Detection of multi-class emergency situations during simulated driving from ERP

Il Hwa Kim, Jeong Woo Kim, Stefan Haufe, Seong Whan Lee

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

3 Citations (Scopus)

Abstract

We present a driving simulator study investigating whether a driver's braking intention in emergency situations can be detected under more general circumstances than previously described in the literature. Precisely, we here simulated three kinds of realistic emergency situations instead of only one as considered in Haufe et al., 2011. For each of the three situations, the analysis of electroencephalography (EEG) data reveals a different characteristic spatio-temporal event-related potential (ERP) sequence. For all stimuli, topographical maps of area under the curve (AUC) scores related to the discrimination between emergency and normal driving situations show a significant positive deflection in parietal regions about 300ms post-stimulus. Thus, it is possible to predict different emergency situations from EEG before the actual braking. A classification analysis indeed reveals that EEG-based emergency braking detection can be performance faster than electromyography- or pedal-based detection, while being as robust.

Original languageEnglish
Title of host publication2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Pages49-51
Number of pages3
DOIs
Publication statusPublished - 2013
Event2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of
Duration: 2013 Feb 182013 Feb 20

Publication series

Name2013 International Winter Workshop on Brain-Computer Interface, BCI 2013

Other

Other2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
CountryKorea, Republic of
CityGangwon Province
Period13/2/1813/2/20

Keywords

  • EEGIERP Emergency braking
  • Neuro-driving

ASJC Scopus subject areas

  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Detection of multi-class emergency situations during simulated driving from ERP'. Together they form a unique fingerprint.

  • Cite this

    Kim, I. H., Kim, J. W., Haufe, S., & Lee, S. W. (2013). Detection of multi-class emergency situations during simulated driving from ERP. In 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 (pp. 49-51). [6506626] (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013). https://doi.org/10.1109/IWW-BCI.2013.6506626