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The Scientific World Journal
Volume 2014, Article ID 627892, 10 pages
http://dx.doi.org/10.1155/2014/627892
Research Article

EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
2National Electronics and Computer Technology Center, Thailand Science Park, Khlong Luang, Pathum Thani 12120, Thailand

Received 2 May 2014; Revised 30 July 2014; Accepted 30 July 2014; Published 1 September 2014

Academic Editor: Jinshan Tang

Copyright © 2014 Suwicha Jirayucharoensak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Akram, M. K. Metwally, H. Han, H. Jeon, and T. Kim, “A novel P300-based BCI system for words typing,” in Proceedings of the International Winter Workshop on Brain-Computer Interface (BCI '13), pp. 24–25, February 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. R. S. Naveen and A. Julian, “Brain computing interface for wheel chair control,” in Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT '13), pp. 1–5, Tiruchengode, India, July 2013. View at Publisher · View at Google Scholar
  3. F. Sharbrough, G. E. Chatrian, R. P. Lesser, H. Luders, M. Nuwer, and T. W. Picton, “American Electroencephalographic Society guidelines for standard electrode position nomenclature,” Journal of Clinical Neurophysiology, vol. 8, no. 2, pp. 200–202, 1991. View at Google Scholar
  4. Wikipedia, “Electroencephalography,” March 2014, http://en.wikipedia.org/wiki/Electroencephalography.
  5. S. Koelstra, A. Yazdani, M. Soleymani et al., “Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos,” in Proceedings of the International Conference on Brain Informatics, Toronto, Canada, 2010.
  6. M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, “A multimodal database for affect recognition and implicit tagging,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 42–55, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Huang, C. Guan, K. K. Ang, H. Zhang, and Y. Pan, “Asymmetric spatial pattern for EEG-based emotion detection,” in Proceeding of the International Joint Conference on Neural Networks (IJCNN '12), pp. 1–7, Brisbane, Australia, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Chanel, J. J. M. Kierkels, M. Soleymani, and T. Pun, “Short-term emotion assessment in a recall paradigm,” International Journal of Human Computer Studies, vol. 67, no. 8, pp. 607–627, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Nie, X.-W. Wang, L.-C. Shi, and B.-L. Lu, “EEG-based emotion recognition during watching movies,” in Proceedings of the 5th International IEEE/EMBS Conference on Neural Engineering (NER '11), pp. 667–670, Cancun, Mexico, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. X.-W. Wang, D. Nie, and B.-L. Lu, “EEG-based emotion recognition using frequency domain features and support vector machines,” in Neural Information Processing, B.-L. Lu, L. Zhang, and J. Kwok, Eds., vol. 7062, pp. 734–743, Springer, Berlin, Germany, 2011. View at Google Scholar
  11. N. Jatupaiboon, S. Pan-ngum, and P. Israsena, “Real-time EEG-based happiness detection system,” The Scientific World Journal, vol. 2013, Article ID 618649, 12 pages, 2013. View at Publisher · View at Google Scholar
  12. G. Chanel, J. Kronegg, D. Grandjean, and T. Pun, “Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals,” in Multimedia Content Representation, Classification and Security, B. Gunsel, A. Jain, A. M. Tekalp, and B. Sankur, Eds., vol. 4105, pp. 530–537, Springer, Berlin, Germany, 2006. View at Google Scholar
  13. O. AlZoubi, R. A. Calvo, and R. H. Stevens, “Classification of EEG for affect recognition: an adaptive approach,” in AI 2009: Advances in Artificial Intelligence, A. Nicholson and X. Li, Eds., vol. 5866 of Lecture Notes in Computer Science, pp. 52–61, Springer, Berlin, Germany, 2009. View at Google Scholar
  14. G. Chanel, C. Rebetez, M. Bétrancourt, and T. Pun, “Emotion assessment from physiological signals for adaptation of game difficulty,” IEEE Transactions on Systems, Man, and Cybernetics A Systems and Humans, vol. 41, no. 6, pp. 1052–1063, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. U. Wijeratne and U. Perera, “Intelligent emotion recognition system using electroencephalography and active shape models,” in Proceedings of the 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES '12), pp. 636–641, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Y. Chung and H. J. Yoon, “Affective classification using Bayesian classifier and supervised learning,” in Proceedings of the 12th International Conference on Control, Automation and Systems (ICCAS '12), pp. 1768–1771, October 2012. View at Scopus
  17. G. E. Hinton, S. Osindero, and Y. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  18. D. F. Wulsin, J. R. Gupta, R. Mani, J. A. Blanco, and B. Litt, “Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement,” Journal of Neural Engineering, vol. 8, no. 3, Article ID 036015, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Längkvist, L. Karlsson, and A. Loutfi, “Sleep stage classification using unsupervised feature learning,” Advances in Artificial Neural Systems, vol. 2012, Article ID 107046, 9 pages, 2012. View at Publisher · View at Google Scholar
  20. S. Koelstra, C. Mühl, M. Soleymani et al., “DEAP: a database for emotion analysis; using physiological signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18–31, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. J. D. Morris, “SAM: the self-assessment manikin. An efficient cross-cultural measurement of emotion response,” Jounal of Advertising Research, vol. 35, no. 8, pp. 63–68, 1995. View at Google Scholar
  22. M. Spüler, W. Rosenstiel, and M. Bogdan, “Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface,” EURASIP Journal on Advances in Signal Processing, vol. 2012, article 129, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. I. T. Jolliffe, Principal Component Analysis, Springer, New York, NY. USA, 1986. View at Publisher · View at Google Scholar · View at MathSciNet
  24. P. Baldi and K. Hornik, “Neural networks and principal component analysis: learning from examples without local minima,” Neural Networks, vol. 2, no. 1, pp. 53–56, 1989. View at Publisher · View at Google Scholar · View at Scopus
  25. C. Chang and C. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, article 27, no. 3, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. K. Li, X. Li, Y. Zhang et al., “Affective state recognition from EEG with deep belief networks,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2013.
  27. F. Lotte and C. Guan, “Learning from other subjects helps reducing brain-computer interface calibration time,” in Proceedings of the 35th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '10), pp. 614–617, Dallas, Tex, USA, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. W. Samek, F. C. Meinecke, and K. Muller, “Transferring subspaces between subjects in brain—computer interfacing,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 8, pp. 2289–2298, 2013. View at Publisher · View at Google Scholar · View at Scopus