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.
Chapter PDF
Similar content being viewed by others
References
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)
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)
National Sleep Foundation. Sleep in America Poll, http://www.sleepfoundation.org/article/sleep-america-polls/2005-adult-sleep-habits-and-styles
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)
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)
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)
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)
Lal, S.K.L., Craig, A.: Driver Fatigue: Electroencephelography and psychological assessment. Psycholphyiology 29(3), 313–321 (2002)
Lal, S.K.L., Craig, A.: A critical review of the pyshcophysiology of driver fatigue. Biological Psychology 55, 173–194 (2001)
Craig, A., Tran, Y., Witjesurya, N., Nguyen, H.: Regional brain wave activity changes associated with fatigue. Psychophysiology 49, 574–582 (2012)
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)
Desmond, P.A., Matthews, G.: Task-induced Fatigue Effects and Simulated Driving. Quart. Journal of Experimental Psychology 55(2), 659–686 (2002)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Akerstedt, T., Gillberg, M.: Subjective and objective sleepiness in the active individual. International Journal of Neuroscience 52, 29–37 (1990)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Apker, G., Lance, B., Kerick, S., McDowell, K. (2013). Combined Linear Regression and Quadratic Classification Approach for an EEG-Based Prediction of Driver Performance. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Foundations of Augmented Cognition. AC 2013. Lecture Notes in Computer Science(), vol 8027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39454-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-39454-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39453-9
Online ISBN: 978-3-642-39454-6
eBook Packages: Computer ScienceComputer Science (R0)