Assessing High Cognitive Load in Drivers through Electroencephalography

This paper explores the influence of high cognitive load on driver’s Electroencephalography (EEG) signals collected from four positions (TP9, Fp1, Fp2, TP10) along with other physiological signals, plus eye tracking, driving performance, and subjective measures. Although EEG has been used in driving research to assess mental workload, only a few studies focused on high cognitive load, but they utilized visual secondary tasks and research-grade EEG systems. Recent advancements allow for less intrusive and more affordable systems to be incorporated into vehicles. The authors tested the feasibility of one such system to differentiate three incremental levels of cognitive taskload in a preliminary simulator study, which so far has been completed by 15 participants. Each participant completed a baseline drive with no secondary task and two drives with a modified version of the n-back task (1-back, 2-back). The modification removed the verbal response during auditory stimulus presentation to increase EEG signal quality, with the 2-back level still imposing higher cognitive demand than the 1-back. The system tested was sensitive to the taskload levels, with alpha band being sensitive among all difficulty levels; beta and gamma bands distinguishing 2-back level from the baseline and 1-back; and the delta band distinguishing baseline from the n-back levels. In line with previous studies, galvanic skin response and standard deviation of gaze position also showed significant stepwise trends from the baseline to 1-back and then to 2-back. Further research is needed to investigate the ability of consumer-grade EEG headbands to differentiate different driver states.

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  • Supplemental Notes:
    • This paper was sponsored by TRB committee AND30 Standing Committee on Simulation and Measurement of Vehicle and Operator Performance.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • He, Dengbo
    • Liu, Cheng Chen
    • Donmez, Birsen
    • Plataniotis, Konstantinos N
  • Conference:
  • Publication Date: 2017

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References; Tables;
  • Pagination: 15p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01624341
  • Record Type: Publication
  • Report/Paper Numbers: 17-02615
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 8 2016 11:00AM