Evaluation of driver fatigue on two channels of EEG data

被引：108

作者：

Li Wei ^{[1
]}

He Qi-chang ^{[1
,2
]}

Fan Xiu-min ^{[1
,2
]}

Fei Zhi-min ^{[3
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200030, Peoples R China

[2] Shanghai Jiao Tong Univ, Shanghai Key Lab Adv Mfg Environm, Shanghai 200030, Peoples R China

[3] Shanghai Univ Tradit Chinese Med, Shu Guang Hosp, Dept Neurosurg, Shanghai 201203, Peoples R China

来源：

NEUROSCIENCE LETTERS | 2012年 / 506卷 / 02期

关键词：

EEG; Driver fatigue; Ratio indices; GRA; KPCA; ELECTROENCEPHALOGRAPHY; LEVEL; KPCA; ICA;

D O I：

10.1016/j.neulet.2011.11.014

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (theta, alpha, and beta) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

引用

页码：235 / 239

页数：5

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