Reduce EOG artifacts through regression

Reduce artifacts by regressing the EOG channels onto the rest of the channels and then subtracting the EOG signal.

This is a quick example to show the most basic application of the technique. See the tutorial for a more thorough explanation that demonstrates more advanced approaches.

# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

Import packages and load data

We begin as always by importing the necessary Python modules and loading some data, in this case the MNE sample dataset.

from matplotlib import pyplot as plt

import mne
from mne.datasets import sample
from mne.preprocessing import EOGRegression

print(__doc__)

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

# Read raw data
raw = mne.io.read_raw_fif(raw_fname, preload=True)
events = mne.find_events(raw, "STI 014")

# Highpass filter to eliminate slow drifts
raw.filter(0.3, None, picks="all")

Perform regression and remove EOG

# Fit the regression model
weights = EOGRegression().fit(raw)
raw_clean = weights.apply(raw, copy=True)

# Show the filter weights in a topomap
weights.plot()

Before/after comparison

Let’s compare the signal before and after cleaning with EOG regression. This is best visualized by extracting epochs and plotting the evoked potential.

tmin, tmax = -0.1, 0.5
event_id = {"visual/left": 3, "visual/right": 4}
evoked_before = mne.Epochs(
    raw, events, event_id, tmin, tmax, baseline=(tmin, 0)
).average()
evoked_after = mne.Epochs(
    raw_clean, events, event_id, tmin, tmax, baseline=(tmin, 0)
).average()

# Create epochs after EOG correction
epochs_after = mne.Epochs(raw_clean, events, event_id, tmin, tmax, baseline=(tmin, 0))
evoked_after = epochs_after.average()

fig, ax = plt.subplots(
    nrows=3, ncols=2, figsize=(10, 7), sharex=True, sharey="row", layout="constrained"
)
evoked_before.plot(axes=ax[:, 0], spatial_colors=True)
evoked_after.plot(axes=ax[:, 1], spatial_colors=True)
fig.suptitle("Before --> After")

Estimated memory usage: 0 MB