EEG Based Generative Depression Discriminator

TL;DR

This paper introduces a generative neural network model that classifies depression using EEG signals, achieving over 86% accuracy and providing explainable outputs to assist in diagnosis.

Contribution

The paper presents a novel generative detection network that learns and regenerates EEG features for depression classification, incorporating physiological laws for improved interpretability.

Findings

Achieved 92.30% accuracy on MODMA dataset

Achieved 86.73% accuracy on HUSM dataset

Provides explainable information to aid diagnosis

Abstract

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.