Analysis on the Characteristics of Electroencephalogram (EEG) and the Duration of Acupuncture Efficacy, Depending on the Stimulation at the Acupuncture Points

  • Jeong-Hoon Shin
  • Dae-Hyeon Park
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


Acupuncture, one of the crucial areas of the Oriental Medicine, has been used for some thousand years in China, Korea, Japan to treat various diseases. Recently, the application of acupuncture to the therapy of cerebral disorder, such as stroke, Alzheimer’s disease, has come to the limelight as it causes no side effect resulting from the surgery and intake of drug in the Western medicine. However, the acupuncture of the Oriental medicine, a medical technique based on the experience and theory, has not been validated scientifically. Thus, the efficacy of acupuncture in the treatment of disease needs to be supported scientifically like the Western medicine that is based on the scientific ground. To seek the measures that can cope with those requirements, the characteristics of electroencephalogram (EEG) were analyzed depending on the change in the nerve cell and the cerebral blood flow by applying the acupuncture to 10 spots of the palm, the area of hand stimulated (pricked by acupuncture needle) to treat the cerebral disorder in the Oriental medicine, through the BCI (Brain Computer Interface) technology. On that basis, the efficacy of acupuncture and the duration of acupuncture efficacy based on the stimulation with acupuncture needle at acupuncture points were intended to be analyzed in relation to the stimulation of the acupuncture point [2],[3],[4].


electroencephalogram (EEG) acupuncture point duration of the acupuncture efficacy BCI (Brain Computer Interface) 


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  1. 1.
    Hsiu, H., Hsu, W.-C., Hsu, C.L., Huang, S.-M., Hsu, T.-L., Wang, Y.-Y.L.: Spectral analysis on the microcirculatory laser Doppler signal of the acupuncture effect. In: 30th Annual International Conference of the 2008, Engineering in Medicine and Biology Society, IEEE-EMBS 2008, pp. 2916–2919 (August 2008)Google Scholar
  2. 2.
    Li, N., Wang, J., Deng, B., Dong, F.: An analysis of EEG when acupuncture with Wavelet entropy. In: 30th Annual International Conference of the 2008, Engineering in Medicine and Biology Society, IEEE-EMBS 2008, pp. 1108–1111 (August 2008)Google Scholar
  3. 3.
    He, W.-X., Yan, X.-G., Chen, X.-P., Liu, H.: Nonlinear Feature Extraction of Sleeping EEG Signals. In: 27th Annual International Conference of the 2005, Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 4614–4617 (September 2005)Google Scholar
  4. 4.
    Murata, T., Akutagawa, M., Kaji, Y., Shichijou, F.: EEG Analysis Using Moving Average-type Neural Network. In: 30th Annual International Conference of the 2008, Engineering in Medicine and Biology Society, IEEE-EMBS 2008, pp. 169–172 (August 2008)Google Scholar
  5. 5.
    Kaji, Y., Akutagawa, M., Shichijo, F., Nagashino, H., Kinouchi, Y., Nagahiro, S.: EEG analysis using neural networks to detect change of brain conditions during operations. In: IFMBE Proceedings, pp. 1079–1082 (April 2006)Google Scholar
  6. 6.
    Sun, Y., Ye, N., Xu, X.: EEG Analysis of Alcoholics and Controls Based on Feature Extraction. In: The 8th International Conference on Signal Processing (2006)Google Scholar
  7. 7.
    Zhang, S.Z., Kawabata, H., Liu, Z.-Q.: EEG Analysis using Fast Wavelet Transform. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol. 4, pp. 2959–2964 (October 2000)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jeong-Hoon Shin
    • 1
  • Dae-Hyeon Park
    • 1
  1. 1.Dept. of Computer & Information Communication Eng.Catholic University of Dae-GuKorea

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