Finding neural signatures for obesity through feature selection on source-localized EEG

TL;DR

This study introduces a machine learning approach to identify neural signatures of obesity using EEG data, achieving high classification accuracy and revealing dysfunctional brain networks in obese females.

Contribution

A novel machine learning model utilizing alpha band functional connectivity features from EEG data to identify obesity-related brain network signatures.

Findings

Classification accuracy of 93.7% in distinguishing obese females

Obese brain networks show impairment in self-referential and environmental processing areas

Highlights dysfunctional neural connectivity associated with obesity

Abstract

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.