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A Study on Comfortableness Evaluation Technique of Chairs using Electroencephalogram  

김동준 (청주대학교 이공대 정보통신 공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.52, no.12, 2003 , pp. 702-707 More about this Journal
Abstract
This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.
Keywords
human sensibility evaluation; linear prediction; neural network; comfortableness;
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1 R. J. Davidson, 'Anterior cerebral asymmetry and the nature of emotion', Brain and Cognition, vol.20, pp.125-151, 1992   DOI   ScienceOn
2 T. Yoshida, 'The estimation of mental stress by 1/f frequency fluctuation of EEG', Brain topography, pp.771-777, 1998
3 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
4 J. D. Markel and A. H. Gray, Jr., Linear Prediction of Speech, Springer-Verlag. Berlin Heidelberg.New York, 1980
5 H. Matsunaga and H. Nakazawa, '만족감 계측을 위한 기초적 연구', 일본 인간공학, vol.34-4, pp.191-20l, 1998
6 T, Musha, Y. Terasaki, H. A. Haque, and G. A. Ivanisky, 'Feature extraction from EEGs associated with emotions', Intl. Sympo, Artif. Life Robotics (Invited Paper), vol.1, pp.15-19, 1997   DOI
7 S. J. Orfanidis, Optimun Signal Processing : An Introduction, 2nd ed., Macmillan Publishing Co., 1988
8 이석필, '인공지능 기법에 의한 근전도 신호의 패턴 인식에 관한 연구', 연세대학교 박사학위 논문, pp.16-17, 1997
9 T. Musha, S. Kimura, K. I, Kaneko, K. Nishida, and 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   DOI   ScienceOn
10 M. T. Hagan, H. B. Demuth, and M. Beale, Neural Network Design, PWS Publishing Co., 1996