Advertisement

Classification of Basic Human Emotions from Electroencephalography Data

  • Ximena Fernández
  • Rosana García
  • Enrique FerreiraEmail author
  • Juan Menéndez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

This paper explores the combination of known signal processing techniques to analyze electroencephalography (EEG) data for the classification of a set of basic human emotions. An Emotiv EPOC headset with 16 electrodes was used to measure EEG data from a population of 24 subjects who were presented an audiovisual stimuli designed to evoke 4 emotions (rage, fear, fun and neutral). Raw data was preprocessed to eliminate noise, interference and physiologic artifacts. Discrete Wavelet Transform (DWT) was used to extract its main characteristics and define relevant features. Classification was performed using different algorithms and results compared. The best results were obtained when using meta-learning techniques with classification errors at 5 %. Final conclusions and future work are discussed.

Keywords

Electroencephalography Discrete wavelet transform Human emotion classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, N., Broderick, A.J., Chanberlain, L.: What is neuromarketing? A discussion and agenda for future research. Int. J. Psychophysiol. 63(2), 199–204 (2007)CrossRefGoogle Scholar
  2. 2.
    Jatupaiboon, N., Pan-ngum, S., Israsena, P.: Real-Time EEG-Based Happiness Detection System. The Scientific World Journal 2013, 12 (2013). ID 618649CrossRefGoogle Scholar
  3. 3.
    Torres, A.A., Reyes, C.A., Villaseor, L., Ramrez, J.M.: Anlisis de Seales Electroencefalogrficas para la Clasificacin de Habla Imaginada. Revista Mexicana de Ingeniera Biomdica 34(1), 23–39 (2013)Google Scholar
  4. 4.
    Cacioppo, C.T., Tassinary, L.G.: Inferring Physiological Significance from Physiological Signals. Am. Psychol. 45(1), 16–28 (1990)CrossRefGoogle Scholar
  5. 5.
    Ekman, P., Levenson, R.W., Freison, W.V.: Autonomic Nervous System Activity Distinguishes Among Emotions. J. Exp. Soc. Psychol. 19, 195–216 (1983)Google Scholar
  6. 6.
    Winton, W.M., Putnam, L., Krauss, R.: Facial and Autonomic Manifestations of the dimensional structure of Emotion. J. Exp. Soc. Psychol. 20, 195–216 (1984)CrossRefGoogle Scholar
  7. 7.
    Murugappan, M., Murugappan, S., Balaganapathy, C.: Wireless EEG signals based neuromarketing system using fast fourier transform (FFT). In: 10th Int. Col. on Signal Processing & its Applications, pp. 25–30. IEEE (2014)Google Scholar
  8. 8.
    Lopes da Silva, F., Niedermeyer, E.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 6th edn. Lippincot Williams & Wilkins (2004). ISBN 0-7817-5126-8Google Scholar
  9. 9.
    Sanei, S., Sanei, S., Chambers, J.A.: EEG Signal Processing. Centre of Digital Signal Processing Cardiff University, UK (2007). ISBN 978-0-470-02581-9CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.
    Pierce, J.W.: PsychoPy. Psychophysics software in Python (2007). http://www.psychopy.org/
  13. 13.
    Schaefer, A., Nils, F., Snchez, X., Philippot, P.: FilmStim, Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers. Cognition and Emotion 24(7), 1153–1172 (2010)CrossRefGoogle Scholar
  14. 14.
    Fernandez Megas, C., Prez Sola, V.: Inducción de emociones en condiciones experimentales: un banco de estímulos audiovisuales. Programa de Doctorado en Psiquiatra Departament de Psiquiatria i Medicin UAB (2012)Google Scholar
  15. 15.
    Murugappan, M., Nagarajan, R., Yaacob, S.: Combining Spatial Filtering and Wavelet Transform for Classifying Human Emotions Using EEG Signals. IEEE Symposium on Industrial Electronics & Applications 2, 836–841 (2009)Google Scholar
  16. 16.
    Murugappan, M.: Human emotion classification using wavelet transform and KNN. In: Int. Conf. on Pattern Analysis and Intelligent Robotics, vol. 11, pp. 48–153 (2011)Google Scholar
  17. 17.
    Murugappan, M., Nagarajan, R., Yaacob, S.: Comparison of different wavelet features from EEG signals for classifying human emotions. IEEE Symposium on Industrial Electronics & Applications 2, 836–841 (2009)Google Scholar
  18. 18.
    Fernandez, C., Pascual, J.C., Soler, J., Garca, E.: Validacin espaola de una batera de pelculas para inducir emociones. Psicothema 23(4), 778–785 (2011)Google Scholar
  19. 19.
    Bradley, M., Lang, P.: Measuring Emotion: the Self-Assessment Semantic Differential. J. Behav. Ther. & Exp. Psvchrar. 25(1), 49–59 (1994)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Weber, P., Letelier, J.: Clasificacion de Espigas Extracelulares Basada en la Transformada de Wavelet Discreta. Universidad de Chile (2002). http://repositorio.uchile.cl/tesis/uchile/2002/weber_p/html/index-frames.html
  22. 22.
    Samar, V.J., Bopardikar, A.: Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain and Language 66(1), 7–60 (1999)CrossRefGoogle Scholar
  23. 23.
    Mallat, S.: A wavelet tour of signal processing. Academic Press (1999)Google Scholar
  24. 24.
    Parameswariah, C., Cox, M.: Frequency characteristics of wavelets. IEEE Transactions on Power Delivery 17(3), 800–804 (2002). ISSN: 0885–8977CrossRefGoogle Scholar
  25. 25.
    Witten, H., Frank, I., Hall, M.: DATA MINING Practical Machine Learning Tools and Techniques, 3rd edn, pp. 356–372 (2011)Google Scholar
  26. 26.
    Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  27. 27.
    Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  28. 28.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Machine Learning 16(3), 235–240 (1994)Google Scholar
  29. 29.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ximena Fernández
    • 1
  • Rosana García
    • 1
  • Enrique Ferreira
    • 1
    Email author
  • Juan Menéndez
    • 2
  1. 1.Universidad Católica del UruguayMontevideoUruguay
  2. 2.Sentia LabsMontevideoUruguay

Personalised recommendations