Finding neural signatures for obesity through feature selection on source-localized EEG
This work addresses obesity-related neurological evidence for females, but it is incremental as it applies a new method to a known bottleneck in EEG analysis.
The study tackled the problem of identifying neural signatures for obesity by developing a novel machine learning model to classify obese females using EEG-derived alpha band functional connectivity features, achieving a classification accuracy of 0.937.
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.