基于无线脑电信号分析的实时疲劳驾驶检测与预警研究  

Research on Real-Time Fatigue Driving Detection and Early Warning Based on Wireless EEG Signal Analysis

作　　者：王恁 周子敬 赵云芃 郭浩 陈俊杰 WANG Nen;ZHOU Zijing;ZHAO Yunpeng;GUO Hao;CHEN Junjie(College of Information and Computer,Taiyuan University of Technology,Shanxi Jinzhong 030600,China;College of Software,Taiyuan University of Technology,Shanxi Jinzhong 030600,China;College of Art,Taiyuan University of Technology,Shanxi Jinzhong 030600,China)

机构地区：[1]太原理工大学信息与计算机学院,山西晋中030600 [2]太原理工大学软件学院,山西晋中030600 [3]太原理工大学艺术学院,山西晋中030600

出　　处：《太原理工大学学报》2020年第6期852-859,共8页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金资助项目(61672374,61741212,61876124,61873178);山西省科技厅应用基础研究项目青年面上项目(201701D221119,201801D121135);山西省科技厅重点研发计划项目(201803D31043);教育部赛尔网络下一代互联网技术创新项目(NGII20170712)。

摘　　要：针对目前基于脑电信号的疲劳驾驶检测存在的缺乏实时检测与预警的问题,设计模拟驾驶试验。通过TGAM模块和蓝牙模块实时采集并记录“eSense”专注度(Attention)、放松度(Meditation)、眨眼次数以及θ波、α波、β波的功率谱,采用专注度与放松度的比值aA/M、(θ+α)/β的功率谱密度比值cPSD以及眨眼频率bBlink作为疲劳指数,计算并使用专注度和放松度的相关性系数作为分类特征进行分类。使用k-最近邻算法(KNN)对不同疲劳程度的3种疲劳指数分类。使用改进D-S证据理论合成算法,将3种特征准确率综合为一种判断疲劳的综合指数m(θ).结果表明,疲劳指数aA/M、cPSD、bBlink能够反映驾驶员驾驶状态的变化,模拟实验驾驶55 min左右被试开始出现疲劳状态,55~75 min被试已处于疲劳驾驶状态。疲劳指数阈值分别为aA/M:0.8~1,cPSD:3.32~4.64,bBlink:0.28~0.42.综合疲劳指数m(θ)的准确率略高于单个疲劳指数的准确率。该方法为未来实际生活中的疲劳驾驶实时检测与预警提供了重要的科学理论依据和技术支撑。With EEG signals as the"gold standard"for fatigue detection,aiming at the lack of real-time detection and early warning of current fatigue driving detection based on EEG signals,we designed a simulated driving test to collect and record the"eSense"Attention and Meditation,blink frequency,and the power spectrum of theθ,α,andβwaves in real time through the TGAM module and the Bluetooth module.The ratio of attention to meditation,the power spectral density ratio of(θ+α)/β,and blink frequency were used as the fatigue indexes to calculate and use the correlation coefficient of Attention and Meditation as classification features.The k-nearest neighbor algorithm(KNN)was used to classify three fatigue exponents with different fatigue degrees.The improved D-S evidence theory synthesis algorithm was used to integrate the accuracy of the three features into a comprehensive index m(θ)to judge fatigue.The results show that fatigue index aA/M,cPSD,and bBlink can reflect the change of driver's driving state.In the simulation experiment,subjects began to be fatigued after driving for about 55 min,and they were already in the fatigue driving state between 55 and 75 min.The fatigue index thresholds are:aA/M:0.8-1;cPSD:3.32-4.64;bBlink:0.28-0.42.The accuracy of comprehensive fatigue index m(θ)is slightly higher than that of single fatigue index.This method provides an important scientific basis and technical support for real-time detection and prediction of fatigue driving in the future.

关 键 词：脑电信号 疲劳驾驶 功率谱密度 K-最近邻算法 D-S证据理论 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]