Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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A Study on the Emotion State Classification using Multi-channel EEG

다중채널 뇌파를 이용한 감정상태 분류에 관한 연구

  • Kang, Dong-Kee (School of Electronic, Semiconductor, Computer & Communication Eng., Chongju Univ.) ;
  • Kim, Heung-Hwan (School of Electronic, Semiconductor, Computer & Communication Eng., Chongju Univ.) ;
  • Kim, Dong-Jun (School of Electronic, Semiconductor, Computer & Communication Eng., Chongju Univ.) ;
  • Lee, Byung-Chae (Dept. of Medical Information System, Yong-in Songdam College) ;
  • Ko, Han-Woo (Korea Research Institute of Standards and Sciences)
  • 강동기 (청주대학교 전자.정보통신.반도체공학부) ;
  • 김흥환 (청주대학교 전자.정보통신.반도체공학부) ;
  • 김동준 (청주대학교 전자.정보통신.반도체공학부) ;
  • 이병채 (용인송담대학교 의료정보시스템과) ;
  • 고한우 (한국표준과학연구원)
  • Published : 2001.07.18
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Abstract

This study describes the emotion classification using two different feature extraction methods for four-channel EEG signals. One of the methods is linear prediction analysis based on AR model. Another method is cross-correlation coefficients on frequencies of ${\theta}$, ${\alpha}$, ${\beta}$ bands. Using the linear predictor coefficients and the cross-correlation coefficients of frequencies, the emotion classification test for four emotions, such as anger, sad, joy, and relaxation is performed with a neural network. Comparing the results of two methods, it seems that the linear predictor coefficients produce the better results than the cross-correlation coefficients of frequencies for-emotion classification.

Keywords