Abstract
Recognizing an emotional context created using human bio-signals has gained traction in contemporary applications. The current emotional ontology however cannot handle probabilistic information in the emotion recognition process. The primary goal of this research is to utilize a Bayesian Network into the study of EEG-based emotion recognition to address the probabilistic context data. The work is based our previous emotion ontology prototype ‘Emotiono’; the EEG dataset for evaluating its performance being extracted from ’DEAP’ which an open multimodal database for emotion analysis. With 10-fold data in validation the average classification rate using the posited method reaches 86.8 % for Arousal and 85.9 % for Valence in the two dimensional emotion recognition processes.
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Zhang, X. et al. (2012). A Bayesian Network (BN) Based Probabilistic Solution to Enhance Emotional Ontology. In: Park, J., Jin, Q., Sang-soo Yeo, M., Hu, B. (eds) Human Centric Technology and Service in Smart Space. Lecture Notes in Electrical Engineering, vol 182. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5086-9_24
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DOI: https://doi.org/10.1007/978-94-007-5086-9_24
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