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.

翻译：肥胖是当今现代社会的普遍问题，可能导致多种疾病并显著降低生活质量。目前，已有研究探索静息态脑电图（electroencephalogram, EEG）信号，旨在识别与肥胖相关的潜在神经学特征。本研究提出一种基于深度学习的框架，用于提取静息态EEG特征以区分肥胖和瘦削受试者。具体而言，采用一种新颖的变分自编码器框架从原始EEG信号中提取受试者不变特征，随后通过一维卷积神经网络进行分类。与传统机器学习和深度学习方法相比，我们证明了使用VAE进行特征提取的优越性，这体现在显著提升的分类准确率、更优的可视化效果以及特征表示中杂质度量的降低。未来工作可侧重于从神经学视角深入理解所提模型学习到的空间模式，并通过使模型能够揭示时间相关信息来增强其可解释性。