Multiscale Jensen-Shannon Divergence Based Analysis of Beta Wave Attention EEG

Abstract:

The article adopted the multiscale Jensen - Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states of Beta EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze),we are aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data, finding that there are large amounts of nonlinear time series in the Beta EEG signals. This method is also fully proved that the multi-scale JSD algorithm can be used to analyze EEG signals, attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

• Periodical: Applied Mechanics and Materials (Volume 574)
• Pages: 723-727
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.574.723
• Online since: July 2014
• Authors: Zheng Xia Zhang*, Si Qiu Xu, Er Ning Zhou, Xiao Lin Huang, Jun Wang
• Keywords: Attention, Beta EEG Signals, JSD, Multiscale

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