Can Differentiable Structure Learning Enable Low-Channel Brainprint Authentication on Resource-Constrained IoT Devices?

High-precision Brain-Computer Interface (BCI) systems typically require dense multi-channel EEG acquisition, which conflicts with the low-power and low-channel constraints of IoT edge devices. Existing channel selection methods also lack end-to-end optimization and structural interpretability. To address these issues, we propose the Dynamic Channel Learning Transformer (DCLT), which formulates EEG channel selection as Differentiable Structure Learning for IoT-based brainprint recognition. DCLT introduces an STE-driven Top-K discrete gating mechanism to jointly optimize physical channel constraints and gradient-based learning, followed by Transformer-based global sparse representation modeling. Furthermore, Dynamic-Static Consistency Analysis is proposed to evaluate structural stability and deployment feasibility. Experiments on three EEG datasets show that DCLT maintains over 90% recognition accuracy with only 25% channels retained, while real-world edge evaluation on 23 subjects demonstrates significant reductions in computational cost and inference latency.