Detecting Emotion from EEG Signals Using the Emotive Epoc Device

  • Rafael Ramirez
  • Zacharias Vamvakousis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7670)


The study of emotions in human-computer interaction has increased in recent years in an attempt to address new user needs. At the same time, it is possible to record brain activity in real-time and discover patterns to relate it to emotional states. This paper describes a machine learning approach to detect emotion from brain activity, recorded as electroencephalograph (EEG) with the Emotic Epoc device, during auditory stimulation. First, we extract features from the EEG signals in order to characterize states of mind in the arousal-valence 2D emotion model. Using these features we apply machine learning techniques to classify EEG signals into high/low arousal and positive/negative valence emotional states. The obtained classifiers may be used to categorize emotions such as happiness, anger, sadness, and calm based on EEG data.


Support Vector Machine Linear Discriminant Analysis Alpha Activity Radial Basis Function Kernel Alpha Wave 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bradley, M.M., Lang, P.J.: International Affective Digitized Sounds (IADS): Stimuli, Instruction Manual and Affective Ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL, USA (1999)Google Scholar
  2. 2.
    Chanel, G., Kronegg, J., Grandjean, D., Pun, T.: Emotion Assessment: Arousal Evaluation Using EEG’s and Peripheral Physiological Signals. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 530–537. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Choppin, A.: Eeg-based human interface for disabled individuals: Emotion expression with neural networks. Masters thesis, Tokyo Institute of Technology, Yokohama, Japan (2000)Google Scholar
  4. 4.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press (2000)Google Scholar
  5. 5.
    Coburn, K., Moreno, M.: Facts and artifacts in brain electrical activity mapping. Brain Topography 1(1), 37–45 (1988)CrossRefGoogle Scholar
  6. 6.
    Fatourechi, M., Bashashati, A., Ward, R.K., Birch, G.E.: EMG and EOG artifacts in brain computer interface systems: A survey. Clininical Neurophysiology (118), 480–494 (2007)Google Scholar
  7. 7.
    Emotiv Systems Inc. Researchers,
  8. 8.
    Harmon-Jones, E.: Clarifying the emotive functions of asymmetrical frontal cortical activity. Psychophysiology 40(6), 838–848 (2003)CrossRefGoogle Scholar
  9. 9.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science. Mc Graw Hill (2000)Google Scholar
  10. 10.
    Lin, Y.-P., Wang, C.-H., Jung, T.-P., Wu, T.-L., Jeng, S.-K., Duann, J.-R., Chen, J.-H.: EEG-Based Emotion Recognition in Music Listening. IEEE Transactions on Biomedical Engineering 57(7) (2010)Google Scholar
  11. 11.
    Mika, S., et al.: Fisher Discriminant Analysis with Kernels. In: IEEE Conference on Neural Networks for Signal Processing IX, pp. 41–48 (1999)Google Scholar
  12. 12.
    Niedermeyer, E., da Silva, F.L.: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, p. 140. Lippincott Williams & Wilkins (2004)Google Scholar
  13. 13.
    Niemic, C.P.: Studies of emotion: A theoretical and empirical review of psychophysiological studies of emotion. Journal of Undergraduate Research 1, 15–18 (2002)Google Scholar
  14. 14.
    Bos, D.O.: EEG-based Emotion Recognition: The Influence of Visual and Auditory StimuliGoogle Scholar
  15. 15.
    OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain-Computer Interfaces in Real and Virtual Environments. MIT Press Journal Presence’ 19(1), 35–53 (2010)Google Scholar
  16. 16.
    Partala, T., Jokiniemi, M., Surakka, V.: Pupillary responses to emotionally provocative stimuli. In: ETRA 2000: Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp. 123–129. ACM Press, New York (2000)CrossRefGoogle Scholar
  17. 17.
    Picard, R.W., Klein, J.: Toward computers that recognize and respond to user emotion: Theoretical and practical implications. Interacting with Computers 14(2), 141–169 (2002)CrossRefGoogle Scholar
  18. 18.
    Takahashi, K.: Remarks on emotion recognition from bio-potential signals. In: 2nd International Conference on Autonomous Robots and Agents, pp. 186–191 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rafael Ramirez
    • 1
  • Zacharias Vamvakousis
    • 1
  1. 1.Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain

Personalised recommendations