Detecting emergency situations by monitoring drivers' states from EEG

Xin'an Fan, Luzheng Bi*, Zhi Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

This paper proposes a new method to detect pedestrian sudden occurrence, as an example of emergency situations, by monitoring drivers' state from EEG. Three drivers attended the experiment in a driving simulator with virtual driving environments with EEG signals being collected at twenty standard locations on the scalp. The (LDA) classifier with power spectrum of EEG potentials as input features of the detection model was used to recognize the emergency situation, and (ROC) was used to determine the threshold of the classifier. The experimental results of three healthy subjects indicate that the detection model can recognize the emergency situation within one second (shorter than the response time of drivers) with an accuracy of about 70%, showing that it is feasible to detect emergency situations by monitoring driver's states from EEG.

Original languageEnglish
Title of host publication2012 ICME International Conference on Complex Medical Engineering, CME 2012 Proceedings
Pages245-248
Number of pages4
DOIs
Publication statusPublished - 2012
Event6th International Conference on Complex Medical Engineering, CME 2012 - Kobe, Japan
Duration: 1 Jul 20124 Jul 2012

Publication series

Name2012 ICME International Conference on Complex Medical Engineering, CME 2012 Proceedings

Conference

Conference6th International Conference on Complex Medical Engineering, CME 2012
Country/TerritoryJapan
CityKobe
Period1/07/124/07/12

Keywords

  • EEG
  • LDA
  • driver response
  • emergency situations
  • pedestrian sudden occurrence

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