Toward Depression Recognition Using EEG and Eye Tracking: An Ensemble Classification Model CBEM

Depression, influencing millions of people, has become a major disease in the past decade. However, the assessment methods of diagnosing depression almost exclusively rely on patient-reported or clinical judgments of symptom severity, which are associated with subjective biases and intensive labor. Some bio-signals such as EEG and eye movements are used for automatic detection but their accuracies are not accurate enough for the real application, further improvements are needed. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, generating data subsets by the content of the experiment, then using the majority vote of subsets to determine the subjects' label. The validation of the method is testified by two different experiments which included free viewing eye tracking and task-state EEG and these two experiments have 36, 40 subjects respectively. In these two experiments CBEM gains accuracies of 82.5% and 92.73% respectively. The results show that CBEM outperform traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and give an objective and quantitative evaluation of depression, which in the future could be used for the auxiliary diagnosis of depression.