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

arXiv: https://arxiv.org/abs/2208.14007 · HTML: https://mlsanity.com/paper/2208.14007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 26 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification](https://mlsanity.com/paper/2302.00789/) (2023)

## Abstract (arXiv)

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.