Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments.

Attention-related Pilot Performance Decrements (APPD) contribute substantially to aviation incidents, yet existing electroencephalography (EEG)-based monitoring methods often lack generalization, robustness to noise, and effective temporal-spectral integration. We propose a temporal-spectral fusion transformer (TF-T) combining multi-scale preprocessing, dual-stream temporal and spectral feature extraction, and transformer-based fusion with enhanced temporal-spectral integration and multi-resolution feature processing for multiclass cognitive state recognition. Three variants (TF-T1-TF-T3) are evaluated on controlled and ecologically realistic EEG datasets under clean and noise-augmented (Gaussian, Uniform, COMBO) conditions, using chronological partitioning to avoid temporal leakage. TF-T2 achieves the highest clean-data accuracy (99.2%), while TF-T3 offers superior robustness, improving Macro-F1 by ~4.5-4.7 points across all noise types and outperforming state-of-the-art baselines by up to +8 Macro-F1 under COMBO noise, supporting its deployment in perturbation-prone aviation environments.