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Brain-Computer Interface for Smart Vehicle: Detection of Braking Intention During Simulated Driving

  • Jeong-Woo Kim
  • Il-Hwa Kim
  • Stefan Haufe
  • Seong-Whan LeeEmail author
Chapter
Part of the Trends in Augmentation of Human Performance book series (TAHP, volume 5)

Abstract

It is most essential to stop a vehicle in time for assuring a driver’s safety. In this study, a simulated driving environment was implemented to study the neural correlation of braking intention in diverse driving situations. We further investigated to what extent these neural correlates can be used to detect a participant’s braking intention prior to the behavioral response. A feature combination method was proposed for the enhancement of detection performance and additional classification of emergency braking triggered by stimuli and voluntary braking. It consists of event-related potential (ERP), readiness potential (RP), and event-related desynchronization (ERD) features. Fifteen participants drove a virtual vehicle and were exposed to the diversified traffic situations in the constructed simulator framework, while technical signals (i.e., gas pedal and brake pedal), electroencephalogram (EEG) and electromyogram (EMG) signals were measured. After that, the neural correlation of the measured signals was analyzed. The proposed framework shows excellent detection performance for various kinds of driver’s braking intention. Our study suggests that a driver’s braking intention is characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by smart vehicle technology.

Keywords

Brain-computer interface (BCI) Braking intention Feature combination Electroencephalogram (EEG) Driving 

Notes

Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2015R1A2A1A05001867). The authors acknowledge the use of text from the own prior publication [39] in this article. Jeong-Woo Kim and Seong-Whan Lee thank their co-authors for allowing them to use materials from prior joint publication [39].

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Jeong-Woo Kim
    • 1
  • Il-Hwa Kim
    • 1
  • Stefan Haufe
    • 2
    • 3
    • 4
  • Seong-Whan Lee
    • 1
    Email author
  1. 1.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Neural Engineering Group, Department of Biomedical EngineeringThe City College of New YorkNew YorkUSA
  3. 3.Machine Learning Group, Department of Computer ScienceBerlin Institute of TechnologyBerlinGermany
  4. 4.Bernstein Focus NeurotechnologyBerlinGermany

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