SE-Driven Dynamic Convolution for Adaptive EEG-Based Driver Fatigue Detection Across Spectral, Spatial, and Temporal Domains.

Overview

1. Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China
2. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China

Abstract

EEG-based driver fatigue detection faces three signal-level challenges: inter-subject spectral variability, coupled frequency–spatial–temporal dynamics that existing methods process independently, and dependence on a single labeling scheme. This paper presents DCAMNet, a lightweight CNN (12.3 K parameters) that addresses these challenges through three end-to-end blocks. An SE-driven dynamic convolution block adapts spectral sensitivity per sample via input-dependent kernel weighting—applied here for the first time to fatigue detection. A spatial convolution block encodes electrode-level cortical patterns, and a temporal attention block captures fatigue dynamics through windowed variance descriptors with group-wise attention scoring. DCAMNet was evaluated on SEED-VIG (PERCLOS labels) and MESD (reaction-time labels) under both subject-mixed and leave-one-subject-out (LOSO) protocols. Under LOSO cross-validation—the operationally relevant test that eliminates within-subject information leakage and simulates deployment on unseen drivers—DCAMNet achieved 85.43% accuracy on SEED-VIG with a 2.86-point advantage over the strongest baseline, and 79±5% accuracy on MESD with a 3-point advantage. As upper-bound estimates under the subject-mixed protocol, accuracy reached 97.47% (SEED-VIG) and 96.52% (MESD). With 1.35 ms inference latency on a standard GPU, the compact architecture suggests potential suitability for real-time embedded deployment, although on-device validation on representative automotive hardware remains necessary.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• figshare:6427334, at figshare; found in “Data Availability Statement”

Data Availability Statement

The MESD dataset used in this study is openly available at https://doi.org/10.6084/m9.figshare.6427334.v5 (accessed on 23 January 2025). The SEED-VIG dataset is available from Shanghai Jiao Tong University at https://bcmi.sjtu.edu.cn/home/seed/index.html (accessed on 23 January 2025); access requires application to the data provider. No new data were created in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 keywords, 7 MeSH terms, 41 references.

Cite

This paper

Zhou, T., Cheng, J., & Zhang, J. (2026). SE-Driven Dynamic Convolution for Adaptive EEG-Based Driver Fatigue Detection Across Spectral, Spatial, and Temporal Domains. Sensors (Basel, Switzerland), 26(9), 2728. https://doi.org/10.3390/s26092728

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