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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51158

Title: An EEG-based perceptual function integration network for application to drowsy driving
Authors: CH Chuang
CS Huang
LW Ko
CT Lin
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Electroencephalogram
Independent component analysis
Multiple classifiers system
Drowsy driving
Date: 2015-05
Issue Date: 2018-11-14T08:56:38Z
Publisher: Knowledge-Based Systems
Abstract: Abstract: 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.
Relation: 80 pp.143-152
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51158
Appears in Collections:[Department of Computer Science and Engineering] Periodical Articles

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