ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Brain Health and Clinical Neuroscience
Feasibility of EEG-Based Machine Learning for the Objective Assessment of Non-Strabismic Binocular Vision Dysfunction
Zhili Lu 1
Xin Zuo 2
Qixuan Zhang 3,2
Yue Liu 1
Xiang Ma 1
Chi Zhang 2
1. The First Affiliated Hospital of Dalian Medical University, Dalian, China
2. Dalian University of Technology Faculty of Medicine, Dalian, China
3. University of Jyväskylä, Jyväskylä, Finland
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Abstract
With the increasing prevalence of prolonged near-work, Non-Strabismic Binocular Vision Dysfunction (NSBVD) has become a growing concern. Current diagnostic methods primarily rely on subjective symptoms and time-consuming examinations, highlighting the need for objective physiological markers. This pilot study explores the feasibility of utilizing electroencephalography (EEG) combined with machine learning as an objective, auxiliary approach for NSBVD assessment. We analyzed EEG activity in 15 NSBVD patients and 15 healthy controls during a natural viewing vergence task. Time-frequency and topographic analyses were used to identify neural features associated with vergence insufficiency. The groups exhibited distinct neural patterns. Healthy controls showed strong activation in visual areas, whereas NSBVD patients displayed reduced activity coupled with compensatory increased frontal activity, particularly in the theta and alpha bands. A linear Support Vector Machine (SVM) trained on these features achieved 76.67% accuracy (AUC = 0.87). These findings suggest that specific neural patterns are potential biomarkers for binocular dysfunction. This study demonstrates the feasibility of objective screening, though validation in larger cohorts is needed for clinical use.
Summary
Keywords
EEG, machine learning, non-strabismic binocular vision dysfunction, objective screening, vergence
Received
04 January 2026
Accepted
18 February 2026
Copyright
© 2026 Lu, Zuo, Zhang, Liu, Ma and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xiang Ma; Chi Zhang
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