Research on the Prediction of Driver Fatigue Degree Based on EEG Signals

This study presents a high-performance hybrid deep learning framework for predicting driver fatigue degree and reveals the applicability differences in EEG features across experimental paradigms, offering guidance for feature selection and model deployment in practical applications.

Authors

Chengbin Jiang
Zhanyang Wang (ORCID: https://orcid.org/0009-0001-4196-8611)
Jingchun Sun (ORCID: https://orcid.org/0000-0002-3284-7973)
Xin Du (ORCID: https://orcid.org/0009-0003-4637-1368)

Institutions

Beijing Jiaotong University (CN)

Publication Details

Journal: Sensors
Published: 2025-12-01
DOI: https://doi.org/10.3390/s25237316
Primary Topic: Sleep and Work-Related Fatigue
Type: article
Field-Weighted Citation Impact: 0.00

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article

Research on the Prediction of Driver Fatigue Degree Based on EEG Signals

Chengbin Jiang, Zhanyang Wang, Jingchun Sun, Xin Du

Dec 1, 2025

Sensors

Sleep and Work-Related Fatigue

article

Research on the Prediction of Driver Fatigue Degree Based on EEG Signals

Chengbin Jiang, Zhanyang Wang, Jingchun Sun, Xin Du

December 1, 2025article en

Abstract

SensorsVol. 25(23)

Beijing Jiaotong University (CN)

Openalex Percentile: Top 59%

Sleep and Work-Related Fatigue

DOI Open Access PDF OpenAlex

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Research on the Prediction of Driver Fatigue Degree Based on EEG Signals — Chengbin Jiang, Zhanyang Wang, et al. · Sensors (2025) | TGRS Research Map | TGRS