  Brain biometrics based on electroencephalography (EEG) have been used
increasingly for personal identification. Traditional machine learning
techniques as well as modern day deep learning methods have been applied with
promising results. In this paper we present EEG-BBNet, a hybrid network which
integrates convolutional neural networks (CNN) with graph convolutional neural
networks (GCNN). The benefit of the CNN in automatic feature extraction and the
capability of GCNN in learning connectivity between EEG electrodes through
graph representation are jointly exploited. We examine various connectivity
measures, namely the Euclidean distance, Pearson's correlation coefficient,
phase-locked value, phase-lag index, and Rho index. The performance of the
proposed method is assessed on a benchmark dataset consisting of various
brain-computer interface (BCI) tasks and compared to other state-of-the-art
approaches. We found that our models outperform all baselines in the
event-related potential (ERP) task with an average correct recognition rates up
to 99.26% using intra-session data. EEG-BBNet with Pearson's correlation and
RHO index provide the best classification results. In addition, our model
demonstrates greater adaptability using inter-session and inter-task data. We
also investigate the practicality of our proposed model with smaller number of
electrodes. Electrode placements over the frontal lobe region appears to be
most appropriate with minimal lost in performance.
