Earticle

초록

영어

Safety driving performance estimation and alertness (SDPEA) has drawn the attention of researchers in preventing traffic accidents caused by drowsiness while driving. Psychophysiological measures, such as electroencephalogram (EEG), are accurately investigated to be robust candidates for drivers’ drowsiness evaluation. This paper presents an effective EEG-based driver drowsiness monitoring system by analyzing the changes of brain activities in a simulator driving environment. The proposed SDPEA system can translate EEG signals into drowsiness level. Firstly, Independent component analysis (ICA) is performed on EEG data to remove artifacts. Then, eight EEG-band powers- related features: beta, alpha, theta, delta, (alpha plus theta)/beta, alpha / beta, (alpha plus theta)/(alpha plus beta) and theta / beta are extracted from the preprocessed EEG signals by employing the Fast Fourier Transform (FFT). Subsequently, fisher score technique selects the most descriptive features for further classification. Finally, Support Vector Machine (SVM) is employed as a classifier to distinguish drowsiness level. Experimental results show that the quantitative driving performance can be correctly estimated through analyzing driver’s EEG signals by the SDPEA system.

목차

Abstract
 1. Introduction
 2. Experiment Setup and Data Acquisition
  2.1. Subjects
  2.2. Data Acquisition
 3. Methodology
  3.1. Data Preprocessing
  3.2. Fisher Score
  3.3. SVM for Classification
 4. Results and Data Analysis
 5. Conclusions
 Acknowledgment
 References

키워드

저자

• Hongyu Sun [ Shandong University of Science and Technology ]
• Lijun Bi [ Shandong University of Science and Technology ]
• Bisheng Chen [ Shandong University of Science and Technology ]
• Yinjing Guo [ Shandong University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605