뇌파 스펙트럼 분석과 베이지안 접근법을 이용한 정서 분류

• 영문명: Emotion Classification Using EEG Spectrum Analysis and Bayesian Approach
• 발행기관: 한국산업경영시스템학회
• 저자명: 정성엽(Seong Youb Chung) 윤현중(Hyun Joong Yoon)
• 간행물 정보: 『산업경영시스템학회지』제37권 제1호, 1~8쪽, 전체 7쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2014.03.31

영문 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.

목차

Abstract

1. 서론

2. 정서 인지 방법

3. 실험 및 구현

4. 실험 결과

5. 결론

References

키워드

Spectrum Analysis Bayesian Approach EEG Emotion Classifier Machine Learning