지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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The emotion is deeply affected by human behavior and cognitive process, so it is important to do research about the emotion. However, the emotion is ambiguous to clarify because of different ways of life pattern depending on each individual characteristics. To solve this problem, we use not only physiological signal for objective analysis but also hybrid unsupervised-supervised learning classifier for automatic emotion detection. The hybrid emotion classifier is composed of K-means, genetic algorithm and support vector machine. We acquire four different kinds of physiological signal including electroencephalography(EEG), electrocardiography(ECG), galvanic skin response(GSR) and skin temperature(SKT) as well as we use 15 features extracted to be used for hybrid emotion classifier. As a result, hybrid emotion classifier(80.6%) shows better performance than SVM(31.3%).
목차
1. 서론
2. 실험
3. 특징추출 및 분류기 설계
4. 결과
5. 결론
References
참고문헌 (15)
참고문헌 신청
1. [단행본] - Dalgleish, Tim / 1999 / Handbook of cognition and emotion / Wiley
2. [학술대회논문] - Hosseini, Seyyed Abed / 2010 / Emotional stress recognition system using EEG and psychophysiological signals: Using new labelling process of EEG signals in emotional stress state / Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on
3. [학술대회논문] - Guang-yuan, Liu / 2009 / Emotion recognition of physiological signals based on adaptive hierarchical genetic algorithm / 2009 WRI World Congress on Computer Science and Information Engineering 4
4. [기타자료] - WooJin Chio / 2007 / A classification analysis of negative emotion based on PPG signal using Fuzzy-GA
5. [학술지(정기간행물)] - Murugappan, Murugappan / 2010 / Classification of human emotion from EEG using discrete wavelet transform / Journal of Biomedical Science and Engineering 3 (04) : 390 ~
2. [학술대회논문] - Hosseini, Seyyed Abed / 2010 / Emotional stress recognition system using EEG and psychophysiological signals: Using new labelling process of EEG signals in emotional stress state / Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on
3. [학술대회논문] - Guang-yuan, Liu / 2009 / Emotion recognition of physiological signals based on adaptive hierarchical genetic algorithm / 2009 WRI World Congress on Computer Science and Information Engineering 4
4. [기타자료] - WooJin Chio / 2007 / A classification analysis of negative emotion based on PPG signal using Fuzzy-GA
5. [학술지(정기간행물)] - Murugappan, Murugappan / 2010 / Classification of human emotion from EEG using discrete wavelet transform / Journal of Biomedical Science and Engineering 3 (04) : 390 ~
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UCI(KEPA) : I410-ECN-0101-2015-500-002602268