期刊
NEUROSCIENCE LETTERS
卷 506, 期 2, 页码 235-239出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.neulet.2011.11.014
关键词
EEG; Driver fatigue; Ratio indices; GRA; KPCA
资金
- State Key Laboratory Mechanical System and Vibration at the Shanghai Jiao Tong University [MSV-MS-2008-10]
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.
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