To assure aviation safety: the pilot fatigue detection based on short-term multimodal physiological signals

TL;DR

This paper introduces a fast and accurate method to detect pilot fatigue using short-term EEG and ECG signals, improving aviation safety with lower computational costs.

Contribution

A novel framework for pilot fatigue detection using multimodal signals with streamlined feature selection and low training cost.

Findings

The framework achieved 88.42% accuracy in cross-subject validation, outperforming previous EEG-only models.

Training time was reduced to 39.3 seconds, significantly lower than deep learning models.

The method uses statistical ECG features and a two-stage ANOVA-SVM process for robust feature selection.

Abstract

Pilot fatigue detection based on physiological signals is practical for aviation safety. Current methods face challenges in balancing the high computational cost of deep learning models with robust accuracy, especially when integrating short-term multimodal physiological signals. To address these challenges, this paper proposes a framework for fast, accurate, and robust pilot fatigue detection by fusing features from electroencephalogram (EEG) and electrocardiogram (ECG) signals. The primary novelty of this work lies in a streamlined selection and classification strategy that overcomes the intrinsic limitations of Heart Rate Variability (HRV) analysis in short (2-s) segments while maintaining competitive accuracy at a drastically lower training cost. Specifically, by utilizing statistical ECG features, which are then integrated with EEG markers through a two-stage ANOVA-SVM feature selection process. The optimized, low-dimensional feature set is then classified using an XGBoost model. Evaluated on data from 32 pilots, the framework demonstrated robust generalization with an accuracy of 88.42% in rigorous cross-subject cross-validation, significantly outperforming our previous EEG-only ASFT-Transformer. While standard cross-clip validation yielded a higher accuracy of 98.36%, the cross-subject metric highlights the model's potential utility for unseen individuals. Crucially, the framework achieves this performance with an average training time of only 39.3 s, a drastic reduction compared to mainstream deep learning models. By striking a balance between accuracy, generalization, and efficiency, this study presents a promising and feasible approach for objective pilot fatigue management.

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