Steady-State Visual-Evoked EEG Dataset for Visual Function Assessment via Biomarker Extraction

According to the data from the World Health Organization, patients with visual function disorders, such as amblyopia and intermittent exotropia, have increased significantly and become an important factor contributing to the global burden of disease.Currently, existing research has found that many human visual function disorders can be reflected in abnormal brain activities without ocular damage. The main clinical methods currently rely on subjective psychophysical tests and ophthalmological examinations, and the detection and utilization of neural signals are insufficient. Moreover, when assessing patients with language expression disorders, including infants, or uncooperative subjects, these methods pose considerable challenges, thereby complicating the implementation of vision assessments and indicating certain limitations of traditional methods.Therefore, this paper introduces a steady-state visual-evoked EEG (S2VEEG) dataset, which includes 60 amblyopia patients, 14 intermittent exotropia patients, and 30 individuals with normal visual function. These patients were carefully diagnosed and selected by professional ophthalmologists in the hospital. A 64-channel EEG cap was used to collect the EEG data records of the subjects under steady-state visual stimulation. These physiological data can be used to explore new possible physiological indicators of visual function and for visual function assessment using emerging artificial intelligence technologies, thus promoting the development of the visual function disorder detection field.The paper extracts differential entropy and small-worldness property as features and uses SVM,RGCB and EmT classifiers for training. The results show that the recognition accuracy among amblyopia patients, intermittent exotropia patients, and individuals with normal visual function reaches 95.38%. This classification performance indicates significant differences among patients with different degrees of visual function disorders, demonstrating the good usability of the S2VEEG dataset and verifying the potential of small-worldness property as a biomarker for visual function disorder detection.