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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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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