- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
The Scientific World Journal
Volume 2013 (2013), Article ID 618649, 12 pages
Real-Time EEG-Based Happiness Detection System
1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
2National Electronics and Computer Technology Center, Pathumthani 12120, Thailand
Received 3 June 2013; Accepted 15 July 2013
Academic Editors: B.-W. Chen, S. Hsieh, and C.-H. Wu
Copyright © 2013 Noppadon Jatupaiboon 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.
- A. Luneski, E. Konstantinidis, and P. D. Bamidis, “Affective medicine: a review of affective computing efforts in medical informatics,” Methods of Information in Medicine, vol. 49, no. 3, pp. 207–218, 2010.
- J. Helliwell, R. layard, and J. Sachs, “World Happiness Report,” http://www.earth.columbia.edu/sitefiles/file/Sachs%20Writing/2012/World%20Happiness%20Report.pdf.
- S. Lyubomirsky, L. King, and E. Diener, “The benefits of frequent positive affect: does happiness lead to success?” Psychological Bulletin, vol. 131, no. 6, pp. 803–855, 2005.
- H. Gunes and M. Pantic, “Automatic, dimensional and continuous emotion recognition,” International Journal of Synthetic Emotions, vol. 1, pp. 68–99, 2010.
- J. W. Papez, “A proposed mechanism of emotion,” Archives of Neurology and Psychiatry, vol. 38, no. 4, pp. 725–743, 1937.
- P. D. MacLean, “Some psychiatric implications of physiological studies on frontotemporal portion of limbic system (Visceral brain),” Electroencephalography and Clinical Neurophysiology, vol. 4, no. 4, pp. 407–418, 1952.
- 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.
- E. Niedermeyer and F. L. da Silva, Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 2004.
- Wikipedia, “Electroencephalography,” http://en.wikipedia.org/wiki/Electroencephalography.
- 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.
- Y. P. Lin, C. H. Wang, T. L. Wu, S. K. Jeng, and J. H. Chen, “Support vector machine for EEG signal classification during listening to emotional music,” in Proceedings of the 10th IEEE Workshop on Multimedia Signal Processing (MMSP '08), pp. 127–130, Cairns, Australia, October 2008.
- P. Ekman and W. Friesen, “Measuring facial movement with the facial action coding system,” in Emotion in the Human Face, Cambridge University Press, New York, NY, USA, 2nd edition, 1982.
- J. A. Russell, “A circumplex model of affect,” Journal of Personality and Social Psychology, vol. 39, no. 6, pp. 1161–1178, 1980.
- R. Horlings, Emotion Recognition Using Brain Activity, Department of Mediamatics, Delft University of Technology, 2008.
- M. M. Bradley, P. J. Lang, and B. N. Cuthbert, International Affective Picture System (IAPS): Digitized Photographs, Instruction Manual and Affective Ratings, University of Florida, Gainesville, Fla, USA, 2005.
- M. M. Bradley and P. J. Lang, The International Affective Digitized Sounds (IADS-2): Affective Ratings of Sounds and Instruction Manual, University of Florida, Gainesville, Fla, USA, 2nd edition, 2007.
- E. S. Dan-Glauser and K. R. Scherer, “The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance,” Behavior Research Methods, vol. 43, no. 2, pp. 468–477, 2011.
- T. Baumgartner, M. Esslen, and L. Jäncke, “From emotion perception to emotion experience: emotions evoked by pictures and classical music,” International Journal of Psychophysiology, vol. 60, no. 1, pp. 34–43, 2006.
- D. Bos, “EEG-based emotion recognition,” http://hmi.ewi.utwente.nl/verslagen/capita-selecta/CS-Oude_Bos-Danny.pdf.
- M. Li and B. L. Lu, “Emotion classification based on gamma-band EEG,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 1223–1226, Minneapolis, Minn, USA, September 2009.
- 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.
- Z. Khalili and M. H. Moradi, “Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '09), pp. 1571–1575, Atlanta, Ga, USA, June 2009.
- 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.
- Y. P. Lin, C. H. Wang, T. P. Jung et al., “EEG-based emotion recognition in music listening,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 7, pp. 1798–1806, 2010.
