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

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

4가지 감정의 뇌파를 이용한 감성평가 기술에 관한 연구

A Study on the Human Sensibility Evaluation Technique Using EEGs of 4 Emotions

  • 김동준 (淸州大學敎 理工大 情報通信工學部) ;
  • 강동기 (淸州大學敎 理工大 電子工學科) ;
  • 김흥환 (淸州大學敎 理工大 電子工學科) ;
  • 이상한 (淸州大學敎 理工大 電子工學科) ;
  • 고한우 (한국표준과학연구원 인간정보 연구그룹) ;
  • 박세진 (한국표준과학연구원 인간정보 연구그룹)
  • 발행 : 2002.11.01
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초록

This paper describes a technique for human sensibility evaluation using EEGs of 4 emotions. The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance suing the proposed method is about 72.6%. This may be promising performance in the human sensibility evaluation.

키워드

참고문헌

  1. 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
  2. T. Yoshida, 'The estimation of mental stress by 1/f frequency fluctuation of EEG', Brain topography, pp.771-777, 1998
  3. 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
  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 Appli-cations in Neural Networks (EANN), pp. 407-414
  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., 1983
  7. 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
  8. H. Matsunaga, H. Nakazawa, '만족감 계측을 위한 기초적 연구', 일본 인간공학, vol. 34-4, pp. 191-201, 1998
  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