EEG Based Generative Depression Discriminator
Ziming Mao Hao wu Yongxi Tan Yuhe Jin
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.