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Liu, Y., Sourina, O., & Nguyen, M. K. (2011). Real-time EEG-based emotion recognition and its applications. In M. L. Gavrilova, C. J. K. Tan, A. Sourin & O. Sourina (Eds), Transactions on Computational Science: Special Issue on Cyberworlds Vol. XII, (pp. 256–277). Berlin: Springer. 
Added by: sirfragalot (04/23/2014 08:04:01 AM)   Last edited by: sirfragalot (04/23/2014 08:09:30 AM)
Resource type: Book Article
DOI: 10.1007/978-3-642-22336-5_13
ID no. (ISBN etc.): 978-3-642-22335-8
BibTeX citation key: Liu2011
View all bibliographic details
Categories: General
Keywords: Emotion, Psychophysiology
Creators: Gavrilova, Liu, Nguyen, Sourin, Sourina, Tan
Publisher: Springer (Berlin)
Collection: Transactions on Computational Science: Special Issue on Cyberworlds
Views: 4/221
Since emotions play an important role in the daily life of human beings, the need and importance of automatic emotion recognition has grown with increasing role of human computer interface applications. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of “inner” emotions from electroencephalogram (EEG) signals. We propose real-time fractal dimension based algorithm of quantification of basic emotions using Arousal-Valence emotion model. Two emotion induction experiments with music stimuli and sound stimuli from International Affective Digitized Sounds (IADS) database were proposed and implemented. Finally, the real-time algorithm was proposed, implemented and tested to recognize six emotions such as fear, frustrated, sad, happy, pleasant and satisfied. Real-time applications were proposed and implemented in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called “emotion dimension” to human computer interfaces. An EEG-enabled music therapy site was proposed and implemented. The music played to the patients helps them deal with problems such as pain and depression. An EEG-based web-enable music player which can display the music according to the user’s current emotion states was designed and implemented.
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