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基于脑电信号多特征融合的驾驶员精神疲劳状态多分类研究

王雨生 杨聚芬 刘志钢

汽车工程学报2025,Vol.15Issue(3):340-352,13.
汽车工程学报2025,Vol.15Issue(3):340-352,13.DOI:10.3969/j.issn.2095-1469.2025.03.07

基于脑电信号多特征融合的驾驶员精神疲劳状态多分类研究

A Study on Multi-Class Classification of Driver Mental Fatigue States Based on Multi-Feature Fusion of EEG Signals

王雨生 1杨聚芬 1刘志钢1

作者信息

  • 1. 上海工程技术大学,上海 201620
  • 折叠

摘要

Abstract

To meet the safety requirements for driver fatigue detection and warning in the most common L2 level intelligent driving scenarios,a four-class classification of driver states is achieved using EEG signals collected by a multi-channel wireless EEG cap.A convolutional recurrent neural network is used to train models using different combinations of frequency-domain,time-domain and nonlinear features.The results show that the best recognition performance is achieved when combining the differential entropy from nonlinear features with the average absolute value from time-domain features.Furthermore,three integration strategies are proposed to fuse base classifiers trained on different feature combinations.The method achieves accurate multi-class classification of driver fatigue states in a cost-effective and user-friendly manner,and promotes the application of wearable devices in driving scenarios to improve driving safety.

关键词

驾驶员疲劳检测/脑电信号/卷积循环神经网络/可穿戴式设备/驾驶安全

Key words

driver fatigue detection/EEG signal/convolutional recurrent neural network/wearable devices/driving safety

分类

交通工程

引用本文复制引用

王雨生,杨聚芬,刘志钢..基于脑电信号多特征融合的驾驶员精神疲劳状态多分类研究[J].汽车工程学报,2025,15(3):340-352,13.

基金项目

国家自然科学基金项目(52302438) (52302438)

汽车工程学报

2095-1469

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