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
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)