EFFECTIVE APPROACH FOR THE DRUNKEN DRIVING AND DROWSY STATE DETECTION USING EEG
Creators
- 1. Assistant Professor, Department of Electronics and Communication Engineering, Velalar College of Engineering and Technology, Tamilnadu
Description
In today’s scenario, more and more people are required to travel back and forth to various places. With the increasing vehicular population and their movements on the roads, accidents are also steadily increasing. It has become a nightmare for the authorities to prevent /reduce such fatal accidents on the roads. But the authorities’ efforts are in vain. It is shocking to know the study results that around 50% of the road accidents are owing to drunken driving all over the world and Drowsiness also play a vital role. Any mechanism or device to reduce such deaths will be of great help. Drunken driving and its subsequent catastrophe can be avoided by monitoring the EEG of the driver. The power of the EEG signal in frontal region decreases with the increase in the amount of alcohol intake, and the power of the EEG signal in central, occipital region increases. For drowsiness detection, the frequency variations in EEG wave is to be used. Therefore, power spectral density can be used as a parameter to differentiate EEG of alcoholic from nonalcoholic and similarly Fourier transform is used for drowsiness detection, thereby reducing drunken as well as drowsiness driving.
Files
GLCMC-008.pdf
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Additional details
References
- 1. Chin-Teng Lin, Che-Jui Chang, Bor-Shyh Lin, Shao-Hang Hung, Chih-Feng Chao, and I-Jan Wang ," A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection", IEEE Trans. on Biomedical circuits and systems, Vol. 4, No. 4, August 2010. 2. E. Malar, M. Gauthaam, D. Chakravarthy, "A Novel Approach for the Detection of Drunken Driving using the Power Spectral Density Analysis of EEG", International Journal of Computer Applications (0975 – 8887) Volume 21– No.7, May 2011. 3. Philip, P. Vervialle, F. Le Breton, P. Taillard, J. Horne, J.A. 2001. Fatigue, alcohol, and serious road crashes in France: factorial study of national data. BMJ. 322:829–30. 4. Connor, J. Norton, R. Ameratunga, S., et al. 2002. Driver sleepiness and risk of serious injury to car occupants: population based case-control study. BMJ. 324:1125-8. 5. Horne, J.A., Reyner, L.A.1995. Sleep related vehicle accidents. BMJ. 310,565–7. 6. Banks, S. Catcheside, P. Lack, L. Grunstein, R.R. McEvoy, R.D. 2004. Low levels of alcohol impair driving simulator performance and reduce perception of crash risk in partially sleep deprived subjects. Sleep. 27, 1063–7. 7. Ashkan Yazdani, S. Kamaledin Setarehdan. Classification of EEG signal correlated with alcohol abusers. Control and Intelligent Processing Centre of Excellence, School of ECE, Faculty of Engineering, University of Tehran, Iran.ISBN-1-4244-0779-6/07-2007. 8. Zhong, S. and Ghosh, J. 2002.HMMs and coupled HMMs for multichannel EEG classification. Proc. IEEE Int. Joint Conf. on Neural Networks, Honolulu, Hawaii.1154-1159. 9. Welch, P.D. 1967.The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodogram. IEEE Trans. Audio & Electroacoust.15, 70–73. 10. Vadim, V. Nikulin, Anna, V. Nikulina, Hidehisa yamashita. 2005. Effects of alcohol on spontaneous neuronal oscillations: A combined magneto encephalography and electroencephalography study. Progress in Neuro-Psychopharmacology & Biological 11. Psychiatry. 19, 687-693. 12. J. A. Horne and L. A. Reyner, "Sleep related vehicle accidents," Brit. Med. J., vol. 310, pp. 565–567, 1995. 13. G. Maycock, "Sleepiness and driving: The experience of UK car drivers," J. Sleep Res., vol. 5, pp. 229–237.