An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data.

At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.