Electrooculography-Based Detection of Refractive Vision Problems

Early detection of visual impairments remains a persistent challenge, especially due to the subtle and often unnoticed nature of early-stage symptoms. Recent works have attempted to transition clinical tests to home-based services or develop innovative diagnostic methods, but most approaches remain self-initiated and discrete. In this study, we focused on refractive disorders and explored the feasibility of using electrooculography (EOG) to detect changes in refractive power passively. Thirty-nine participants used optometry trial lenses to simulate different refractive conditions. Participants performed a series of visual tasks while their EOG signals were recorded. We trained classification models to predict simulated refractive power levels relative to baseline visual condition across multiple evaluation settings, including within-subject, temporal generalization, and across-subject scenarios. The findings reveal that refractive power classification models achieve a mean accuracy of $0.950 \pm 0.034$ in within-subject, within-condition scenarios. Within-subject models tested on data from a different time point showed highly variable performance. While some participants achieved promising results, overall accuracy remained low, with a mean of $0.159 \pm 0.285$. We employed three strategies to evaluate the across-subject models. Naive models performed poorly ($0.161 \pm 0.063$) and linear normalization provided limited improvement ($0.175 \pm 0.062$). However, the fine-tuning strategy substantially improved the model's performance ($0.785 \pm 0.123$). EOG signals contain useful information for refractive power classification, particularly in personalized contexts. However, generalizing across time and individuals remains challenging. Overall, this work offers valuable insights for advancing EOG-based systems aimed at passive, real-time monitoring of visual conditions.