Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84775
Title: EEG-based dominance level recognition for emotion-enabled interaction
Authors: Liu, Yisi.
Sourina, Olga.
Issue Date: 2012
Abstract: 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.
URI: https://hdl.handle.net/10356/84775
http://hdl.handle.net/10220/12944
DOI: http://dx.doi.org/10.1109/ICME.2012.20
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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