A Lightweight CNN–Transformer Framework for EEG Seizure Detection with Ablation, Interpretability, and Noise-Robustness Analysis

This study presents a lightweight two-stage hybrid CNN–Transformer architecture for EEG-based seizure detection that replaces recurrent intermediate layers used in conventional hybrid models with a direct CNN-to-Transformer pipeline, reducing model complexity to 1.2 million parameters while enhancing classification performance. The model is evaluated through component-wise ablation, attention-based interpretability analysis, Layer-wise Relevance Propagation (LRP), and noise-robustness assessment under a uniform experimental protocol on two benchmark EEG datasets, with results reported consistently across both. The framework achieves 99.12% accuracy on Bonn EEG and 98.45% on CHB-MIT, outperforming reimplemented baselines under identical preprocessing and evaluation procedures; comparison against externally reported foundation models is included for context only, given sub-1% margins that fall within approximate confidence bounds. Ablation confirms a statistically supported 1.55-percentage-point gain over CNN+LSTM. Attention visualization highlights clinically relevant central channels (Cz, C3, C4, Pz), and LRP indicates the Transformer encoder contributes 60–75% of model relevance. The model maintains 97.2% accuracy under additive Gaussian noise at 5 dB SNR, a 7.7-point improvement over CNN+LSTM; this characterizes noise robustness rather than adversarial robustness in the formal sense, as no gradient-based or bounded-norm perturbations were evaluated. Results show HyConT achieved 96.12% ± 3.82% accuracy under LOSO-CV, confirming that Transformer-based long-range temporal modeling generalizes across patients. The ∼2.3 percentage-point degradation from segment-level (98.45%) to subject-level (96.12%) is expected and consistent with cross-dataset transfer, as both measure performance under distribution shift. These analyses support the architecture's efficiency, interpretability, and noise resilience as a basis for further development toward real-time EEG monitoring, with hardware-level validation and larger heterogeneous datasets identified as necessary next steps.

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