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Emotion Interaction With Virtual Reality Using Hybrid Emotion Classification Technique Toward Brain Signals

Kolivand, H, Abuhashish, F, Zraqou, J, Alkhodour, W and Sunar, MS (2015) Emotion Interaction With Virtual Reality Using Hybrid Emotion Classification Technique Toward Brain Signals. International Journal of Computer Science & Information Technology, 7 (2). ISSN 0975-4660

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Human computer interaction (HCI) considered main aspect in virtual reality (VR) especially in the context of emotion, where users can interact with virtual reality through their emotions and it could be expressed in virtual reality. Last decade many researchers focused on emotion classification in order to employ emotion in interaction with virtual reality, the classification will be done based on Electroencephalogram (EEG) brain signals. This paper provides a new hybrid emotion classification method by combining self- assessment, arousal valence dimension and variance of brain hemisphere activity to classify users’ emotions. Self-assessment considered a standard technique used for assessing emotion, arousal valence emotion dimension model is an emotion classifier with regards to aroused emotions and brain hemisphere activity that classifies emotion with regards to right and left hemisphere. This method can classify human emotions, two basic emotions is highlighted i.e. happy and sad. EEG brain signals are used to interpret the users’ emotional. Emotion interaction is expressed by 3D model walking expression in VR. The results show that the hybrid method classifies the highlighted emotions in different circumstances, and how the 3D model changes its walking style according to the classified users’ emotions. Finally, the outcome is believed to afford new technique on classifying emotions with feedback through 3D virtual model walking expression.

Item Type: Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Academy & Industry Research Collaboration Center (AIRCC)
Date Deposited: 28 Feb 2017 10:13
Last Modified: 04 Sep 2021 11:52
DOI or ID number: 10.5121/ijcsit.2015.7214
URI: https://researchonline.ljmu.ac.uk/id/eprint/5716
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