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| Complexity Analysis for Drinkers' EEG via Wavelet Entropy
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| College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Department of Information Engineering, Anyang Institute of Technology, Anyang 450000, China
School of Information Engineering, Guangdong Medical College, Dongguan 523808, China
College of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
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Abstract This paper investigates the influence of alcohol on brain complexity. Considering electroencephalogram
(EEG) has the nonlinear dynamics characteristic of time-varying and non-stationary, we introduced
the Wavelet Entropy (WE) analysis. We denoise EEG signal by using wavelet decomposition, then
calculated the wavelet entropy of the denoised signal and analyzed the nonlinear complexity of the
signal. The results shows that the EEG wavelet entropy of drinkers' is markedly greater than the EEG
wavelet entropy of normal people's. The EEG complexity of drinkers' is higher and the brain of drinkers'
is in a more chaotic state. In the case of three kinds of external stimulus, we can get the change rule
of the normal people's and alcoholics' EEG, and then analyze the WE and the effects of alcohol on the
brain through a long duration of time. The long-time excessive drinking causes damages to the nerve
cells, which means the human brain consciousness becomes poor, and response is slow.
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| Fund:Project supported by the National Natural Science Funds (60674100) and Nanjing University of Aeronautics
and Astronautics basic scientific research Funds (NS2010069). |
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2014, Vol. 7 
