DARN: A Dual Attention Refinement Network for Enhancing Feature Robustness in VEP-Based EEG Biometrics

Visual evoked potential (VEP)-based EEG biometrics provide a secure, spoof-resistant approach for identification and authentication; however, cross-session variability, driven by temporal fluctuations in neural responses, often undermines feature stability and degrades performance. To tackle this, we propose the Dual Attention Refinement Network (DARN), a novel method that enhances the spatiotemporal consistency of EEG representations without requiring frequent retraining. DARN combines a lightweight CNN backbone with two complementary attention modules: the Spatial Feature Refinement Unit (SFRU), which prioritizes consistent spatial patterns, and the Inter-channel Refinement Unit (ICRU), which captures stable inter-channel dependencies, jointly refining the spatial and channel dimensions of extracted EEG feature maps. Evaluated on two public multi-session VEP datasets with 30 and 54 subjects, with sample durations of 6 seconds for the 30-class dataset and 4 seconds for the 54-class dataset, DARN surpasses state-of-the-art baselines, achieving identification accuracies of 93.83% (30 classes) and 84.55% (54 classes), and authentication equal error rates of 3.05% and 3.85%, respectively. Moreover, our analysis highlights the pivotal role of visual stimulus diversity in improving cross-session generalization, offering practical insights for designing robust VEP-based biometric systems. The source code is available at https://github.com/Ultramua/DARN.