  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.
