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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.
Citations to this Article [6 citations]
The following is the list of published articles that have cited the current article.
- Cipresso Pietro, Serino Silvia, and Riva Giuseppe, “The Pursuit of Happiness Measurement: A Psychometric Model Based on Psychophysiological Correlates,” The Scientific World Journal, vol. 2014, pp. 1–15, 2014.
- Hong Zeng, and Aiguo Song, “Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis,” Scientific World Journal, 2014.
- Suwicha Jirayucharoensak, Setha Pan-Ngum, and Pasin Israsena, “EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation,” The Scientific World Journal, vol. 2014, pp. 1–10, 2014.
- Atika Qazi, Ram Gopal Raj, Muhammad Tahir, Erik Cambria, and Karim Bux Shah Syed, “Enhancing Business Intelligence by Means of Suggestive Reviews,” The Scientific World Journal, vol. 2014, pp. 1–11, 2014.
- O. Georgieva, S. Milanov, P. Georgieva, I. M. Santos, A. T. Pereira, and C. F. Silva, “Learning to decode human emotions from event-related potentials,” Neural Computing & Applications, vol. 26, no. 3, pp. 573–580, 2015.
- Timothy McMahan, Ian Parberry, and Thomas D. Parsons, “Modality Specific Assessment of Video Game Player’s Experience Using the Emotiv,” Entertainment Computing, 2015.