  Deep learning methods for person identification based on
electroencephalographic (EEG) brain activity encounters the problem of
exploiting the temporally correlated structures or recording session specific
variability within EEG. Furthermore, recent methods have mostly trained and
evaluated based on single session EEG data. We address this problem from an
invariant representation learning perspective. We propose an adversarial
inference approach to extend such deep learning models to learn
session-invariant person-discriminative representations that can provide
robustness in terms of longitudinal usability. Using adversarial learning
within a deep convolutional network, we empirically assess and show
improvements with our approach based on longitudinally collected EEG data for
person identification from half-second EEG epochs.
