"

# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
events = mne.find_events(raw, "STI 014")
eog_event_id = 512
eog_events = mne.preprocessing.find_eog_events(raw, eog_event_id)
raw.add_events(eog_events, "STI 014")

# Read epochs
picks = mne.pick_types(raw.info, meg=False, eeg=False, stim=True, eog=False)
tmin, tmax = -0.2, 0.5
event_ids = {"AudL": 1, "AudR": 2, "VisL": 3, "VisR": 4}
epochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks)

# Get the stim channel data
data = epochs.get_data(picks="STI 014").squeeze()
data = np.sum((data.astype(int) & eog_event_id) == eog_event_id, axis=0)

Plot EOG artifact distribution

fig, ax = plt.subplots(layout="constrained")
ax.stem(1e3 * epochs.times, data)
ax.set(xlabel="Times (ms)", ylabel=f"Blink counts (from {len(epochs)} trials)")

Estimated memory usage: 0 MB