DRFNet: Enhancing Identity Discriminability and Feature Robustness for Cross-Session VEP-Based EEG Biometrics

Biometric recognition using visually evoked potentials (VEPs), a type of neural response to visual stimuli recorded via electroencephalography (EEG), has shown great promise. However, the non-stationary nature of EEG signals poses a major challenge in cross-session scenarios, where data collected on different days often leads to performance degradation. To address this, we propose the Discriminative Robust Feature Network (DRFNet) to enhance the robustness and inter-subject discriminability of identity representations across sessions. DRFNet incorporates two key components: (1) A log-power transformation that amplifies inter-individual differences by capturing non-linear energy patterns from VEP features via signal squaring and logarithmic scaling; and (2) A hierarchical normalization strategy with adaptive attention to balance discriminative identity cues with inter-session invariance by stabilizing feature distributions across multiple levels (feature map, batch, and sample). On two public multi-session SSVEP datasets (Dataset A: 30 subjects, 6 s trials; Dataset B: 54 subjects, 4 s trials), our model outperformed state-of-the-art methods, achieving identification accuracies of 92.92% and 86.30%, and equal error rates of 3.92% and 4.09%, respectively. Further analysis demonstrates that filter bank processing and a reduced set of parietal-occipital electrodes can provide more discriminative features while offering a practical path toward system lightweighting.