Abstract
This paper focuses on the variation of EEG at different emotional states. We use pure music segments as stimuli to evoke the exciting or relaxing emotions of subjects. EEG power spectrum is adopted to form features, power spectrum, differential asymmetry, and rational asymmetry. A linear dynamic system approach is applied to smooth the feature sequence. Minimal-redundancy-maximal-relevance algorithm and principal component analysis are used to reduce the dimension of features. We evaluate the performance of support vector machine, k-nearest neighbor classifiers and least-squares classifiers. The accuracy of our proposed method reaches 81.03% on average. And we show that the frequency bands, beta and theta, perform better than other frequency bands in the task of emotion recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Anderson, K., McOwan, P.W.: A Real-Time Automated System for the Recognition of Human Facial Expressions. IEEE Transaction on System, Man, and Cybernetics, Part B: Cybernetics 36(1), 96–105 (2006)
Bruck, C., Kreifelts, B., Wildgruber, D.: Emotional Voices in Context: a Neurobiological Model of Multimodal Affective Information Processing. Physics of Life Reviews 8, 383–403 (2011)
Aftanas, L.I., Lotova, N.V., Koshkarov, V.I., et al.: Non-Linear Dynamic Complexity of the Human EEG During Evoked Emotions. International Journal of Psychophysiology 28, 63–76 (1998)
Lin, Y.P., Wang, C.H., Jung, T.P., et al.: EEG-Based Emotion Recognition in Music Listening. IEEE Transaction on Biomedical Engineering 57(7), 1798–1806 (2010)
Nie, D., Wang, X.W., Shi, L.C., et al.: EEG-Based Emotion Recognition During Watching Movies. In: Proceedings of IEEE EMBS Conference on Neural Engineering, pp. 667–670. IEEE Press (2011)
Shi, S.J., Lu, B.L.: EEG Signal Classification during Listening to Native and Foreign Languages Songs. In: Proceedings of 4th International IEEE EMBS Conference on Neural Engineering, pp. 440–443 (2009)
Shi, L.C., Lu, B.L.: Off-Line and On-Line Vigilance Estimation Based on Linear Dynamical System and Manifold Learning. In: Proceedings of 32nd International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6587–6590. IEEE Press (2010)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Peng, H., Long, F., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Muller, M.M., Keil, A., Gruber, T., Elbert, T.: Processing of Affective Pictures Modulates Right-Hemispheric Gamma Band EEG Activity. Clinical Neurophysiology 110(11), 1913–1920 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duan, RN., Wang, XW., Lu, BL. (2012). EEG-Based Emotion Recognition in Listening Music by Using Support Vector Machine and Linear Dynamic System. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-34478-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
Online ISBN: 978-3-642-34478-7
eBook Packages: Computer ScienceComputer Science (R0)