Alpha Wave Attention EEG Analysis Based on the Multiscale Jensen-Shannon Divergence

Abstract:

The article adopted the multiscale Jensen - Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status (eyes closed, count, in a daze) acquisition of Alpha EEG time series, which shows three different states of Alpha EEG time series having significant differences. In each state of the three different states (eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series proxy data, finding that there are large amounts of nonlinear time series in the Alpha EEG signals. This method is also fully proved that the multiscale 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 595)
• Pages: 269-273
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.595.269
• Online since: July 2014
• Authors: Zheng Xia Zhang*, Si Qiu Xu, Er Ning Zhou, Xiao Lin Huang, Jun Wang
• Keywords: Alpha EEG Signals, Attention, JSD, Multiscale

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