Electroencephalographic Signal Processing for Detection and Prediction of Emotion

  • Aruna Chakraborty
  • Amit Konar
Part of the Studies in Computational Intelligence book series (SCI, volume 234)


This chapter deals with processing of electroencephalographic signals for detection and prediction of emotion. Prediction of electroencephalographic signal form past samples are needed for early diagnosis of patients, suffering from frequent epileptic seizure and/or psychotherapeutic treatment. The chapter begins with comparing the performance of various digital filter algorithms to identify the right candidate for application in electroencephalographic signal prediction. It then considers clustering/classification of emotion from filter co-efficient, wavelet co-efficient and Fast Fourier Transform co-efficient. It also considers the scope of bio-potential signals, such as skin conductance and pulse count along with electroencephalographic signal on emotion detection of personals. Several algorithms of pattern classification have been examined to determine the best algorithm for the emotion classification problem.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arnold, S.H., Karrass, J., Edward, G.C., Tedra, A.W., Susan, M.W., Alexandra, F.K.: Emotional Reactivity and Regulation in Young Children who Stutter: Preliminary Behavioral and Brain Activity Data. Vanderbilt University, NashvilleGoogle Scholar
  2. 2.
    Datta, S.: Emotion Detection and Control: A Computational Intelligence Approach, M. Tech. Thesis. Jadavpur University (2007)Google Scholar
  3. 3.
    Durka, P.: Matching Pursuit and Unification in EEG Analysis. Artech House, Norwood (2007)Google Scholar
  4. 4.
    Frackowiak, R.S.J., Ashburner, J.T., Penny, W.D., Zeki, S., Friston, K.J., Frith, C.D., Dolan, R.J., Price, C.J. (eds.): Human Brain Function, ch. 19. Elsevier Publisher, North Holand (2005)Google Scholar
  5. 5.
    Greg, W., Bishop, G.: An Introduction to the Kalman Filter TR 95 – 041, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3175, Updated Monday (July 24, 2006)Google Scholar
  6. 6.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New Jersey (1999)zbMATHGoogle Scholar
  7. 7.
    Michel, C.M., Lehmann, B., Hemggler, B., Brandeis, D.: Localization of sources of EEG delta, theta, alpha and beta frequency bands using the FFT dipole approximation. Electroencephalography and clinical Neurophysiology 82, 38–44 (1992)CrossRefGoogle Scholar
  8. 8.
    Monson, H.H.: Statistical Digital Signal Processing and Modeling. Wiley, Chichester (1996)Google Scholar
  9. 9.
    Proakis, J.G., Manolakis, D.G.: Digital Signal Processing: Principles, Algorithms and Applications. Prentice-Hall, Englewood-Cliffs (1996)Google Scholar
  10. 10.
    Rippon, G.: Electroencephalography. In: Senior, C., Russell, T., Gazzaniga, M.S. (eds.) Methods in Mind. MIT Press, Cambridge (2006)Google Scholar
  11. 11.
    Senior, C., Russell, T., Gazzaniga, N.S. (eds.): Methods in Mind. MIT Press, Cambridge (2006)Google Scholar
  12. 12.
    Simon, H.: Adaptive Filter Theory. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  13. 13.
    Solo, V., Kong, X.: Adaptive Signal Processing Algorithms: Stability and Performance. Prentice-Hall, Englewood-Cliffs (1986)Google Scholar
  14. 14.
    Takahashi, K.: Remarks on Emotion Recognition from Biopotential Signals. In: Second Int. Conf. on Autonomous Robots and Agents, Japan, pp. 186–191 (2004)Google Scholar
  15. 15.
    Widrow, B., Starns, S.D.: Adaptive Signal Processing. Prentice-Hall, Englewood-Cliffs (1985)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aruna Chakraborty
    • Amit Konar

      There are no affiliations available

      Personalised recommendations