Obesity is a common issue in modern societies today that can lead to various
diseases and significantly reduced quality of life. Currently, research has
been conducted to investigate resting state EEG (electroencephalogram) signals
with an aim to identify possible neurological characteristics associated with
obesity. In this study, we propose a deep learning-based framework to extract
the resting state EEG features for obese and lean subject classification.
Specifically, a novel variational autoencoder framework is employed to extract
subject-invariant features from the raw EEG signals, which are then classified
by a 1-D convolutional neural network. Comparing with conventional machine
learning and deep learning methods, we demonstrate the superiority of using VAE
for feature extraction, as reflected by the significantly improved
classification accuracies, better visualizations and reduced impurity measures
in the feature representations. Future work can be directed to gaining an
in-depth understanding regarding the spatial patterns that have been learned by
the proposed model from a neurological view, as well as improving the
interpretability of the proposed model by allowing it to uncover any
temporal-related information.

本文提出了一种基于深度学习的框架来从放松状态脑电图信号中提取肥胖和健康人群的特征，并使用时空变分自动编码器和一维卷积神经网络分类。结果显示，该方法在分类精度、特征可视化和特征表示的杂质度方面具有优势。未来的方向包括在从神经学的角度深入了解模型所学习的空间模式以及允许发掘任何时间相关信息来提高模型的可解释性。