Although sleep and wake can be easily distinguished by using EEG, how to detect a drowsy state before subject falls asleep is of more importance in avoiding fatal consequences in accidents behind steering wheel caused by low level vigilance. Starting with the classical problem of difference between wake and sleep, we propose a method based on probabilistic principle component analysis (PPCA) and succeed in detecting drowsiness distinguished from wake and sleep based on EEG.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
George, CF., Boudreau, AC., Smiley, A.: Simulated driving performance in patients with obstructive sleep apnea. American Journal of Respiratory and Critical Care Medicine. 154(1), 175-181 (1996)
Dement, W., Kleitman, N.: Cyclic variations in EEG during sleep and their relation to eye movements, body motility, and dreaming. Electroencephalography and Clinical Neurophysiology. Suppl. 9(4), 673-690 (1957)
Hori, T.: Electrodermal and electro-oculographic activity in a hypnagogic state. Psychophysi- ology. 19(6), 668-672 (1982)
Makeig, S., Jung, TP.: Changes in alertness are a principal component of variance in the EEG spectrum. Neuroreport. 7(1), 213-216 (1995)
M üller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clinical Neurophysiology. 110(5), 787-798 (1999)
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined handmovement. IEEE Transactions on Rehabilitation Engineering. 8(4), 441-446 (2000)
Oken, BS., Salinsky, MC., Elsas, SM.: Vigilance, alertness, or sustained attention: physiological basis and measurement. Clinical Neurophysiology. 117(9), 1885-1901 (2006)
Makeig, S., Jung, TP., Sejnowski, TJ.: Using feedforward neural networks to monitor alertness from changes in EEG correlation and coherence. Advances in Neural Information Processing Systems. 8, 931-937 (1996)
Xu, W., Guan, C., Siong, CE., Ranganatha, S., Thulasidas, M., Wu, J.: High accuracy classification of EEG signal. Proceedings of 17th International Conference on Pattern Recognition. 391-394 (2004)
Shi, LC., Yu, H., Lu, BL.: Semi-supervised clustering for vigilance analysis based on EEG. Proceedings of 20th International Joint Conference on Neural Networks. 1518-1523 (2007)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society. 61(3), 611-622 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science + Business Media B.V
About this paper
Cite this paper
Fu, JW., Li, M., Lu*, BL. (2008). Detecting Drowsiness in Driving Simulation Based on EEG. In: Mahr, B., Huanye, S. (eds) Autonomous Systems – Self-Organization, Management, and Control. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8889-6_3
Download citation
DOI: https://doi.org/10.1007/978-1-4020-8889-6_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8888-9
Online ISBN: 978-1-4020-8889-6
eBook Packages: EngineeringEngineering (R0)