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.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
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)

Out:

Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Current compensation grade : 0
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
319 events found
Events id: [ 1  2  3  4  5 32]
EOG channel index for this subject is: [375]
Filtering the data to remove DC offset to help distinguish blinks from saccades
Setting up band-pass filter from 2 - 45 Hz
Setting up band-pass filter from 1 - 10 Hz
Now detecting blinks and generating corresponding events
Number of EOG events detected : 46
288 matching events found
4 projection items activated
Loading data for 288 events and 106 original time points ...
0 bad epochs dropped

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 0.952 seconds)

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