Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system

Fu Chang Lin*, Li-Wei Ko, Chun Hsiang Chuang, Tung Ping Su, Chin Teng Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

Abstract

A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value <0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.

Original languageEnglish
Article number6148312
Pages (from-to)2044-2055
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume59
Issue number9
DOIs
StatePublished - 7 Sep 2012

Keywords

  • Drowsiness
  • drowsy state monitoring
  • electroencephalogram (EEG)
  • neural fuzzy system
  • prediction

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