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.
@article{arxiv.2208.14007,
title = {Finding neural signatures for obesity through feature selection on source-localized EEG},
author = {Yuan Yue and Dirk De Ridder and Patrick Manning and Samantha Ross and Jeremiah D. Deng},
journal= {arXiv preprint arXiv:2208.14007},
year = {2023}
}