An EEG-Based Brain-Computer Interface for Dual Task Driving Detection

Chin-Teng Lin*, Yu Kai Wang, Shi-An Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

A novel detective model for driver distraction was proposed in this study. Driver distraction is a significant cause of traffic accidents during these years. To study human cognition under a specific driving task, one virtual reality (VR)-based simulation was built. Unexpected car deviations and mathematics questions with stimulus onset asynchrony (SOA) were designed. Electroencephalography (EEG) is a good index for the distraction level to monitor the effects of the dual tasks. Power changing in Frontal and Motor cortex were extracted for the detective model by independent component analysis (ICA). All distracting and non-distracting EEG epochs could be revealed the existence by self-organizing map (SOM). The results presented that this system approached about 90% accuracy to recognize the EEG epochs of non-distracting driving, and might be practicable for daily life.
Original languageEnglish
Title of host publicationNEURAL INFORMATION PROCESSING, PT I
PublisherSpringer-Verlag Berlin Heidelberg
Pages701-+
Volume7062
ISBN (Print)978-3-642-24954-9 , 978-3-642-24955-6
DOIs
StatePublished - Nov 2011

Keywords

  • driver distraction
  • SOA
  • EEG
  • ICA
  • SOM

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  • Cite this

    Lin, C-T., Wang, Y. K., & Chen, S-A. (2011). An EEG-Based Brain-Computer Interface for Dual Task Driving Detection. In NEURAL INFORMATION PROCESSING, PT I (Vol. 7062, pp. 701-+). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_83