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This paper introduces Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative to conventional neural networks for real-time EEG classification on edge devices, achieving competitive performance with significantly lower latency and model size.
Introduces ERP-XTTN, a cross-attention architecture for interpretable ERP classification across subjects without calibration. Evaluated on multiple datasets, it achieves competitive performance with black-box models while providing transparent routing insights.