- 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 Proceeding of the International Conference on Brain Informatics (BI '10), pp. 89–100, Toronto, Canada, 2010.
- R. Khosrowabadi, H. C. Quek, A. Wahab, and K. K. Ang, “EEG-based emotion recognition using self-organizing map for boundary detection,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 4242–4245, Istanbul, Turkey, August 2010.
- M. Murugappan, R. Nagarajan, and S. Yaacob, “Combining spatial filtering and wavelet transform for classifying human emotions using EEG Signals,” Journal of Medical and Biological Engineering, vol. 31, no. 1, pp. 45–51, 2011.
- S. A. Hosseini, M. A. Khalilzadeh, M. B. Naghibi-Sistani, and V. Niazmand, “Higher order spectra analysis of EEG signals in emotional stress states,” in Proceedings of the 2nd International Conference on Information Technology and Computer Science (ITCS '10), pp. 60–63, ukr, July 2010.
- Y. Liu, O. Sourina, and M. K. Nguyen, “Real-time EEG-based human emotion recognition and visualization,” in Proceedings of the International Conference on Cyberworlds (CW '10), pp. 262–269, Singapore, October 2010.
- 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.
- 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.
- 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, vol. 41, no. 6, pp. 1052–1063, 2011.
- 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 of Lecture Notes in Computer Science, pp. 734–743, Springer, Berlin, Germany, 2011.
- L. Brown, B. Grundlehner, and J. Penders, “Towards wireless emotional valence detection from EEG,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '11), pp. 2188–2191, Boston, Mass, USA, September 2011.
- 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.
- V. H. Anh, M. N. Van, B. B. Ha, and T. H. Quyet, “A real-time model based support vector machine for emotion recognition through EEG,” in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS '12), pp. 191–196, Ho Chi Minh City, Vietnam, November 2012.
- H. Xu and K. N. Plataniotis, “Affect recognition using EEG signal,” in Proceedings of the 14th IEEE International Workshop on Multimedia Signal Processing (MMSP '12), pp. 299–304, Banff, Canada, September 2012.
- D. Huang, C. Guan, K. K. Ang, H. Zhang, and Y. Pan, “Asymmetric spatial pattern for EEG-based emotion detection,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '12), pp. 1–7, Brisbane, Australia, June 2012.
- T. F. Bastos-Filho, A. Ferreira, A. C. Atencio, S. Arjunan, and D. Kumar, “Evaluation of feature extraction techniques in emotional state recognition,” in Proceedings of the 4th International Conference on Intelligent Human Computer Interaction (IHCI '12), pp. 1–6, Kharagpur, India, December 2012.
- U. Wijeratne and U. Perera, “Intelligent emotion recognition system using electroencephalography and active shape models,” in Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES '12), pp. 636–641, Langkawi, Malaysia, December 2012.
- N. Jatupaiboon, S. Pan-ngum, and P. Israsena, “Emotion classification using minimal EEG channels and frequency bands,” in Proceedings of the 10th International Joint Conference on Computer Science and Software Engineering (JCSSE '13), pp. 21–24, 2013.
- R. W. Levenson, “Emotion and the autonomic nervous system: a prospectus for research on autonomic specificity,” in Social Psychophysiology and Emotion: Theory and Clinical Applications, H. L. Wagner, Ed., pp. 17–42, John Wiley & Sons, New York, NY, USA, 1988.
- F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” Journal of Neural Engineering, vol. 4, no. 2, pp. R1–R13, 2007.
- N. N. Vempala and F. A. Russo, “Predicting emotion from music audio features using neural networks,” in Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval (CMMR '12), 2012.
- “Emotiv EEG Neuroheadset,” http://emotiv.com/upload/manual/EEGSpecifications.pdf.
- C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011.
- R. W. Levenson, L. L. Carstensen, W. V. Friesen, and P. Ekman, “Emotion, physiology, and expression in old age,” Psychology and Aging, vol. 6, no. 1, pp. 28–35, 1991.
- J. A. Onton and S. Makeig, “High-frequency broadband modulations of electroencephalographic spectra,” Frontiers in Human Neuroscience, vol. 3, article 61, 2009.
- G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, “BCI2000: a general-purpose brain-computer interface (BCI) system,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034–1043, 2004.