Abstract
EEG signal has been regarded as an reliable signal for vigilance estimation for humans who engage in monotonous and attention demanding jobs or tasks, research work in this area have made satisfying progress and most of these methods or algorithms are based on the pattern recognition and clustering principal. Inspired by the HMM(Hiden Markov Model), we proposed a probability method based on the (PSD) Power Spectral Density distribution of the energy changes to estimate the vigilance level of humans using only one ICA(Independent Component Analysis) component of EEG signal. We firstly extract the specific frequency band energy feature using (CWT)Continuous Wavelet Transform, then analyze different vigilance states energy data to get the energy distribution information and vigilance states transformation probability matrix, finally use the energy distribution and vigilance states transformation matrixes to estimate vigilance level. Experiments result show that the proposed method promising and efficiently.
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Yu, H., Lu, HT. (2011). Vigilance Estimation Based on Statistic Learning with One ICA Component of EEG Signal. 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_49
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DOI: https://doi.org/10.1007/978-3-642-24955-6_49
Publisher Name: Springer, Berlin, Heidelberg
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