Advanced EEG Processing for the Detection of Drowsiness in Drivers

Griet Goovaerts, Ad Denissen, Milica Milosevic, Geert van Boxtel, Sabine Van Huffel

Abstract

Drowsiness is a serious problem for drivers which causes many accidents every day. It is estimated that drowsiness is the cause of four deaths and 100 injuries per day in the United States. In this paper two methods have been developed to detect drowsiness based on features of ocular artifacts in EEG signals. The ocular artifacts are derived from the EEG signals by using Canonical Correlation Analysis (BSS-CCA). Wavelet transforms are used to automatically select components containing eye blinks. Sixteen features are then calculated from the eye blink and used for drowsiness detection. The first method is based on linear regression, the second on fuzzy detection. For the first method, the drowsiness level is correctly detected in 72% of the epochs. The second method uses fuzzy detection and detects the drowsiness correctly in 65% of the epochs. The best results are obtained when using one single eye blink feature.

References

  1. A.A.A. Foundation (2010). Asleep at the wheel: The prevalence and impact of drowsy driving.
  2. Abdi, H. and Williams, L. (2010). Principal component analysis. Wiley Interdisciplinary Review: computational statistics, (2):433-459.
  3. Borga, M. and Knutsson, H. (2001). A canonical approach to Blind Source Separation. Report LiUIMT-EX-0062 Department of Biomedical Engineering, Linkping University.
  4. Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., and Babiloni, F. (2012). Measuring neurphysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience and Biobehavioral Reviews, pages 45-57.
  5. De Clercq, W., Vergult, A., Vanrumste, B., Van Paesschen, W., and Van Huffel, S. (2006). Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram. IEEE transactions on Biomedical Engineering, 53(12):2583-2587.
  6. Fenton, N. and Neil, M. (2012). Correlation coefficient and p-values: what they are and why you need to be wary of them. In Risk assessment and Decision Analysis with Bayesian Networks. CRC Press.
  7. Geetha, G. and Geethalakshmi, S. (2011). Scrutinizing different techniques for artifact removal from EEG signals. International Journal of Engineering Science and Technology, (1).
  8. Hargutt, V. and Kruger, H. (2001). Eyelid movements and their predictive value for fatigue stages. In International Conference on Traffic and Transport Psychology.
  9. Homan, R. W., Herman, J., and Purdy, P. (1987). Cerebral location of international 10-20 system electrode placement. Electroencephalography and clinical neurophysiology, 66(4):376-382.
  10. Hu, S. and Zheng, G. (2009). Driver drowsiness detection with eyelid related parameters by support vector machine. Expert Systems with Applications, 36(4):7651- 7658.
  11. Kaida, K., Takahasi, M., Akerstedt, T., Nakata, A., Otsuka, Y., Haratani, T., and Fukasawa, K. (2006). Validation of the Karolinska sleepiness scale against performance and EEG variables. Clinical Neurophysiology, 7(117):1574-1581.
  12. Krishnaveni, V., Jayaraman, S., Aravind, S., Hariharasudhan, V., and Ramadoss, K. (2006). Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform. Measurement science review, 6(2):45-57.
  13. Picot, A., Charbonnier, S., and Caplier, A. (2011). EOGbased drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers. Preprints of the 18th IFAC World Congress, 18:14283-14288.
  14. Reyner, L. and Horne, J. (1998). Falling asleep whilst driving: are drivers aware of prior sleepiness? Int J Legal Med, 3(111):120-123.
  15. Rosenfield, M. (2011). Computer vision syndrome: a review of ocular causes and potential treatments. Ophthalmic and Physiological Optics, 31(5):502-515.
  16. Salwani, M. and Jasmy, Y. (2005). Comparison of few wavelets to filter ocular artifacts in eeg using lifting wavelet transform. In TENCON 2005 2005 IEEE Region 10, pages 1-6. IEEE.
  17. Simon, M., Schmidt, E. A., Kincses, W. E., Fritzsche, M., Bruns, A., Aufmuth, C., Bogdan, M., Rosenstiel, W., and Schrauf, M. (2011). Eeg alpha spindle measures as indicators of driver fatigue under real traffic conditions. Clinical Neurophysiology, 122(6):1168-1178.
  18. Svensson, U. (2004). Blink behaviour based drowsiness detecion - method development and validation. Master's thesis, University of Linköping.
  19. Vergult, A., De Clercq, W., Palmini, A., Vanrumste, B., Dupont, P., Van Huffel, S., and Van Paesschen, W. (2007). Improving the Interpretation of Ictal Scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia, 48(5):950-958.
  20. Verwey, W. B. and Zaidel, D. M. (2000). Predicting drowsiness accidents from personal attributes, eye blinks and ongoing driving behaviour. Personality and Individual Differences, 28(1):123-142.
  21. Vuckovic, A., Radivojevic, V., Chen, A. C., and Popovic, D. (2002). Automatic recognition of alertness and drowsiness from eeg by an artificial neural network. Medical Engineering & Physics, 24(5):349-360.
  22. Yue, C. (2011). EOG signals in drowsiness research. Master's thesis, University of Linköping.
Download


Paper Citation


in Harvard Style

Goovaerts G., Denissen A., Milosevic M., van Boxtel G. and Van Huffel S. (2014). Advanced EEG Processing for the Detection of Drowsiness in Drivers . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 205-212. DOI: 10.5220/0004800102050212


in Bibtex Style

@conference{biosignals14,
author={Griet Goovaerts and Ad Denissen and Milica Milosevic and Geert van Boxtel and Sabine Van Huffel},
title={Advanced EEG Processing for the Detection of Drowsiness in Drivers},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={205-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004800102050212},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Advanced EEG Processing for the Detection of Drowsiness in Drivers
SN - 978-989-758-011-6
AU - Goovaerts G.
AU - Denissen A.
AU - Milosevic M.
AU - van Boxtel G.
AU - Van Huffel S.
PY - 2014
SP - 205
EP - 212
DO - 10.5220/0004800102050212