Complexity Analysis for Drinkers' EEG via Wavelet Entropy (Journal of Fiber Bioengineering and Informatics, 2014)
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 introducedthe Wavelet Entropy (WE) analysis. We denoise EEG signal by using wavelet decomposition, thencalculated the wavelet entropy of the denoised signal and analyzed the nonlinear complexity of thesignal. The results shows that the EEG wavelet entropy of drinkers' is markedly greater than the EEGwavelet 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 ruleof the normal people's and alcoholics' EEG, and then analyze the WE and the effects of alcohol on thebrain through a long duration of time. The long-time excessive drinking causes damages to the nervecells, which means the human brain consciousness becomes poor, and response is slow.