BrainprintNet: A Multiscale Cross-Band Fusion Network for EEG-Based Brainprint Recognition

User identification technologies are essential for ensuring security and privacy. Compared to conventional biometric identification methods, electroencephalogram (EEG)-based brainprint recognition provides unique advantages, including non-replicability, resistance to coercion, and inherent liveness detection. However, existing EEG-based brainprint recognition methods are typically tailored for specific tasks and evaluated under conditions that differ substantially from real-world use. To overcome these limitations, we propose BrainprintNet, a convolutional neural network architecture integrating fine-grained filter banks, grouped multiscale temporal convolutions, and cross-band spatial fusion to enhance EEG-based brainprint recognition. BrainprintNet surpasses previous architectures in challenging scenarios involving simultaneous cross-session and cross-task recognition, demonstrating its generalization ability under strict simulation for real-world applications. Comprehensive experiments were conducted using three publicly available datasets encompassing nine distinct tasks. Furthermore, visualization of the learned network weights revealed strong correlations between user identity and specific EEG frequency subbands and channels. The proposed BrainprintNet significantly advances the accuracy, flexibility, and practical applicability of EEG-based brainprint recognition systems.