Resting-state EEG sex classification using selected brain connectivity representation
This research addresses the problem of identifying sex-specific differences in EEG signals, which could be beneficial for clinical applications that currently treat EEG analysis as sex-neutral.
This paper explores sex effects on resting-state EEG signals using a machine learning approach. It confirms the generality of these effects by successfully predicting sex based on brain connectivity represented by coherence between specific EEG sensor channels.
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.