Gender Classification Of Eeg Signals Using A Motif Attribute Classification Ensemble

Effective analysis of EEG signals remains a challenging task. So far, the analysis and conditioning of EEG have largely remained gender-neutral. This paper explores the evidence of gender effects on EEG signals and confirms the generality of these effects by achieving successful gender prediction through EEG signals. Specifically, we propose a novel statistical feature representation that captures the gender discrepancy, and design a customized classification ensemble framework to overcome the non-stationary characteristics in EEG signals, utilizing findings obtained through several machine learning techniques including clustering, visualization, and metric learning. Apart from gender differentiation, the age effect on EEG gender patterns is also revealed.