Skip to main content

Comparing the Differences of EEG Signals Based on Collision and Non-collision Cases

  • Conference paper
  • First Online:
  • 2434 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 964))

Abstract

Hazard Perception can be considered to be situation cognition for dangerous situations in the traffic environment. Enough cognition could ensure drivers’ safety especially when they are facing emergent situations, which can ensure that drivers have full time to make timely response. Maintaining constant attention is necessary for drivers which could help them to better control vehicles and then avoid conflicts effectively. Drivers’ abilities to concentrate, visual search and distraction will affect brain waves, and drivers’ attention requires coordination between brain waves in different brain regions. Some researches extracted drivers’ electroencephalography (EEG) to explore the changes of their brain waves, and previous studies indicated that different frequency bands within the normal EEG frequency range reflected quite different cognitive processes. Moreover, some researches always associate with different brain areas to explore wave activity in different frequency bands. However, there are limited studies explore how could drivers’ EEG signals influence traffic safety and which EEG variables could measure drivers’ attention. The purpose of the study is to examine which EEG variables could be taken as measurement indexes to evaluate drivers’ attention level, furthermore we compare the differences of these EEG variables under different collision avoidance results. The experimental results of this study would lead to a better understanding of choosing which EEG variables could be used to measure drivers’ attention during the emergent collision avoidance process, and how drivers’ EEG variables changed could avoid the happening of conflicting.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Lee, M.H., Im, S.Y., Lee, B.U., et al.: [IEEE 2015 Seventh International Conference on Ubiquitous and Future Networks (ICUFN) - Sapporo, Japan (2015.7.7–2015.7.10)] 2015 Seventh International Conference on Ubiquitous and Future Networks - Red-signal delay scheme to prevent vehicle accidents at the intersection, pp. 232–236 (2015)

    Google Scholar 

  2. Isler, R.B., Parsonson, B.S., Hansson, G.J.: Age-related effects of restricted head movements on the useful field of view of drivers. Accid. Anal. Prev. 29(6), 793–801 (1997)

    Article  Google Scholar 

  3. Fildes, B.: Older drivers’ safety and mobility: current and future issues. Transp. Res. Part F 9(5), 307–308 (2006)

    Article  Google Scholar 

  4. Yan, X., Zhang, X., Zhang, Y., et al.: Changes in drivers’ visual performance during the collision avoidance process as a function of different field of views at intersections. PLoS ONE 11(10), e0164101 (2016)

    Article  Google Scholar 

  5. Zhang, Y., Yan, X., Li, X., Xue, Q.: Drivers’ eye movements as a function of collision avoidance warning conditions in red light running scenarios. Accid. Anal. Prev. 96(2016), 185–197 (2016)

    Article  Google Scholar 

  6. Horswill, M.S., McKenna, F.P.: Drivers’ hazard perception ability: situation awareness on the road. In: Banbury, S., Tremblay, S. (eds.) A Cognitive Approach to Situation Awareness: Theory and Application, Chap. 4, pp. 155–175. Ashgate Publishing, Ltd., Farnham (2004)

    Google Scholar 

  7. Anstey, K.J., Horswill, M.S., Wood, J.M., Hatherly, C.: The role of cognitive and visual abilities as predictors in the multifactorial model of driving safety. Accid. Anal. Prev. 45(2012), 766–774 (2012)

    Article  Google Scholar 

  8. Darby, P., Murray, W., Raeside, R.: Applying online fleet driver assessment to help identify, target and reduce occupational road safety risks. Saf. Sci. 47(3), 436–442 (2009)

    Article  Google Scholar 

  9. McKenna, F.P., Horswill, M.S.: Hazard perception and its relevance for driver licensing. IATSS Res. 23(1), 26–41 (1999)

    Google Scholar 

  10. Quimby, A.R., Maycock, G., Carter, I.D., Dixon, R., Wall, J.G.: Perceptual abilities of accident-involved drivers. J. Saf. Res. 18(1), 45 (1987)

