Eye Movement Attention Based Depression Detection Model

• Published in: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) pp. 1 - 2
• Main Authors: Zhao, Ju, Wang, Qingxiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2022
• Subjects: Analytical models; Attention; Computational modeling; Data science; Depression; Depression Detection; Eye Movement; Feature extraction; Fluctuations; Mood
• DOI: 10.1109/DSAA54385.2022.10032433

Depression is a common mental illness. Unlike normal mood fluctuations which affect individuals only temporarily, depressed episodes can profoundly disrupt a person's daily life and even lead to suicide. Eye movement data are commonly employed in depression identification because they are simple to collect and can show psychological processes. Given the above, we proposed EnSA, a novel model based on eye movement data. We established forward and reverse target stimuli to identify the subject's saccade reaction and capture the subject's eye movement data. The gathered eye movement data were entered into EnSA first, and the self-attention weights of each characteristic were calculated. To produce more expressive features, the self-attention features were convolved and then summed feature outputs. According to the results, our model performed well, with an accuracy of 93.5% and 95.5% in the prosaccade and antisaccade experiments, respectively.