Adversarial Deep Learning in EEG Biometrics
This work addresses the challenge of longitudinal usability in EEG biometrics for person identification, representing an incremental improvement over existing methods.
The paper tackled the problem of session-specific variability in EEG-based person identification by proposing an adversarial inference approach to learn session-invariant representations, resulting in improved robustness for longitudinal usability with empirical assessments on half-second EEG epochs.
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.