    Article  Google Scholar 

  11. Wells, P., Tong, S., Grayson, G., Jones, E.: Cohort II-a study of learner and new drivers-volume 1-main report, volume 2-questionnaires and data tables. Road Saf. Res. Rep. (2008)

    Google Scholar 

  12. Lamble, D., Kauranen, T., Laakso, M., et al.: Cognitive load and detection thresholds in car following situations: safety implications for using mobile (cellular) telephones while driving. Accid. Anal. Prev. 31(6), 617–623 (1999)

    Article  Google Scholar 

  13. Pulvermüller, F., Birbaumer, N., Lutzenberger, W., et al.: High-frequency brain activity: its possible role in attention, perception and language processing. Prog. Neurobiol. 52(5), 427–445 (1997)

    Article  Google Scholar 

  14. Ball, K., Owsley, C., Sloane, M.E., Roenker, D.L., Bruni, J.R.: Visual attention problems as a predictor of vehicle crashes in older drivers. Invest. Ophthalmol. Vis. Sci. 34(11), 3110±3123 (1993). PMID: 8407219

    Google Scholar 

  15. Klimesch, W.: Induced alpha band power changes in the human EEG and attention. Neurosci. Lett. 244(2), 73–76 (1998)

    Article  Google Scholar 

  16. Joochan, K., Jungryul, S., Laine, T.H.: Detecting boredom from eye gaze and EEG. Biomed. Signal Process. Control. https://doi.org/10.1016/j.bspc.2018.05.034

    Article  Google Scholar 

  17. Almahasneh, H., Chooi, W.T., Kamel, N., et al.: Deep in thought while driving: an EEG study on drivers’ cognitive distraction. Transp. Res. Part F Traffic Psychol. Behav. 26(26), 218–226 (2014)

    Article  Google Scholar 

  18. Yang, L., Ma, R., Zhang, H.M., et al.: Driving behavior recognition using EEG data from a simulated car-following experiment. Accid. Anal. Prev. (2017). S0001457517303974

    Google Scholar 

  19. Chuang, C.H., Huang, C.S., Ko, L.W., Lin, C.T.: An EEG-based perceptual function integration network for application to drowsy driving. Knowl. Based Syst. 80, 143–152 (2015)

    Article  Google Scholar 

  20. Kar, S., Bhagat, M., Routray, A.: EEG signal analysis for the assessment and quantification of driver’s fatigue. Transp. Res. Part F Traffic Psychol. Behav. 13, 297–306 (2010)

    Article  Google Scholar 

  21. Clarke, A.R., Barry, R.J., Mccarthy, R., et al.: EEG analysis in Attention-Deficit/Hyperactivity Disorder: a comparative study of two subtypes. Psychiatry Res. 81(1), 19–29 (1998)

    Article  Google Scholar 

  22. Angelos, P.: Surgical ethics and the future of surgical practice. Surgery 163(1), 1–5 (2018)

    Article  Google Scholar 

  23. Schutter, D.J., Van, H.J.: The cerebellum on the rise in human emotion. Cerebellum 4(4), 290–294 (2005)

    Article  Google Scholar 

  24. Miskovic, V., Schmidt, L.A.: Frontal brain electrical asymmetry and cardiac vagal tone predict biased attention to social threat. Int. J. Psychophysiol. 75(3), 332–338 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

The work described in this paper was supported by the National Natural Science Foundation of China (No. 71621001 and No. 71771014) and BJTU Basic Scientific Research (2017YJS108).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xuedong Yan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, X., Yan, X. (2020). Comparing the Differences of EEG Signals Based on Collision and Non-collision Cases. In: Stanton, N. (eds) Advances in Human Factors of Transportation. AHFE 2019. Advances in Intelligent Systems and Computing, vol 964. Springer, Cham. https://doi.org/10.1007/978-3-030-20503-4_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20503-4_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20502-7

  • Online ISBN: 978-3-030-20503-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics