Show EOG artifact timingΒΆ

Compute the distribution of timing for EOG artifacts.

# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)


import numpy as np
import matplotlib.pyplot as plt

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

print(__doc__)

data_path = sample.data_path()

Set parameters

raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

# Setup for reading the raw data
raw = io.Raw(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
pick_ch = mne.pick_channels(epochs.ch_names, ['STI 014'])[0]
data = epochs.get_data()[:, pick_ch, :].astype(int)
data = np.sum((data.astype(int) & 512) == 512, axis=0)

Plot EOG artifact distribution

plt.stem(1e3 * epochs.times, data)
plt.xlabel('Times (ms)')
plt.ylabel('Blink counts (from %s trials)' % len(epochs))
plt.show()
../../_images/sphx_glr_plot_eog_artifact_histogram_001.png

Total running time of the script: (0 minutes 0.485 seconds)

Download Python source code: plot_eog_artifact_histogram.py