基于脑电的生成式抑郁症判别器
EEG Based Generative Depression Discriminator
AI总结：
本文提出基于EEG信号的生成式检测网络（GDN），通过训练两个生成器学习抑郁症与对照组脑活动特征并比较信号一致性来判别抑郁症，在MODMA和HUSM数据集上分别达到92.30%和86.73%准确率，且能输出可解释信息辅助发现误判。
摘要：
抑郁症是一种非常常见但严重的情绪障碍。本文中，我们根据三条生理学规律构建了一个生成式检测网络（GDN）。我们的目标是期望神经网络基于EEG信号学习相关的大脑活动，同时基于大脑活动重新生成目标电极信号。我们训练了两个生成器，第一个学习抑郁症大脑活动的特征，第二个学习对照组大脑活动的特征。在测试中，一段EEG信号被分别输入两个生成器，如果EEG信号与大脑活动之间的关系符合某一类别的特征，那么相应类别的生成器生成的信号与原始信号更一致。因此可以判定某段EEG信号对应的类别。我们在MODMA数据集上获得了92.30%的准确率，在HUSM数据集上获得了86.73%的准确率。此外，该模型能够输出可解释的信息，可用于帮助用户发现网络可能的误判。我们的代码将会发布。
英文摘要：
Depression is a very common but serious mood disorder.In this paper, We built a generative detection network(GDN) in accordance with three physiological laws. Our aim is that we expect the neural network to learn the relevant brain activity based on the EEG signal and, at the same time, to regenerate the target electrode signal based on the brain activity. We trained two generators, the first one learns the characteristics of depressed brain activity, and the second one learns the characteristics of control group's brain activity. In the test, a segment of EEG signal was put into the two generators separately, if the relationship between the EEG signal and brain activity conforms to the characteristics of a certain category, then the signal generated by the generator of the corresponding category is more consistent with the original signal. Thus it is possible to determine the category corresponding to a certain segment of EEG signal. We obtained an accuracy of 92.30\% on the MODMA dataset and 86.73\% on the HUSM dataset. Moreover, this model is able to output explainable information, which can be used to help the user to discover possible misjudgments of the network.Our code will be released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
