Research On Depression Recognition Based On Eye Movement Feature And EEG Feature

Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying eye movement features and EEG features during free viewing task, an accuracy of 99.1%, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate mild depressed and non-depressed subjects by EEG signals. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Besides, an accuracy of 79.3% was achived though Random Forest in classification of eye movement data. Though the performance is lower than EEG analysis, but it is still a satisfying result for the potential of eye movement data. We believe that by adding new features and trying different machine learning methods, data from gaze can be applied to depression detection for the small scale of data and its easy acquisition. Combined with wearable EEG collecting devices and low cost eye tracking devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time.