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  • 标题:Complexity Comparison for Drinkers' and Normal People's EEG Using Wavelet Entropy
  • 本地全文:下载
  • 作者:Jiufu Liu ; Lei Gao ; Zaihong Zhou
  • 期刊名称:International Journal of Hybrid Information Technology
  • 印刷版ISSN:1738-9968
  • 出版年度:2015
  • 卷号:8
  • 期号:8
  • 页码:47-56
  • DOI:10.14257/ijhit.2015.8.8.04
  • 出版社:SERSC
  • 摘要:This paper investigates the influence of alcohol on brain complexity. Considering electro-encephalogram (EEG) has the nonlinear dynamics characteristic of time-varying and non-stationary, we introduce the wavelet entropy (WE) analysis. We denoise EEG signal by using wavelet decomposition, then calculate the wavelet entropy of the denoised signal and analyze the nonlinear complexity. In 64 conductive poles experiments and in different stimulus experiments for FP2 electrode's EEG, the drinkers' EEG wavelet entropy is greater than normal people's. The wavelet entropy of every conductive pole of drinkers' or normal persons' is inconformity.
  • 关键词:EEG; wavelet transform; wavelet entropy; complexity
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