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Isolating Eye-Movement Artifacts from EEG Signals.

Christian O'Reilly¹, Scott Huberty²

¹Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29201, USA.

International Journal of Neural Systems

|May 23, 2026

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This study introduces a new method combining deep learning and biophysical modeling with eye-tracking to accurately remove eye-movement artifacts from electroencephalogram (EEG) data, ensuring neural activity remains intact.

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Electroencephalogram (EEG) is crucial for measuring brain activity but often suffers from eye-movement artifacts.
Current artifact removal methods like Independent Component Analysis (ICA) risk removing genuine neural signals.
Accurate artifact removal is vital to prevent bias in EEG analysis.

Purpose of the Study:

To develop and validate a novel framework for deconfounding eye-movement artifacts from EEG data.
To differentiate between eye-movement-related artifacts and neural activity.
To enhance the reliability of EEG analysis by ensuring neurogenic activity is preserved.

Main Methods:

Developed complementary deep learning and biophysical modeling approaches.

Keywords:

Electroencephalogram deep learning electrooculogram eye tracking independent component analysis machine learning modeling

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Leveraged eye-tracking (ET) data to inform artifact removal.

Trained a deep learning model to predict EEG components from ET data.

Utilized a realistic head model to simulate eye-movement artifacts.

Main Results:

Successfully distinguished neural and non-neural correlates of eye movements.
Enabled the separation of eye-movement artifacts from non-artifactual neural activity.
Provided a framework to evaluate the sensitivity and specificity of artifact removal techniques.

Conclusions:

The developed ET-informed framework accurately removes eye-movement artifacts without compromising neural data.
This approach offers a robust solution to a significant challenge in EEG analysis.
Paves the way for more refined artifact removal strategies and future research in disentangling neural components.