Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments.
Overview
- Faculty of Information Technology, Electric Power University, Ha Noi, Vietnam
- Computer Science and Engineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States
Abstract
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.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data
Datasets cited
- kaggle.com/
competitions/ , at Kaggle; found in “Data availability statement”reducing-commercial-avia tion-fatalities
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 3 authors, 8 keywords, 26 references.
Cite
This paper
Nguyen, Q. A., Anh Dao, N., & Nguyen, L. (2026). Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments. Frontiers in big data, 9, 1837706. https://
BibTeX
@article{nguyen2026noise
author = {Nguyen, Quynh Anh and Anh Dao, Nam and Nguyen, Long},
title = {{Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments}},
journal = {Frontiers in big data},
year = {2026},
month = jul,
volume = {9},
pages = {1837706},
publisher = {Frontiers Media SA},
issn = {2624-909X},
doi = {10.3389/
url = {https://
pmid = {42494926},
pmcid = {PMC13392987}
}
RIS
TY - JOUR
AU - Nguyen, Quynh Anh
AU - Anh Dao, Nam
AU - Nguyen, Long
TI - Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments
T2 - Frontiers in big data
J2 - Front Big Data
PY - 2026
DA - 2026/
VL - 9
SP - 1837706
SN - 2624-909X
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
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