Research on Attention EEG Based on the Multiscale Jensen-Shannon Divergence

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 EEG time series. It showed that three different states of EEG time series have 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 shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence 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 529)
• Pages: 675-678
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.529.675
• Online since: June 2014
• Authors: Zheng Xia Zhang, Si Qiu Xu, Er Ning Zhou, Xiao Lin Huang, Jun Wang*
• Keywords: Attention, EEG Signals, JSD, Multiscale

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