Abstract
A novel approach for Microsleep Event detection is presented. This is achieved based on multisensor electroencephalogram (EEG) and electrooculogram (EOG) measurements recorded during an overnight driving simulation task. First, using video clips of the driving, clear Microsleep (MSE) and Non-Microsleep (NMSE) events were identified. Next, segments of EEG and EOG of the selected events were analyzed and features were extracted using Power Spectral Density and Delay Vector Variance. The so obtained features are used in several combinations for MSE detection and classification by means of populations of Learning Vector Quantization (LVQ) networks. Best classification results, with test errors down to 13%, were obtained by a combination of all the recorded EEG and EOG channels, all features, and with feature relevance adaptation using Genetic Algorithms.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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References
Hall, D., Llinas, J.: An introduction to multisensor data fusion. In: Proceedings of the IEEE, vol. 85 (1997)
Polychronopoulos, A., Amditis, A., Bekiaris, E.: Information data flow in AWAKE multi-sensor driver monitoring system. In: Proc. IEEE Intell. Vehicles Symp., pp. 902–906 (2004)
Sagberg, F., Jackson, P., Krüger, H.-P., Muzet, A., Williams, A.J.: Fatigue, sleepiness and reduced alertness as risk factors in driving, Project Report, Transport RTD (2004)
Sommer, D., Hink, T., Golz, M.: Application of Learning Vector Quantization to detect drivers dozing-off. In: European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, pp. 119–123 (2002)
Gautama, T., Van Hulle, M.M., Mandic, D.P.: On the characterization of deterministic/stochastic and linear/nonlinear nature of time series, Technical Report (2004)
Gautama, T., Mandic, D.P., Van Hulle, M.M.: A Novel Method for Determining the Nature of Time Series. IEEE Trans. Biomedical Engineering 51(5), 728–736 (2004)
Gasser, T., Bächer, P., Möcks, J.: Transformations Towards the Normal Distribution of Broad Band Spectral Parameters of the EEG. Electroenceph. Clin. Neurophysiol. 53, 119–124 (1982)
Devroye, L., Gyorfi, L., Lugosi, G.: A probabilistic theory of pattern recognition. Springer, New York (1996)
Golz, M., Sommer, D., Seyfarth, A., Trutschel, U., Moore-Ede, M.: Application of vector-based neural networks for the recognition of beginning Microsleep episodes with an eyetracking system. In: Kuncheva, L.I. (ed.) Computational Intelligence: Methods & Applications, pp. 130–134 (2001)
Jung, T.-P., Stensmo, M., Sejnowski, T., Makeig, S.: Estimating Alertness from the EEG Power Spectrum. IEEE Transactions on Biomedical Engineering 44, 60–69 (1997)
Wilhelm, B., Giedke, H., Lüdtke, H., Bittner, E., Hofmann, A., Wilhelm, H.: Daytime variations in central nervous system activation measured by a pupillographic sleepiness test. J. Sleep Research 10, 1–8 (2001)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Networks 15, 1059–1068 (2002)
Fell, J., Röschke, J., Mann, K., Schaffner, C.: Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalogr. Clin. Neurophysiol. 98, 401–410 (1996)
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Sommer, D., Chen, M., Golz, M., Trutschel, U., Mandic, D. (2005). Fusion of State Space and Frequency- Domain Features for Improved Microsleep Detection. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_120
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DOI: https://doi.org/10.1007/11550907_120
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