Estimating driving performance based on EEG spectrum analysis

Chin Teng Lin, Ruei Cheng Wu, Tzyy Ping Jung, Sheng Fu Liang, Teng Yi Huang

Research output: Contribution to journalArticlepeer-review

124 Citations (Scopus)


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 EEC-based drowsiness estimation system that combines electroencephalogram (EEC) 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.

Original languageEnglish
Pages (from-to)3165-3174
Number of pages10
JournalEurasip Journal on Applied Signal Processing
Issue number19
Publication statusPublished - 2005

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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