title: Evaluation of driver fatigue on two channels of EEG data.
abstract: Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) 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.
authors: School of Mechanical Engineering, Shanghai Jiaotong University, No. 800 Dongchuan Road, Minhang District, Shanghai 200030, China.
authors: Wei
authors: W
authors: Li
authors: Qi-chang
authors: QC
authors: He
authors: Xiu-min
authors: XM
authors: Fan
authors: Zhi-min
authors: ZM
authors: Fei
mesh_terms: D000328:Adult; D001334:Automobile Driving; D004569:Electroencephalography / Q000379:methods*; D005221:Fatigue / Q000150:complications*; D006801:Humans; D008297:Male; D008959:Models, Neurological; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult