An attempt to predict drowsiness by Bayesian estimation

Atsuo Murata, Takehito Hayami, Yusuke Matsuda, Makoto Moriwaka

Research output: Chapter in Book/Report/Conference proceedingChapter


EEG (EEG-MPF, EEG-α/β), heart rate variability (RRV3), tracking error and subjective rating of fatigue (drowsiness) while performing a simulated driving task were measured to predict drowsiness on the basis of Bayesian estimation method. The relation between these measurements and drowsiness was analyzed. As a result, EEG-MPF tended to decrease with the increase of drowsiness. It tended that EEG-α/β, RRV3 and tracking error increased with the increase of drowsiness. Then, we have proposed a method that can predict drowsiness by applying Bayesian estimation method to physiological measurements such as EEG-MPF, EEG-α/β, RRV3. Bayesian estimation carries out a statistical inference using some kind of evidences or observations and calculating the probability that a hypothesis is true. As a result, we confirmed that the proposed method could to some extent predict the symptom of decreased consciousness (drowsiness).

Original languageEnglish
Title of host publicationAdvances in Human Aspects of Road and Rail Transportation
PublisherCRC Press
Number of pages10
ISBN (Electronic)9781439871249
ISBN (Print)9781439871232
Publication statusPublished - Jan 1 2012


  • Bayesian estimation
  • Drowsiness
  • EEG
  • HRV

ASJC Scopus subject areas

  • Engineering(all)


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