Resting-state EEG sex classification using selected brain connectivity representation

TL;DR

This study demonstrates that resting-state EEG signals, particularly coherence between specific sensor channels, can reliably predict sex, highlighting the importance of sex-specific analysis in EEG-based clinical applications.

Contribution

The paper introduces a machine learning approach that confirms sex-related differences in EEG signals using brain connectivity measures, emphasizing their predictive power.

Findings

EEG coherence between certain sensors predicts sex accurately

Sex effects on EEG are generalizable across subjects

Connectivity features outperform other EEG features in sex classification

Abstract

Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex prediction of resting-state EEG signals. We have found that the brain connectivity represented by the coherence between certain sensor channels are good predictors of sex.