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EEG-Based Emotion Recognition via Fast and Robust Feature Smoothing

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Brain Informatics (BI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

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Abstract

Electroencephalograph (EEG) signals reveal much of our brain states and have been widely used in emotion recognition. However, the recognition accuracy is hardly ideal mainly due to the following reasons: (i) the features extracted from EEG signals may not solely reflect one’s emotional patterns and their quality is easily affected by noise; and (ii) increasing feature dimension may enhance the recognition accuracy, but it often requires extra computation time. In this paper, we propose a feature smoothing method to alleviate the aforementioned problems. Specifically, we extract six statistical features from raw EEG signals and apply a simple yet cost-effective feature smoothing method to improve the recognition accuracy. The experimental results on the well-known DEAP dataset demonstrate the effectiveness of our approach. Comparing to other studies on the same dataset, ours achieves the shortest feature processing time and the highest classification accuracy on emotion recognition in the valence-arousal quadrant space.

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Notes

  1. 1.

    http://www.biosemi.com.

  2. 2.

    http://www.eecs.qmul.ac.uk/mmv/datasets/deap/.

References

  1. Asteriadis, S., Tzouveli, P., Karpouzis, K., Kollias, S.: Estimation of behavioral user state based on eye gaze and head pose-application in an e-learning environment. Multimedia Tools Appl. 41(3), 469–493 (2009)

    Article  Google Scholar 

  2. Bos, D.O.: EEG-based emotion recognition-the influence of visual and auditory stimuli. Capita Selecta (MSc course) (2006)

    Google Scholar 

  3. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Article  Google Scholar 

  4. Goldberg, D.: The detection and treatment of depression in the physically ill. World Psychiatry 9(1), 16–20 (2010)

    Article  Google Scholar 

  5. Heraz, A., Frasson, C.: Predicting the three major dimensions of the learners emotions from brainwaves. Int. J. Comput. Sci. 2(3), 183–193 (2007)

    Google Scholar 

  6. Jap, B.T., Lal, S., Fischer, P., Bekiaris, E.: Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst. Appl. 36(2), 2352–2359 (2009)

    Article  Google Scholar 

  7. Kandel, E.R., Schwartz, J.H., Jessell, T.M., Siegelbaum, S.A., Hudspeth, A.J.: Principles of Neural Science. Mc Graw Hill, New York (2012)

    Google Scholar 

  8. Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)

    Article  Google Scholar 

  9. Liu, Y., Sourina, O.: EEG databases for emotion recognition. In: International Conference on Cyberworlds, pp. 302–309. IEEE (2013)

    Google Scholar 

  10. Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23(2), 209–237 (2009)

    Article  Google Scholar 

  11. Pham, T.D., Tran, D., Ma, W., Tran, N.T.: Enhancing performance of EEG-based emotion recognition systems using feature smoothing. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9492, pp. 95–102. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26561-2_12

    Chapter  Google Scholar 

  12. Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  13. Pijn, J.P., Van Neerven, J., Noest, A., da Silva, F.H.L.: Chaos or noise in EEG signals’ dependence on state and brain site. Electroencephalogr. Clin. Neurophysiol. 79(5), 371–381 (1991)

    Article  Google Scholar 

  14. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  15. Shi, L.C., Lu, B.L.: Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning. In: International Conference on Engineering in Medicine and Biology, pp. 6587–6590. IEEE (2010)

    Google Scholar 

  16. Sourina, O., Liu, Y.: A fractal-based algorithm of emotion recognition from EEG using arousal-valence model. In: BIOSIGNALS, pp. 209–214 (2011)

    Google Scholar 

  17. Takahashi, K.: Remarks on emotion recognition from multi-modal bio-potential signals. In: International Conference on Industrial Technology, vol. 3, pp. 1138–1143. IEEE (2004)

    Google Scholar 

  18. Vyzas, E., Picard, R.W.: Affective pattern classification. In: Emotional and Intelligent: The Tangled Knot of Cognition, pp. 176–182 (1998)

    Google Scholar 

  19. Wang, D., Tan, A.H., Miao, C.: Modelling autobiographical memory in human-like autonomous agents. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 845–853. ACM (2016)

    Google Scholar 

  20. Wang, D., Tan, A.H.: Mobile humanoid agent with mood awareness for elderly care. In: International Joint Conference on Neural Networks, pp. 1549–1556. IEEE (2014)

    Google Scholar 

  21. Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)

    Article  Google Scholar 

  22. Yanagimoto, M., Sugimoto, C.: Recognition of persisting emotional valence from EEG using convolutional neural networks. In: International Workshop on Computational Intelligence and Applications, pp. 27–32. IEEE (2016)

    Google Scholar 

  23. Yohanes, R.E., Ser, W., Huang, G.B.: Discrete wavelet transform coefficients for emotion recognition from EEG signals. In: International Conference on Engineering in Medicine and Biology, pp. 2251–2254. IEEE (2012)

    Google Scholar 

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Acknowledgments

This research is supported in part by the National Research Foundation, Prime Minister’s Office, Singapore under its IDM Futures Funding Initiative. This research is also partially supported by the NTU-PKU Joint Research Institute, a collaboration between Nanyang Technological University and Peking University that is sponsored by a donation from the Ng Teng Fong Charitable Foundation.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Cheng Tang, Ah-Hwee Tan & Chunyan Miao

  2. Joint NTU-UBC Research Center of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, 639798, Singapore

    Di Wang, Ah-Hwee Tan & Chunyan Miao

Authors
  1. Cheng Tang
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  2. Di Wang
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  3. Ah-Hwee Tan
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  4. Chunyan Miao
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Corresponding author

Correspondence to Di Wang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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Tang, C., Wang, D., Tan, AH., Miao, C. (2017). EEG-Based Emotion Recognition via Fast and Robust Feature Smoothing. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

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