An EEG-based perceptual function integration network for application to drowsy driving

Chun Hsiang Chuang*, Chih Sheng Huang, Li-Wei Ko, Chin Teng Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


Drowsy driving is among the most critical causes of fatal crashes. Thus, the development of an effective algorithm for detecting a driver's cognitive state demands immediate attention. For decades, studies have observed clear evidence using electroencephalography that the brain's rhythmic activities fluctuate from alertness to drowsiness. Recognition of this physiological signal is the major consideration of neural engineering for designing a feasible countermeasure. This study proposed a perceptual function integration system which used spectral features from multiple independent brain sources for application to recognize the driver's vigilance state. The analysis of brain spectral dynamics demonstrated physiological evidenced that the activities of the multiple cortical sources were highly related to the changes of the vigilance state. The system performances showed a robust and improved accuracy as much as 88% higher than any of results performed by a single-source approach.

Original languageEnglish
Pages (from-to)143-152
Number of pages10
JournalKnowledge-Based Systems
StatePublished - 1 May 2015


  • Drowsy driving
  • Electroencephalogram
  • Independent component analysis
  • Multiple classifiers system

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