The rise in population and aging has led to a significant increase in the number of individuals affected by common causes of vision loss. Early diagnosis and treatment are crucial to avoid the consequences of visual impairment. However, in early stages, many visual problems are making it difficult to detect. Visual adaptation can compensate for several visual deficits with adaptive eye movements. These adaptive eye movements may serve as indicators of vision loss. In this work, we investigate the association between eye movement and blurred vision. By using Electrooculography (EOG) to record eye movements, we propose a new tracking model to identify the deterioration of refractive power. We verify the technical feasibility of this method by designing a blurred vision simulation experiment. Six sets of prescription lenses and a pair of flat lenses were used to create different levels of blurring effects. We analyzed binocular movements through EOG signals and performed a seven-class classification using the ResNet18 architecture. The results revealed an average classification accuracy of 94.7% in the subject-dependent model. However, the subject-independent model presented poor performance, with the highest accuracy reaching only 34.5%. Therefore, the potential of an EOG-based visual quality monitoring system is proven. Furthermore, our experimental design provides a novel approach to assessing blurred vision.