EEG-based dominance level recognition for emotion-enabled interaction
Emotions recognized from Electroencephalogram (EEG) could reflect the real "inner" feelings of the human. Recently, research on real-time emotion recognition received more attention since it could be applied in games, e-learning systems or even in marketing. EEG signal can be divided into...
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sg-ntu-dr.10356-847752020-03-07T13:24:45Z EEG-based dominance level recognition for emotion-enabled interaction Liu, Yisi. Sourina, Olga. School of Electrical and Electronic Engineering IEEE International Conference on Multimedia and Expo (2012 : Melbourne, Australia) Emotions recognized from Electroencephalogram (EEG) could reflect the real "inner" feelings of the human. Recently, research on real-time emotion recognition received more attention since it could be applied in games, e-learning systems or even in marketing. EEG signal can be divided into the delta, theta, alpha, beta, and gamma waves based on their frequency bands. Based on the Valence-Arousal-Dominance emotion model, we proposed a subject-dependent algorithm using the beta/alpha ratio to recognize high and low dominance levels of emotions from EEG. Three experiments were designed and carried out to collect the EEG data labeled with emotions. Sound clips from International Affective Digitized Sounds (IADS) database and music pieces were used to evoke emotions in the experiments. Our approach would allow real-time recognition of the emotions defined with different dominance levels in Valence-Arousal-Dominance model. 2013-08-02T07:17:54Z 2019-12-06T15:51:00Z 2013-08-02T07:17:54Z 2019-12-06T15:51:00Z 2012 2012 Conference Paper https://hdl.handle.net/10356/84775 http://hdl.handle.net/10220/12944 10.1109/ICME.2012.20 en |
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Emotions recognized from Electroencephalogram (EEG) could reflect the real "inner" feelings of the human. Recently, research on real-time emotion recognition received more attention since it could be applied in games, e-learning systems or even in marketing. EEG signal can be divided into the delta, theta, alpha, beta, and gamma waves based on their frequency bands. Based on the Valence-Arousal-Dominance emotion model, we proposed a subject-dependent algorithm using the beta/alpha ratio to recognize high and low dominance levels of emotions from EEG. Three experiments were designed and carried out to collect the EEG data labeled with emotions. Sound clips from International Affective Digitized Sounds (IADS) database and music pieces were used to evoke emotions in the experiments. Our approach would allow real-time recognition of the emotions defined with different dominance levels in Valence-Arousal-Dominance model. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Liu, Yisi. Sourina, Olga. |
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Conference or Workshop Item |
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Liu, Yisi. Sourina, Olga. |
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Liu, Yisi. Sourina, Olga. EEG-based dominance level recognition for emotion-enabled interaction |
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Liu, Yisi. |
title |
EEG-based dominance level recognition for emotion-enabled interaction |
title_short |
EEG-based dominance level recognition for emotion-enabled interaction |
title_full |
EEG-based dominance level recognition for emotion-enabled interaction |
title_fullStr |
EEG-based dominance level recognition for emotion-enabled interaction |
title_full_unstemmed |
EEG-based dominance level recognition for emotion-enabled interaction |
title_sort |
eeg-based dominance level recognition for emotion-enabled interaction |
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2013 |
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https://hdl.handle.net/10356/84775 http://hdl.handle.net/10220/12944 |
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