Research on Driver Mental Fatigue Detection Based on Improved Stripe Attention Mechanism and Deep Residual Shrinking Network

Vollständiger Abstract

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Driving-fatigue-induced attentional decline and response retardation are critical contributors to traffic accidents. However, stably and precisely identifying fatigue states from noisy electroencephalogram (EEG) signals remains a challenging issue in intelligent driving safety. To address the dual deficiencies of traditional methods in fatigue feature extraction precision and noise robustness, this paper innovatively constructs a collaborative recognition framework that integrates an Improved Strip Attention Mechanism (ISAM) with a Deep Residual Shrinkage Network (DRSN). The core innovations of this framework are twofold: ISAM achieves precise localization and focused enhancement of fatigue-related rhythmic bands in EEG signals via row–column separable adaptive pooling and channel-wise attention augmentation; concurrently, the DRSN module introduces an improved soft-thresholding function, which adaptively generates filtering thresholds through channel attention to effectively suppress noise and artifact interference in physiological signals. The deep fusion of these two modules forms a closed-loop optimization chain of “targeted feature reinforcement–adaptive noise suppression,” enabling the model to stably extract highly discriminative fatigue representations from complex non-stationary EEG signals. Validation on two public datasets, SEED-VIG and SADT, demonstrates that the proposed method achieves recognition accuracies of 98.86% and 97.38%, respectively, outperforming mainstream methods such as the convolutional spatial-frequency network and multi-scale convolutional neural network by 17.38% and 17.76%. These results confirm the significant advantages of the proposed dual-module collaborative architecture in precise fatigue characterization and anti-interference capability, offering a highly reliable technical solution for real-time driver mental fatigue monitoring in real-world road scenarios.