About this Journal Submit a Manuscript Table of Contents
The Scientific World Journal
Volume 2013 (2013), Article ID 618649, 12 pages
http://dx.doi.org/10.1155/2013/618649
Research Article

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 [11 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. View at Publisher · View at Google Scholar
  • Hong Zeng, and Aiguo Song, “Removal of EOG Artifacts from EEG Recordings Using Stationary Subspace Analysis,” Scientific World Journal, 2014. View at Publisher · View at Google Scholar
  • 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. View at Publisher · View at Google Scholar
  • 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. View at Publisher · View at Google Scholar
  • L. I. Aftanas, N. V. Reva, S. V. Pavlov, V. V. Korenek, and I. V. Brak, “Linkage of Brain Oscillatory Systems with the Cognitive (experience and valence) and Physiological (cardiovascular reactivity) Components of the Emotions,” Neuroscience and Behavioral Physiology, 2015. View at Publisher · View at Google Scholar
  • Lachezar Bozhkov, Petia Koprinkova-Hristova, and Petia Georgieva, “Learning to decode human emotions with Echo State Networks,” Neural Networks, 2015. View at Publisher · View at Google Scholar
  • Noppadon Jatupaiboon, Setha Pan-Ngum, and Pasin Israsena, “Subject-Dependent and Subject-Independent Emotion Classification Using Unimodal and Multimodal Physiological Signals,” Journal Of Medical Imaging And Health Informatics, vol. 5, no. 5, pp. 1020–1027, 2015. View at Publisher · View at Google Scholar
  • 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. View at Publisher · View at Google Scholar
  • Timothy McMahan, Ian Parberry, and Thomas D. Parsons, “Modality Specific Assessment of Video Game Player’s Experience Using the Emotiv,” Entertainment Computing, 2015. View at Publisher · View at Google Scholar
  • Raja Majid Mehmood, and Hyo Jong Lee, “Exploration of Prominent Frequency Wave in EEG Signals from Brain Sensors Network,” International Journal of Distributed Sensor Networks, vol. 2015, pp. 1–9, 2015. View at Publisher · View at Google Scholar
  • Dongcui Wang, Fongming Mo, Yangde Zhang, Chao Yang, Jun Liu, Zhencheng Chen, and Jinfeng Zhao, “Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system,” Bio-Medical Materials And Engineering, vol. 26, pp. S917–S923, 2015. View at Publisher · View at Google Scholar