The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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Sensibility Classification Algorithm of EEGs using Multi-template Method

다중 템플릿 방법을 이용한 뇌파의 감성 분류 알고리즘

  • 김동준 (청주대 공대 정보통신공학부)
  • Published : 2004.12.01
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Abstract

This paper proposes an algorithm for EEG pattern classification using the Multi-template method, which is a kind of speaker adaptation method for speech signal processing. 10-channel EEG signals are collected in various environments. The linear prediction coefficients of the EEGs are extracted as the feature parameter of human sensibility. The human sensibility classification algorithm is developed using neural networks. Using EEGs of comfortable or uncomfortable seats, the proposed algorithm showed about 75% of classification performance in subject-independent test. In the tests using EEG signals according to room temperature and humidity variations, the proposed algorithm showed good performance in tracking of pleasantness changes and the subject-independent tests produced similar performances with subject-dependent ones.

Keywords

References

  1. T. Musha, Y. Terasaki, H. A. Haque, and G. A. Ivanisky, 'Feature extraction from EEGs associated with emotions', IntI. Sympo. Artif. Life Robotics (Invited Paper), vol.1 , pp.15-19, 1997 https://doi.org/10.1007/BF02471106
  2. T. Yoshida, 'The estimation of mental stress by 1/f frequency fluctuation of EEG', Brain topography, pp. 771-777, 1998
  3. R. J. Davidson, 'Anterior cerebral asymmetry and the nature of emotion', Brain and Cognition, vol.20, pp.l25-151, 1992 https://doi.org/10.1016/0278-2626(92)90065-T
  4. C. W. Anderson and Z. Sijercic, 'Classification of EEG signals from four subjects during five mental tasks', In Solving Engineering Problems with Neural Networks : Proceedings of the Conference on Engineering Applications in Neural Networks(EANN), pp. 407-414. 1996
  5. J. D. Markel and A. H. Gray, Jr., Linear prediction of Speech, Springer-Verlag Berlin Heidelberg . New York, 1980
  6. S. J. Orfanidis, Optimun Signal Processing : An Introduction, 2nd ed., Macmillan Publishing Co., 1988
  7. S. Furui, Digital Speech Processing, Synthesis, and Recognition, Marcel Dekker, Inc., 1992
  8. T. Yoshida, S. Ohmoto, and S. Kanamura, '1/f frequency-fluctuation of human EEG and emotional changes', Noise in Physical System and 1/f fluctuations, edited by T. Musha, S. Sato and Yamamoto, Ohmsha, Ltd. pp. 719-722, 1991
  9. M. B. Kostyunina and M. A. Kulikov, 'Frequency characteristics of EEG spectra in the emotions', Neuroscience and Behavioral Physiology, vol. 26, no. 4, 1996 https://doi.org/10.1007/BF02359037
  10. T. Musha, S. Kimura, K. I, Kaneko, K. Nishida, K. Sekine, 'Emotion spectrum analysis method(ESAM) for Monitoring the effects of art therapy applied on demented patients', CyberPsychology & Behavior. vol. 3, no. 3, pp. 441-446, 2000 https://doi.org/10.1089/10949310050078904