论文标题
通过选择源元素的特征选择来寻找肥胖的神经特征
Finding neural signatures for obesity through feature selection on source-localized EEG
论文作者
论文摘要
肥胖是现代社会中的一个严重问题，通常与显着降低生活质量有关。目前进行的研究是为了探索与肥胖相关的神经系统证据，使用脑电图（EEG）数据仅限于传统方法。在这项研究中，我们开发了一种新型的机器学习模型，以使用来自EEG数据的Alpha带功能连接功能来识别肥胖女性的大脑网络。总体分类精度为0.937。我们的发现表明，肥胖大脑的特征是一个功能失调的网络，在该网络中，负责处理自指信息和环境环境信息的领域受损。
Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.937 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that responsible for processing self-referential information and environmental context information are impaired.
