Abstract
Effective vigilance level estimation can be used to prevent disastrous accident occurred frequently in high-risk tasks. Brain Computer Interface(BCI) based on ElectroEncephalo-Graph(EEG) is a relatively reliable and convenient mechanism to reflect one’s psychological phenomena. In this paper we propose a new integrated approach to predict human vigilance level, which incorporate an automatically artifact removing pre-process, a novel vigilance quantification method and finally a hierarchical Gaussian Mixed Model(hGMM) to discover the underlying pattern of EEG signals. A reasonable high classification performance (88.46% over 12 data sets) is obtained using this integrated approach. The hGMM is proved to be more powerful against Support Vector Machine(SVM) and Linear Discriminant Analysis(LDA) under complicated probability distributions.
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Gu, JN., Liu, HJ., Lu, HT., Lu, BL. (2011). An Integrated Hierarchical Gaussian Mixture Model to Estimate Vigilance Level Based on EEG Recordings. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_46
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DOI: https://doi.org/10.1007/978-3-642-24955-6_46
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