Estimating Driving Performance Based on EEG Spectrum Analysis
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Estimating Driving Performance Based on EEG Spectrum Analysis Chin-Teng Lin Brain Research Center, University System of Taiwan, Taipei 112, Taiwan Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan Email: [email protected]
Ruei-Cheng Wu Brain Research Center, University System of Taiwan, Taipei 112, Taiwan Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan Email: [email protected]
Tzyy-Ping Jung Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093-0523, USA Email: [email protected]
Sheng-Fu Liang Brain Research Center, University System of Taiwan, Taipei 112, Taiwan Department of Biological Science and Technology, National Chiao-Tung University, Hsinchu 300, Taiwan Email: [email protected]
Teng-Yi Huang Brain Research Center, University System of Taiwan, Taipei 112, Taiwan Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan Email: [email protected] Received 12 February 2004; Revised 14 March 2005 The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver’s drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver’s drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator. Keywords and phrases: drowsiness, EEG, power spectrum, correlation analysis, linear regression model.
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INTRODUCTION
Driving safety has received increasing attention due to the growing number of traffic accidents in recent years. Driver’s fatigue has been implicated as a causal factor in many accidents. The National Transportation Safety Board found that 58 percent of 107 single-vehicle roadway departure crashes were fatigue-related in 1995, where the truck driver survived
and no other vehicle was involved. Accidents caused by drowsiness at the wheel have a high fatality rate because of the marked decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents is thus a major focus of efforts in the field of active safety research [1, 2, 3, 4, 5, 6]. A
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