EEG Based Driver Inattention Identification via Feature Profiling and Dimensionality Reduction

  • Omid Dehzangi
  • Mojtaba TaherisadrEmail author
Conference paper
Part of the Internet of Things book series (ITTCC)


More than 90% of the persistently increasing traffic fatalities is related to human choice/error. Monitoring driver attention has a direct effect on decreasing injury/fatality rates. In recent years, there has been much effort to estimate drivers’ state with the goal of improving their driving behavior and preventing vehicle crashes in the first place. Physiological based detection has shown to be the most direct method of measuring driver state among which, electroencephalogram (EEG) is the most comprehensive method. EEGs are recorded from multiple channels that are processed separately. However, contribution of a fairly large number of the channels might be minimal to the target application. The computational load and the redundancy induced by those channels can hurt the identification performance. In this study, we propose an EEG-based systematic methodology for the assessment of driver state of inattention. Our proposed framework includes three major modules: (1) We first characterize each EEG channel rigorously via extraction of various categories of descriptors as features, (2) we then capture the contribution of each channel toward the identification task via channel specific feature dimensionality reduction, (3) we then conduct channel selection in order to find key brain regions of impact. Eight subjects participated in our naturalistic driving study. Our proposed method resulted in the accuracy of 98.99 ± 1.2% inattention identification accuracy. We also discovered that the first and second best channels are consistently selected from frontal and parietal regions for participating subjects.


EEG Dimensionality reduction Linear discriminant analysis Neighborhood preserving embedding Driver distraction ReliefF 


  1. 1.
    Lenhart, A., et al.: Social Media & Mobile Internet Use among Teens and Young Adults. Millennials. Pew Internet & American Life Project (2010)Google Scholar
  2. 2.
    Green, L.: Texting and driving: a look at self-control. knowledge, and adherence to the law among young drivers. Diss. Soc. Learn. Theor. (2017)Google Scholar
  3. 3.
    Zhang, H., et al.: Effect of personality traits, age and sex on aggressive driving: psychometric adaptation of the driver aggression indicators scale in China. Accident Anal. Prevent. 103, 29–36 (2017)CrossRefGoogle Scholar
  4. 4.
    Lal, S.K.L., et al.: Development of an algorithm for an EEG-based driver fatigue countermeasure. J. Saf. Res. 34.3, 321–328 (2003)CrossRefGoogle Scholar
  5. 5.
    Yang, G., Lin, Y., Bhattacharya, P.: A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Informat. Sci. 180(10), 1942–1954 (2010)CrossRefGoogle Scholar
  6. 6.
    Lin, C.T., et al.: EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Trans. Circuits Syst. I: Regul. Pap. 52.12, 2726–2738 (2005)Google Scholar
  7. 7.
    Khushaba, R.N., et al.: Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans. Biomed. Eng. 58.1, 121–131 (2011)CrossRefGoogle Scholar
  8. 8.
    Dong, Y., et al.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12.2 , 596–614 (2011)CrossRefGoogle Scholar
  9. 9.
    Metz, B., Schmig, N., Hans-Peter, K.: Attention during visual secondary tasks in driving: adaptation to the demands of the driving task. Trans. Res. Part F Traffic Psychol. Behav. 14(5), 369–380 (2011)CrossRefGoogle Scholar
  10. 10.
    Young, K.L., Lenn, M.G., Williamson, A.R.: Sensitivity of the lane change test as a measure of in-vehicle system demand. Appl. Ergonom. 42(4), 611–618 (2011)CrossRefGoogle Scholar
  11. 11.
    Wege, C., Will, S., Victor, T.: Eye movement and brake reactions to real world brake-capacity forward collision warningsA naturalistic driving study. Accident Anal. Prevent. 58, 259–270 (2013)CrossRefGoogle Scholar
  12. 12.
    Alizadeh, V., Dehzangi, O.: The impact of secondary tasks on drivers during naturalistic driving: analysis of EEG dynamics. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (2016)Google Scholar
  13. 13.
    Deshmukh, S., Dehzangi, O.: Identification of real-time driver distraction using optimal subBand detection powered by wavelet packet transform. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE (2017)Google Scholar
  14. 14.
    Rajendra, V., Dehzangi, O.: Detection of distraction under naturalistic driving using galvanic skin responses. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE (2017)Google Scholar
  15. 15.
    Lin, C.T., et al.: Spatial and temporal EEG dynamics of dual-task driving performance. J. Neuroeng. Rehabil. 8.1(11) (2011)Google Scholar
  16. 16.
    Wali, M.K., Murugappan, M., Ahmad, B.: Subtractive fuzzy classifier based driver distraction levels classification using EEG. J. Phys. Ther. Sci. 25(9), 1055–1058 (2013)CrossRefGoogle Scholar
  17. 17.
    Wang, S., et al.: Online prediction of driver distraction based on brain activity patterns. IEEE Trans. Intell. Transp. Syst. 16.1, 136–150 (2015)CrossRefGoogle Scholar
  18. 18.
    Almahasneh, H., et al.: Deep in thought while driving: An EEG study on drivers cognitive distraction. Transp. Res. Part F: Traffic Psychol. Behav. 26, 218–226 (2014)CrossRefGoogle Scholar
  19. 19.
    Bach, K.M., et al.: Evaluating driver attention and driving behaviour: comparing controlled driving and simulated driving. In: Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction, vol. 1. British Computer Society (2008)Google Scholar
  20. 20.
    Taherisadr, M., Dehzangi, O., Parsaei, H.: Single channel EEG artifact identification using two-dimensional multi-resolution analysis. Sensors 17(12), 2895 (2017)CrossRefGoogle Scholar
  21. 21.
    Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 1027–1061 (2007)Google Scholar
  22. 22.
    Pampu, N.C.: Study of effects of the short time Fourier transform configuration on EEG spectral estimates. Acta Technica Napocensis 52.4(26) (2011)Google Scholar
  23. 23.
    Safavian, S., Rasoul, S., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybernet. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Loo, C.K., Samraj, A., Lee, G.C.: Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface. Discret. Dyn. Nat. Soc. (2011)Google Scholar
  25. 25.
    Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory. Phys. D: Nonlin. Phenom. 31(2), 277–283 (1988)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Lei, S., Roetting, M.: Influence of task combination on EEG spectrum modulation for driver workload estimation. Human Fact. 53(2), 168–179 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Rockefeller Neuroscience Institute, West Virginia UniversityMorgantownUSA
  2. 2.University of MichiganDearbornUSA

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