Research on Attention EEG Based on Jensen-Shannon Divergence

Abstract:

The article adopted the Jensen - Shannon Divergence analysis method for alpha wave EEG complexity analysis, used to quantify the three different status (Eyes closed, count, idle) degree of coupling between acquisition of EEG time series. The algorithm are used to calculate the statistical complexity of alpha wave EEG signals then T test, the results show that the state of eyes closed and idle under the coupling degree between EEG time series, and the state of eyes closed and counting, counting and daze cases EEG time series have significant differences. Thus JSD algorithm can be used to analyze EEG signals attention, statistical complexity can be used as a measure of brain function parameters and would be applied to the auxiliary clinical brain function evaluation in the future.

• Periodical: Advanced Materials Research (Volumes 884-885)
• Pages: 512-515
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.884-885.512
• Online since: January 2014
• Authors: Zheng Xia Zhang, Si Qiu Xu, Er Ning Zhou, Xiao Lin Huang, Jun Wang*
• Keywords: Attention, EEG Signals, JSD

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