Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Wavelet Entropy
  2.1. Wavelet Transform
  2.2. Wavelet Energy
  2.3. Wavelet Entropy
 3. WE Performances to Nonlinear Signals
 4. Application Examples
  4.1. Experiment Data
  4.2. Experiment Results and Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jiufu Liu [ College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ]
• Lei Gao [ Department of Information Management, Henan Information and Statistics Vocational College, Zhengzhou 450008, China ]
• Zaihong Zhou [ School of Information Engineering, Guangdong Medical College, Dongguan 523808, China ]
• Haiyang Liu [ College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ]
• Zhengqian Wang [ College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ]
• Wenyuan Liu [ College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ]
• Jianyong Zhou [ College of Electronic Science and Engineering, Southeast University, Nanjing 210096, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605