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- W64797718 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." @default.
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- W64797718 date "2012-01-01" @default.
- W64797718 modified "2023-09-24" @default.
- W64797718 title "A Bayesian Network (BN) Based Probabilistic Solution to Enhance Emotional Ontology" @default.
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- W64797718 doi "https://doi.org/10.1007/978-94-007-5086-9_24" @default.
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