Analyze Electrooculography (EOG)

This example can be referenced by citing the package.

This example shows how to use NeuroKit to analyze EOG data.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import neurokit2 as nk

# This "decorative" cell should be hidden from the docs once this is implemented:
# https://github.com/microsoft/vscode-jupyter/issues/1182 
plt.rcParams['figure.figsize'] = [15, 5]  # Bigger images
plt.rcParams['font.size']= 14

Explore the EOG signal

Let’s load the example dataset corresponding to a vertical EOG signal.

eog_signal = nk.data('eog_100hz.csv')

nk.signal_plot(eog_signal)

Let’s zoom in to some areas where clear blinks are present.

nk.signal_plot(eog_signal[100:1700])

Clean the signal

We can now filter the signal to remove some noise and trends.

eog_cleaned = nk.eog_clean(eog_signal, sampling_rate=100, method='neurokit')

Let’s visualize the same chunk and compare the clean version with the original signal.

nk.signal_plot([eog_signal[100:1700], eog_cleaned[100:1700]], 
               labels=["Raw Signal", "Cleaned Signal"])

Detect and visualize eye blinks

We will nor run a peak detection algorithm to detect peaks location.

blinks = nk.eog_findpeaks(eog_cleaned, sampling_rate=100, method="mne")
blinks

array([  277,   430,   562,   688,   952,  1378,  1520,  1752,  3353,
        3783,  3928,  4031,  4168,  4350,  4723,  4878,  5213,  5365,
        5699,  5904,  6312,  6539,  6714,  7224,  7382,  7553,  7827,
        8014,  8154,  8312,  8626,  8702,  9140,  9425,  9741,  9948,
       10142, 10230, 10368, 10708, 10965, 11256, 11683, 11775])

events = nk.epochs_create(eog_cleaned, blinks, sampling_rate=100, epochs_start=-0.3, epochs_end=0.7)

data = nk.epochs_to_array(events)  # Convert to 2D array
data = nk.standardize(data)  # Rescale so that all the blinks are on the same scale

# Plot with their median (used here as a robust average)  
plt.plot(data, linewidth=0.4)
plt.plot(np.median(data, axis=1), linewidth=3, linestyle='--', color="black")

[<matplotlib.lines.Line2D at 0x7f886e5aa710>]