Abstract
Recognition of user felt emotion is an exciting field because visual, verbal and facial communications can be falsified more easily than ‘inner’ emotions. Non-invasive EEG-based human emotion recognition entails the classification of discrete emotions using EEG data. These emotions can be defined by the arousal-valence dimensions. We performed real-time emotion classification for four categories of emotional states, namely: pleasant, sad, happy and frustrated. Higuchi’s Fractal Dimension was applied on EEG data and used as a feature extraction method and Support Vector Machine was used for classification. This paper documents a comparative study of classification accuracy achieved by collecting raw EEG data from 3 electrode locations vs. collection from 8 electrode locations.
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References
Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based Human Emotion Recognition and Visualization
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Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory
Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based Human Emotion Recognition and Visualization
Lin, Y.P., Wang, C.H., Wu, T.L., Jeng, S.K., Chen, J.H.: EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Taipei, pp. 489–492 (2009)
Bos, D.O.: EEG-based emotion recognition (2006). http://hmi.ewi.utwente.nl/verslagen/capitaselecta/CS-Oude_Bos-Danny.pdf
Russell, J.A.: Affective space is bipolar. J. Pers. Soc. Psychol. 37, 345–356 (1979)
Higuchi, T.: Approach to an irregular time series on the basis of the fractal theory
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Javaid, M.M., Yousaf, M.A., Sheikh, Q.Z., Awais, M.M., Saleem, S., Khalid, M. (2015). Real-Time EEG-based Human Emotion Recognition. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_22
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DOI: https://doi.org/10.1007/978-3-319-26561-2_22
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