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.
@article{arxiv.2012.11105,
title = {Resting-state EEG sex classification using selected brain connectivity representation},
author = {Jean Li and Jeremiah D. Deng and Divya Adhia and Dirk de Ridder},
journal= {arXiv preprint arXiv:2012.11105},
year = {2020}
}
Comments
11 pages, 6 figures, book chapter to be published by Springer