Browse > Article
http://dx.doi.org/10.9728/dcs.2018.19.4.821

Research on Classification of Human Emotions Using EEG Signal  

Zubair, Muhammad (Information and communication networks Department, University of Science and Technology)
Kim, Jinsul (Department of Electronics and Computer Engineering, Chonnam National University)
Yoon, Changwoo (Electronics and Telecommunication Research Institute)
Publication Information
Journal of Digital Contents Society / v.19, no.4, 2018 , pp. 821-827 More about this Journal
Abstract
Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.
Keywords
EEG signals; Emotion recognition; Wavelet transform; SVM;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Lan, Tian, et al. "Estimating cognitive state using EEG signals." Signal Processing Conference, 2005 13th European. IEEE, 2005.
2 Murugappan et al."Classification of human emotion from EEG using discrete wavelet transform." Journal of Biomedical Science and Engineering 3.04 (2010): 390.   DOI
3 S.Koelstraet al., "Single trial classificationof EEG and peripheral physiological signals for recognitionof emotions induced by music videos," in Proceeding of the International Conference on Brain Informatics (BI '10), pp. 89-100, Toronto, Canada, 2010.
4 U. Wijeratneet al, "Intelligent emotion recognition system using electroencephalography and active shapemodels," in Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES '12), pp. 636-641, 2012.
5 L. Zhang, D. Tjondronegoro, "Facial expression recognition using facial movement features," IEEE Transactions on Affective Computing, vol. 2, pp. 219-229, 2011.   DOI
6 K. Wang, A. Ning, B.N. Li and Y. Zhang, "Speech emotion recognition using Fourier parameters," IEEE Transactions on Affective Computing, vol. 6, pp. 69-75, 2015.   DOI
7 J. Anttonen and V. Surakka, "Emotions and Heart Rate While Sitting on a Chair," Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 491-499. 2005.
8 C.M. Jones and T. Troen, "Biometric Valence and Arousal Recognition," Proc. 19th Australasian Conf. Computer-Human Interaction, pp. 191-194, 2007.
9 M. Soleymani et al. A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing, 2012, 3. Jg., Nr. 1, S. 42-55.   DOI
10 Z. Khalili and M. H. Moradi, "Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG," in Proceedings of the International Joint Conference on Neural Networks (IJCNN '09), pp.1571-1575, Atlanta, Ga, USA, June 2009.
11 Subramanian R, Wache J, Abadi M, Vieriu R, Winkler S, Sebe N. ASCERTAIN: Emotion and personality recognition using commercial sensors. IEEE Transactions on Affective Computing. 2016 Nov 4.
12 Sourina, Olga, and Yisi Liu. "A Fractal-based Algorithm of Emotion Recognition from EEG using Arousal-Valence Model." BIOSIGNALS. 2011.
13 Bilal, Muhammad, and Shin-Gak Kang. "An Authentication Protocol for Future Sensor Networks." Sensors 17.5 (2017): 979.   DOI
14 Paul Ekman, Emotions Revealed. TIMES BOOKS, 2003.
15 Posner et al. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology.Dev. Psychopathol. 17, (2005).
16 P. J. Lang, M. M. Bradley, and B. N. Cuthbert, "International affective picture system (IAPS): Affective ratings of pictures and instruction manual," Tech. Rep. A-8, 2008.
17 M. M. Bradley and P. J. Lang, "The international affective digitized sounds (; iads-2): Affective ratings of sounds and instruction manual," Univ. of Florida, Gainesville, FL, USA, Tech. Rep. B-3, 2007.
18 Petrantonakis et al. "Emotion recognition from EEG using higher order crossings." IEEE Transactions on Information Technology in Biomedicine 14.2 (2010): 186-197.   DOI
19 Daimi, Syed Naser, and GoutamSaha. "Classification of emotions induced by music videos and correlation with participants' rating." Expert Systems with Applications 41.13 (2014): 6057-6065.   DOI
20 Koelstra, Sander, et al. "Deap: A database for emotion analysis; using physiological signals." IEEE Transactions on Affective Computing 3.1 (2012): 18-31.   DOI