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.

中文翻译：

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使用脑电图和眼动数据进行抑郁检测的改进分类模型。

目前，抑郁症已成为世界上主要的健康负担。但是，抑郁症的诊断存在很多问题，例如患者的配合不足，主观偏见和准确性低。因此，需要可靠和客观的评估方法来实现有效的抑郁症检测。脑电图（EEG）和眼动（EMs）数据因其易于记录和不侵入的优点而被广泛用于抑郁症检测。这项研究提出了一种基于内容的集成方法（CBEM）来提高抑郁症检测的准确性，讨论了静态和动态CBEM。在提出的模型中，通过实验将脑电图或电磁数据集划分为子集，然后使用多数投票策略确定受试者的标签。该方法的验证在包括自由观看眼动和静息状态脑电图的两个数据集上进行了验证，这两个数据集分别具有36,34个对象。对于这两个数据集，CBEM分别达到82.5％和92.65％的准确性。结果表明，CBEM优于传统分类方法。我们的发现为提高抑郁症鉴定的准确性提供了有效的解决方案，并为抑郁症的鉴定提供了有效的方法，将来可用于抑郁症的辅助诊断。