| Paper ID | ASPS-5.1 |
| Paper Title |
DEPRESSION DETECTION BY ANALYSING EYE MOVEMENTS ON EMOTIONAL IMAGES |
| Authors |
Ruizhe Shen, Qi Zhan, Yu Wang, Tsinghua University, China; Huimin Ma, University of Science and Technology Beijing, China |
| Session | ASPS-5: Audio & Images |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Emerging Topics [OTH-EMRG] |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
To achieve an objective and efficient depression detection system, we propose a cognitive psychology experimental paradigm based on the attentional bias theory and eye movements in this paper. We select images of three different emotions (positive, neutral, and negative) as experimental stimulus. Comparing with the traditional free viewing paradigm, the paradigm we proposed adds a stage of frame tracking to analyse the process of attention disengagement. Based on extracted psychological features from eye movement data, we train a mental state classifier of Support Vector Machine to classify people with depression and normal controls, and the model achieves 77.0% of accuracy, which achieve state-of-the-art under the same data condition. Our model is interpretable and our results demonstrate the theory of attention bias. |
