Abstract
Vigilance is an ability to maintain concentrated attention on a particular event or target stimulus. Monitoring tasks require certainly high vigilance to properly detect rare occurrence or accurately respond to stimulation. Changes in vigilance can be reflected by EEG signal, so vigilance levels can be classified based on features extracted from EEG. Up to now, power spectral density was commonly employed as features to differentiate between vigilance levels in majority of previous studies. To the best of our knowledge, multifractal attributes for vigilance differentiation have not been exploited, and their feasibility still need to be investigated. In this study, we first extracted multifractal attributes based on wavelet leaders, and then selected statistically significant distinct attributes for the following classification (two vigilance levels). According to the results, classification accuracy was improved with increase of time window used for feature extraction. When time window was increased to 50 s, an averaged accuracy of 91.67 % was achieved, and accuracies for all subjects were higher than 85 %. Our results suggest that multifractal attributes are promising for vigilance differentiation.
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References
Gu, J.N., Liu, H.J., Lu, H.T., Lu, B.L.: An integrated hierarchical gaussian mixture model to estimate vigilance level based on EEG recordings. In: Lu, B.L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part I. LNCS, vol. 7062, pp. 380–387. Springer, Heidelberg (2011)
Li, W., He, Q.C., Fan, X.M., Fei, Z.M.: Evaluation of driver fatigue on two channels of EEG data. Neurosci. Lett. 506, 235–239 (2012)
Trejo, L.J., Kubitz, K., Rosepal, R., Kochavi, R.L., Matthews, B.L., Montgomery, L.D.: EEG-based Estimation and Classification of Mental Fatigue Leonard, pp. 1–44 (2009)
Yu, Z.E., Kuo, C.C., Chou, C.H., Yen, C.T., Chang, F.: A machine learning approach to classify vigilance states in rats. Expert Syst. Appl. 38, 10153–10160 (2011)
Li, J., Struzik, Z., Zhang, L., Cichocki, A.: Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing 165, 23–31 (2015)
Lin, C.T., Chuang, C.H., Huang, C.S., Tsai, S.F., Lu, S.W., Chen, Y.H., Ko, L.W.: Wireless and wearable EEG system for evaluating driver vigilance. IEEE Trans. Biomed. Circ. Syst. 8, 165–176 (2014)
Shi, L.-C., Lu, B.-L.: EEG-based vigilance estimation using extreme learning machines. Neurocomputing 102, 135–143 (2012)
Li, J., Cichocki, A.: Deep learning of multifractal attributes from motor imagery induced EEG. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 503–510. Springer, Heidelberg (2014)
He, P., Wilson, G., Russell, C.: Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med. Biol. Eng. Comput. 42, 407–412 (2004)
De Clercq, W., Vergult, A., Vanrumste, B., Van Paesschen, W., Van Huffel, S.: Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans. Biomed. Eng. 53, 2583–2587 (2006)
Jaffard, S., Lashermes, B., Abry, P.: Wavelet leaders in multifractal analysis. In: Wavelet Analysis and Applications, pp. 201–246 (2007)
Wendt, H., Abry, P.: Multifractality tests using bootstrapped wavelet leaders. IEEE Trans. Sig. Process. 55, 4811–4820 (2007)
Wendt, H., Abry, P.: Bootstrap for multifractal analysis. In: Proceedings of 2006 IEEE International Conference Acoustic Speech Signal Process, vol. 3, pp. 38–48 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Dockree, P.M., Kelly, S.P., Foxe, J.J., Reilly, R.B., Robertson, I.H.: Optimal sustained attention is linked to the spectral content of background EEG activity: greater ongoing tonic alpha (10 Hz) power supports successful phasic goal activation. Euro. J. Neurosci. 25, 900–907 (2007)
Li, J., Liang, J., Zhao, Q., Li, J., Hong, K., Zhang, L.: Design of assistive wheelchair system directly steered by human thoughts. Int. J. Neural Syst. 23, 1350013 (2013)
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This paper is supported by the Singapore Ministry of Defence, Singapore (Grant No. 9011103788).
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Li, J., Prasad, I., Dauwels, J., Thakor, N.V., AI-Nashash, H. (2015). Vigilance Differentiation from EEG Complexity Attributes. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_24
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DOI: https://doi.org/10.1007/978-3-319-26561-2_24
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