산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제37권1호
  • /
  • Pages.1-8
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  • 2014
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  • 2005-0461(pISSN)
  • /
  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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뇌파 스펙트럼 분석과 베이지안 접근법을 이용한 정서 분류

Emotion Classification Using EEG Spectrum Analysis and Bayesian Approach

  • 정성엽 (한국교통대학교 기계공학과) ;
  • 윤현중 (대구가톨릭대학교 기계자동차공학부)
  • Chung, Seong Youb (Department of Mechanical Engineering, Korea National University of Transportation) ;
  • Yoon, Hyun Joong (School of Mechanical and Automotive Engineering, Catholic University of Daegu)
  • 투고 : 2013.07.18
  • 심사 : 2013.12.20
  • 발행 : 2014.03.31
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초록

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

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참고문헌

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