Feature Selection with Sequential Forward Selection Algorithm from Emotion Estimation based on EEG Signals

Abstract

In this study, we conducted EEG-based emotion recognition on arousal-valence emotion model. We collected our own EEG data with mobile EEG device Emotiv Epoc+ 14 channel by applying visual-aural stimulus. After collection we performed information measurement techniques, statistical methods and time-frequency attributes to obtain key features and created feature space. We wanted to observe the effect of features thus, we performed Sequential Forward Selection algorithm to reduce the feature space and compared the performance of accuracies for both all features and diminished features. In the last part, we applied QSVM (Quadratic Support Vector Machines) to classify the features and contrasted the accuracies. We observed that diminishing the feature space increased our average performance accuracy for arousal-valence dimension from 55% to 65%.

Keywords

• emotion estimation,feature selection,support vector machines,EEG

References

1. T.B. Alakus, and I. Turkoglu, ‘’EEG based emotion analysis systems’’, Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, vol. 11, no. 1, pp.26–39, 2018.
2. A. Turnip, A.I. Simbolon, M.F. Amri, P. Sihombing, R.H. Setiadi, and E. Mulyana, ‘’Backpropagation neural networks training for EEG-SSVEP classification of emotion recognition’’, Internetworking Indonesia Journal, vol. 9, no. 1, pp. 53-57, 2017.
3. W. Szwoch, ‘’Using physiological signals for emotion’’, 2013 6th International Conference on Human System Interaction (HSI), pp. 556-561, 2013.
4. Emotiv Epoc+ 14 Channel Mobile EEG Device, Online Link: https://www.emotiv.com/product/emotiv-epoc-14-channel-mobile-eeg/
5. Emotiv Insight 5 Channel Mobile EEG Device, Online Link: https://www.emotiv.com/product/emotiv-insight-5-channel-mobile-eeg/
6. Brainwave NeuroSky One Channel EEG Device, Online Link: https://store.neurosky.com/pages/mindwave
7. J.A. Russel, ‘’Core affect and physiological construction of emotion’’, Psychological Review, vol. 110, no. 1, pp. 145 – 150, 2003.
8. R. Cooper, J.W. Osselton, J.C. Shaw, ‘’EEG Technology’’, 2nd Edition, 1974.

9. H.H. Jasper, ‘’The ten-twenty electrode system of the international federation, Electroencephalography and Clinical Neurophysiology, vol. 10, pp. 371 – 375, 1958.
10. P. Ekman, ‘’An argument for basic emotions’’, Cognition and Emotion, vol. 6, no. ¾, pp. 169 – 200, 1992.
11. L.C. Yu, L.H. Lee, S. Hao, J. Wang, Y. He, J. Hu, K.R. Lali, and X. Zhang, ‘’Building chinese affective resources in valence-arousal dimensions’’, Proceedings of the 15th Annual Conference of the Nort American Chapter of the Association for Computational Linguistics: Human Language Techniques (NAACL-HLT), pp. 540 – 545, 2016.
12. J. A. Russel, ‘’Culture and the categorization of emotions’’, Psychological Bulletin, vol. 110, pp. 425 – 450, 1991.
13. P.J. Lang, M.M. Bradley, and B.N. Cuthbert, ‘’International affective picture system (IAPS): Affective ratings of pictures and instruction manual’’, Technical Report A-8, 2008.
14. IAPS Request Form, Online Link: https://csea.phhp.ufl.edu/media.html#topmedia
15. M.M. Bradley, and P.J. Lang, ‘’ ’International affective digitized sounds (IADS): Affective ratings of sounds and instruction manual’’, Technical Report B-3, 2007.
16. IADS Request Form, Online Link: https://csea.phhp.ufl.edu/media.html#topmedia
17. S. Koelstra, C. Mühl, and M. Soleymani, ‘’DEAP: A database for emotion analysis using physiological signals’’, IEEE Transactions of Affective Computing, vol. 3, no. 1, pp. 18 – 31, 2012.
18. T. B. Alakus, and I. Turkoglu, ‘’Emotion Detection Based on EEG Signals by Applying Signal Processing and Classification Techniques’’, Master Thesis, Institute of Science, Department of Software Engineering, 2018.
19. DEAP Dataset Access, Online Link: http://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html
20. S. Jirayucharoensak, S. Pan-Ngum, and P. Israsena, ‘’EEG-based emotion recognition using deep learning network with principal component-based covariate shift adaptation’’, The Scientific World Journal, vol. 2014, 2014.
21. N.H. Frijda, ‘’The emotions’’, Cambridge University Press, pp. 207, 1986.
22. Y.H. Liu, W.T. Cheng, Y.T. Hsiao, C.T. Wu., and M.D. Jeng, ‘’EEG-based emotion recognition based on kernel fishers discriminant analysis and spectral powers’’, IEEE International Conference on Systems, Man, and Cybernetics, pp. 5 – 8, 2014.
23. M.M. Javaid, M.A. Yousaf, Q.Z. Sheikh, M.M. Awais, S. Saleem, and M. Khalid, ‘’Real-time EEG-based human emotion recognition’’, Neural Information Processing, pp. 182 – 190, 2015.
24. A. Mert, and A. Akan, ‘’Emotion recognition based on time-frequency distribution of EEG signals using multivariate synchrosqueezing transform’’, Digital Signal Processing, vol. 81, no. 2018, pp. 152 – 157, 2018.
25. L. Xin, S. Xiao-Qi, Q. Xiao-Ying, and S. Xiao-Feng, ‘’Relevance vector machine based EEG emotion recognition’’, 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, pp. 293-297, 2016.
26. T.B. Alakus, and I. Turkoglu, ‘’Emotion estimation based on various computer games by using pattern recognition methods’’, IEEE Transactions of Affective Computing, submitted.
27. A. Marcano-Cedeno, J. Quintanilla-Dominguez, M.G. Cortina-Januchs, and D. Andina,’’Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network’’, 36th Annual Conference on IEEE Industrial Electronics Society, pp. 2845 – 2850, 2010.
28. I. A. Basheer, and M. Hajmeer, ‘’Artificial neural networks: Fundamentals, computing, design, and application’’, Journal of Microbiological Methods, vol. 43, no. 1, pp. 3 – 31, 2000.