Automatic classification of driving mental fatigue with EEG by wavelet packet energy and KPCA-SVM

International Journal of Innovative Computing, Information and Control

Volumn 7, Issue 3, 2011, Pages 1157-1168

  Zhao, Chunlin ^a,b   Zheng, Chongxun ^a   Zhao, Min ^a   Liu, Jianping ^b   Tu, Yaling ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b ENGINEERING COLLEGE OF ARMED POLICE FORCE   (China)

Author keywords

Driving mental fatigue; EEG; KPCA; SVM; WPE

Indexed keywords

EEG; KPCA; MENTAL FATIGUE; SVM; WPE;

ACCIDENTS; FEATURE EXTRACTION; PATIENT MONITORING; PRINCIPAL COMPONENT ANALYSIS; WAVELET TRANSFORMS;

SUPPORT VECTOR MACHINES;

EID: 79951686865     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. NHTSA, Drowsly Driver Detection and Warning System for Commercial Vehicle Drives: Field Proportional Test Design, Analysis, and Progress, http://www.nhtsa.dot.gov.
2. S. K. L. Lal, A. Craig, P. Boord, L. Kirkup and H. Nguyen, Development of an algorithm for an EEG-based driver fatigue countermeasure, J. Saf. Res., vol.34, no.3, pp.321-328, 2003.
3. M. P. Reed and P. A. Green, Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialling task, Ergonomics, vol.42, no.8, pp.1015-1037, 1999. (Pubitemid 29401736)
4. S. K. L. Lal and A. Craig, A critical review of the psychophysiology of driver fatigue, J. Biol. Psychol., vol.55, no.3, pp.173-194, 2001. (Pubitemid 32181358)
5. L. J. Cao, K. S. Chua, W. K. Chong, H. P. Lee and Q. M. Gu, A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine, Neurocomputing, vol.55, no.1-2, pp.321-336, 2003. (Pubitemid 37336682)
6. A. R. Teixeira, A. M. Tome, K. Stadlthanner and E. W. Lang, KPCA denoising and the pre-image problem revisited, Digit Signal Process, vol.18, no.4, pp.568-580, 2008. (Pubitemid 351759156)
7. A. Kocsor and L. Toth, Kernel-based feature extraction with a speech technology application, IEEE Transactions on Signal Processing, vol.52, no.8, pp.2250-2263, 2004.
8. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Network, vol.12, no.2, pp.181-201, 2001. (Pubitemid 32371478)
9. C. W. Anderson, E. A. Stolz and S. Shamsunder, Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Trans. Biomed. Eng., vol.45, no.3, pp.277-286, 1998. (Pubitemid 28128653)
10. L. Sun, G. Chang and P. J. Beadle, Multiresolution of clinical EEG recordings based on wavelet packet analysis, Proc. of ISNN, LNCS, vol.4492, pp.1168-1176, 2007. (Pubitemid 350270171)
11. Z. Chen, W. Hong and C. Wang, RNA secondary structure prediction with plane pseudoknots based on support vector machine, ICIC Express Letters, vol.3, no.4(B), pp.1411-1416, 2009.
12. S. Mallat, A Wavelet Tour of Signal Processing, 2nd Edition, Academic Press, San Diego, 1999.
13. O. A. Rosso, M. T. Martin and A. Plastino, Brain electrical activity analysis using wavelet-based informational tools, Phys. Stat. Mech. Appl., vol.313, no.3-4, pp.587-608, 2002.
14. S. K. L. Lal and A. Craig, Driver fatigue: Electroencephalography and psychological assessment, Psychophysiology, vol.39, no.3, pp.313-321, 2002. (Pubitemid 34478048)
15. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
16. H. Tamura and K. Tanno, Midpoint validation method for support vector machines with margin adjustment technique, International Journal of Innovative Computing, Information and Control, vol.5, no.11(A), pp.4025-4032, 2009.
17. N. Acir, A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems, Expet. Syst. Appl., vol.31, no.1, pp.150-158, 2006. (Pubitemid 43344272)
18. B. Scholkopf, A. Smola and K. R. Muller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput., vol.10, no.5, pp.1299-1319, 1998. (Pubitemid 128463674)
19. B. Scholkopf and A. J. Smola, Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2001.
20. Y. Xing, X.Wu and Z. Xu, Multiclass least squares auto-correlation wavelet support vector machines, ICIC Express Letters, vol.2, no.4, pp.345-350, 2008.
21. C. C. C. Pang, A. R. M. Upton, G. Shine and M. V. Kamath, A comparison of algorithms for detection of spikes in the electroencephalogram, IEEE Trans. Biomed. Eng., vol.50, no.4, pp.521-526, 2003. (Pubitemid 36438231)
22. I. Clark, R. Biscay, M. Echeverria and T. Virues, Multiresolution decomposition of nonstationary eeg signals - A preliminary-study, Comput. Biol. Med., vol.25, no.4, pp.373-382, 1995.
23. M. E. Smith and A. Gevins, Neurophysiologic monitoring of mental workload and fatigue during operation of a ight simulator, Proc. of the SPIE, vol.5797, pp.116-126, 2005. (Pubitemid 41537694)
24. S. M. Bowyer, L. Hsieh, J. E. Moran, R. A. Young et al., Conversation effects on neural mechanisms underlying reaction time to visual events while viewing a driving scene using MEG, Brain Research, vol.1251, pp.151-161, 2009.
25. A. S. Gevins and R. E. Schaffer, A critical-review of electroencephalographic (Eeg) correlates of higher cortical functions, CRC Crit. Rev. Bioeng., vol.4, no.2, pp.113-164, 1980.
26. J.-M. Lee, C. Yoo, S. W. Choi, P. A. Vanrolleghem and I.-B. Lee, Nonlinear process monitoring using kernel principal component analysis, Chem. Eng. Scie., vol.59, no.1, pp.223-234, 2004. (Pubitemid 38034007)
27. C. J. Stam, Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field, Clin. Neurophysiol., vol.116, no.10, pp.2266-2301, 2005. (Pubitemid 41268636)
28. Y. E. Shao and B.-S. Hsu, Determining the contributors for a multivariate SPC chart signal using artificial neural networks and support vector machine, International Journal of Innovative Computing, Information and Control, vol.5, no.12(B), pp.4899-4906, 2009.