A Bayesian Network (BN) Based Probabilistic Solution to Enhance Emotional Ontology

  • Xiaowei Zhang
  • Daniel Cao
  • Philip Moore
  • Jing Chen
  • Lin Zhou
  • Yang Zhou
  • Xu Ma
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 182)

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.

Keywords

emotion emotion recognition Ontology Bayesian Networks 

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References

  1. 1.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human computer interaction. IEEE Signal Processing Magazine 18, 32–80 (2001)CrossRefGoogle Scholar
  2. 2.
    Bodenreider, O.: Biomedical ontologies in action: role in knowledge management, data integration and decision support. IMIA Yearbook of Medical Informatics, 67–79 (2008)Google Scholar
  3. 3.
    Zhang, X.W., Hu, B., Moore, P., Chen, J., Zhou, L.: Emotiono: An Ontology with Rule-Based Reasoning for Emotion Recognition. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part II. LNCS, vol. 7063, pp. 89–98. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Tiedens, L.Z., Linton, S.: Judgment under emotional uncertainty: The effects of specific emotions on information processing. Journal of Personality and Social PsychologyGoogle Scholar
  5. 5.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kauffman Publishers (1988)Google Scholar
  6. 6.
    Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, London (2000)MATHGoogle Scholar
  7. 7.
    Hollings, R.: Emotion recognition using brain activity. Department of Mediamatics. Delft University of Technology (2008)Google Scholar
  8. 8.
    Wooldridge, M.: Intelligent agents. In: Gerhard, W. (ed.) Multi-agent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 27–78. The MIT Press (1999)Google Scholar
  9. 9.
    Ding, Z., Peng, Y., Pan, R.: BayesOWL: Uncertainty modeling in semantic web ontologies. In: Ma, Z. (ed.) Soft Computing in Ontologies and Semantic Web. Springer (2005)Google Scholar
  10. 10.
    Yang, Y.: A Framework for Decision Support Systems Adapted to Uncertain Knowledge. Ph. D thesis. University of Karlsruhe (TH) (2007)Google Scholar
  11. 11.
    Mish, F.C.: Webster’s Ninth New Collegiate Dictionary. Merriam Webster. Spring, MA (1983)Google Scholar
  12. 12.
    Russel, J.A., Lewicka, M., Niit, T.: A Cross-Cultural Study of a Circumplex Model of Affect. Journal of Personality and Social Psychology 57, 848–856 (1989)CrossRefGoogle Scholar
  13. 13.
    Damásio, A.R.: Emotions and the Human Brain. Iowa. Department of Neurology, USA (1999)Google Scholar
  14. 14.
    Cohen, I., Sebe, N., Cozman, F., Cirelo, M., Huang, T.: Learning Bayesian network classifiers for facial expression recognition using both labeled and unlabeled data. Computer Vision and Pattern Recognition (2003)Google Scholar
  15. 15.
    Ball, G., Breese, J.: Modeling the Emotional State of Computer Users. In: Workshop on ’Attitude, Personality and Emotions in User-Adapted Interaction’, UM 1999, Canada (1999)Google Scholar
  16. 16.
    López, J.M., Gil, R., García, R., Cearreta, I., Garay, N.: Towards an Ontology for Describing Emotions. In: Lytras, M.D., Damiani, E., Tennyson, R.D. (eds.) WSKS 2008. LNCS (LNAI), vol. 5288, pp. 96–104. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Deborah, L.M., Frank, V.H.: OWL Web Ontology Language Overview. W3C Recommendation (2004), http://www.w3.org/TR/owl-features
  18. 18.
  19. 19.
    Koelstra, S., Muehl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: A Database for Emotion Analysis using Physiological Signals. IEEE Transaction on Affective Computing (2011)Google Scholar
  20. 20.
    Scherer, K.R.: What are emotions? and how can they be measured. Social Science Information 44(4), 695–729 (2005)CrossRefGoogle Scholar
  21. 21.
    Quilan, R.J.: C4.5: Programs for Machine Learning. Morgan Kauffman, San Mateo (1993)Google Scholar
  22. 22.
    Kohavi, R.: Scaling up the accuracy of naive-Bayes classifiers: A decision-tree hybrid. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207. AAAI Press, Portland (1996)Google Scholar
  23. 23.
    Bouckaert, R.: Bayesian Network Classifiers in WEKA. Technical Report, Department of Computer Science. Waikato University, Hamilton, NZ (2005)Google Scholar
  24. 24.
    WEKA 3: Data Mining Software in Java, http://www.cs.waikato.ac.nz/ml/weka/
  25. 25.
    Netica: Bayesian network development software, http://www.norsys.com/
  26. 26.
    Frantzidis, C.A., et al.: On the classification of emotional bio-signals evoked while viewing affective pictures: An integrated data-mining based approach for healthcare applications. IEEE Trans. on Information Technique. in Biomedicine 14(2), 309–318 (2010)CrossRefGoogle Scholar
  27. 27.
    Hu, B., Majoe, D., Ratcliffe, M., Qi, Y., Zhao, Q., Peng, H., Fan, D., Zheng, F., Jackson, M., Moore, P.: EEG-based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges. IEEE Intelligent Systems (2011)Google Scholar
  28. 28.
    Hu, B., Moore, P., Wan, J.: Ontology Based Mobile Monitoring and Treatment against Depression. Wireless Communications and Mobile Computing, Special Issue on Pervasive Computing Technology and its Applications, 1–16 (2008)Google Scholar
  29. 29.
    Hu, B., Hu, B.: On Capturing Semantics in Ontology Mapping. World Wide Web 11(3), 361–385 (2008)CrossRefGoogle Scholar
  30. 30.
    Moore, P., Hu, B., Wan, J.: Smart-Context: A Context Ontology for Pervasive Mobile Computing. Computer Journal 53(2), 191–207 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Xiaowei Zhang
    • 1
  • Daniel Cao
    • 1
  • Philip Moore
    • 2
  • Jing Chen
    • 1
  • Lin Zhou
    • 1
  • Yang Zhou
    • 1
  • Xu Ma
    • 1
  1. 1.The School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.The School of Computing, Telecommunications and NetworksBirmingham City UniversityBirminghamUK

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