Advertisement

Combined Linear Regression and Quadratic Classification Approach for an EEG-Based Prediction of Driver Performance

  • Gregory Apker
  • Brent Lance
  • Scott Kerick
  • Kaleb McDowell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)

Abstract

Electroencephalography (EEG) has been used to reliably and non-invasively detect fatigue in drivers. In fact, linear relationships between EEG power-spectral estimates and indices of driver performance have been found during simplified driving tasks. Here we sought to predict driver performance using linear regression in a more complex paradigm. Driver performance varied widely between participants, often varying greatly within a single driving session. We found that a non-selective linear regression model did not generalize well between periods of stable and erratic driving, yielding large errors. However, prediction errors were significantly reduced by training a linear regression model on stable driving for each participant. To provide a confidence estimate for the stable driving model, a quadratic discriminate classifier was trained to detect the transition from stable to erratic driving from the EEG power-spectra. Combined, the regression model and classifier yielded significantly lower prediction errors and provided improved discrimination of poor driving.

Keywords

EEG Regression Driving Fatigue Power Spectral Density 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Treat, J.R., Tumbas, N.S., McDonald, S.T., Shinar, D., Hume, R.D., Mayer, R.E., Stanisfer, R.L., Castellan, N.J.: Tri-level study of the causes of traffic accidents.  Report No. DOT-HS-034-3-535-77, TAC (1977)Google Scholar
  2. 2.
    Fletcher, K., McCulloch, S., Baulk, D., Dawson, D.: Countermeasures to driver fatigue: a review of public awareness campaigns and legal approaches. Aust. N.Z. J. Public Health 29, 471–476 (2005)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Smith, P., Shah, M., da Vitoria Lobo, N.: Monitoring head/eye motion for driver alertness with one camera. In: Proc.15th International Conference on Pattern Recognition (ICPR 2000), Barcelona, Spain, vol. 4, pp. 636–642 (September 2000)Google Scholar
  5. 5.
    Perez, C.A., Palma, A., Holzmann, C.A., Pena, C.: Face and eye tracking algorithm based on digital image processing. In: Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC 2001), Tucson, Ariz, USA, vol. 2, pp. 1178–1183 (October 2001)Google Scholar
  6. 6.
    Popieul, J.C., Simon, P., Loslever, P.: “Using driver’s head movements evolution as a drowsiness indicator. In: Proc. IEEE International Intelligent Vehicles Symposium (IV 2003), Columbus, Ohio, USA, pp. 616–621 (June 2003)Google Scholar
  7. 7.
    Okogbaa, O.G., Shell, R.L., Filipusic, D.: On the investigation of the nerophysiological correlates of knowledge worker fatigue using the EEG signal. Applied Ergonomics 25, 355–365 (1994)CrossRefGoogle Scholar
  8. 8.
    Lal, S.K.L., Craig, A.: Driver Fatigue: Electroencephelography and psychological assessment. Psycholphyiology 29(3), 313–321 (2002)CrossRefGoogle Scholar
  9. 9.
    Lal, S.K.L., Craig, A.: A critical review of the pyshcophysiology of driver fatigue. Biological Psychology 55, 173–194 (2001)CrossRefGoogle Scholar
  10. 10.
    Craig, A., Tran, Y., Witjesurya, N., Nguyen, H.: Regional brain wave activity changes associated with fatigue. Psychophysiology 49, 574–582 (2012)CrossRefGoogle Scholar
  11. 11.
    Desmond, P.A., Matthews, G.: Implications of task-induced fatigue effects for in-vehicle countermeasures to driver fatigue. Accid. Anal. Prev. 29(4), 515–523 (1997)CrossRefGoogle Scholar
  12. 12.
    Desmond, P.A., Matthews, G.: Task-induced Fatigue Effects and Simulated Driving. Quart. Journal of Experimental Psychology 55(2), 659–686 (2002)Google Scholar
  13. 13.
    Peiris, M.T.R., Davidson, P.R., Bones, P.J., Jones, R.D.: Detection of lapses in responsiveness from the EEG. Journal of Neural Engineering 8 (2011)Google Scholar
  14. 14.
    Stikic, M., Johnson, R.R., Levendowski, D.J., Popovic, D.P., Olmstead, R.E., Berka, C.: EEG-derived estimators of present and future cognitive function. Frontiers in Human Neuroscience 5 (2011)Google Scholar
  15. 15.
    Sandberg, D., Akerstedt, T., Anund, A., Kecklund, G., Wahde, M.: Detecting Driver Sleepiness Using Optimized Non-Linear Combinations of Sleepiness Indicators. IEEE Trans. on Intelligent Transportation Systems 12(1), 97–108 (2011)CrossRefGoogle Scholar
  16. 16.
    Zhoa, C., Zheng, C., Zhao, M., Tu, Y., Liu, J.: Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic. Expert Systems with Applications 38, 1859–1865 (2011)CrossRefGoogle Scholar
  17. 17.
    Shen, K.Q., Ong, C.J., Li, X.P., Wilder-Smith, E.P.V.: A feature selection method multilevel mental fatigue classification. IEEE Trans. Biomed. Eng. 54(7), 1231–1237 (2007)CrossRefGoogle Scholar
  18. 18.
    Shen, K.Q., Li, X.P., Ong, C.J., Shao, S., Wilder-Smith, E.P.V.: EEG-based mental Fatigue measurement using multi-class support vector machines with confidence estimate. Clinical Neurophysiology 119, 1524–1533 (2008)CrossRefGoogle Scholar
  19. 19.
    Lin, C.T., Wu, R.C., Jung, T.P., Liang, S.F., Huang, T.Y.: Estimating Driving Performance Based on EEG Spectrum Analysis. EURASIP Journal on Applied Signal Processing 19, 3165–3174 (2005a)Google Scholar
  20. 20.
    Lin, C.T., Wu, R.C., Liang, S.F., Huang, T.Y., Chao, W.H., Chen, Y.J., Jung, T.P.: EEG-based drowsiness estimation for safety driving using independent component analysis. IEEE Trans. Circ. Syst. 52, 2726–2738 (2005b)CrossRefGoogle Scholar
  21. 21.
    Chuang, S.W., Ko, L.W., Lin, Y.P., Huang, R.S., Jung, T.P., Lin, C.T.: Co-modulatory spectral changes of independent brain processes are correlated with task performance. Neyroimage 62, 1467–1477 (2012)Google Scholar
  22. 22.
    Pattyn, N., Neyt, X., Henderickx, D., Soetens, E.: Psychophysiological investigation of vigilance decrement: boredom or cognitive fatigue? Physiological Behavior 93(1-2), 369–378 (2008)CrossRefGoogle Scholar
  23. 23.
    Akerstedt, T., Gillberg, M.: Subjective and objective sleepiness in the active individual. International Journal of Neuroscience 52, 29–37 (1990)CrossRefGoogle Scholar
  24. 24.
    Yang, G., Lin, Y., Bhattacharya, P.: A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Information Sciences 108, 1942–1954 (1942)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gregory Apker
    • 1
  • Brent Lance
    • 1
  • Scott Kerick
    • 1
  • Kaleb McDowell
    • 1
  1. 1.Human Research and Engineering DirectorateU.S. Army Research LaboratoryUSA

Personalised recommendations