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EEG-based classification of emotions using empirical mode decomposition and autoregressive model

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Abstract

Emotion can be classified based on 2-dimensional valence-arousal model which includes four categories of emotional states, such as high arousal high valence, low arousal high valence, high arousal low valence, and low arousal low valence. In this paper, we present the attempt to investigate feature extraction of electroencephalogram (EEG) based emotional data by focusing on empirical mode decomposition (EMD) and autoregressive (AR) model, and construct an EEG-based emotion recognition method to classify these emotional states. We first employ EMD method to decompose EEG signals into several intrinsic mode functions (IMFs), and then the features are calculated from IMFs based on AR model using a sliding window, and finally we use these features to recognize emotions. The average recognition rate of our proposed method is 86.28% for 4 binary-class tasks on DEAP dataset. Experimental results show that our proposed method has a uniform and stable performance of emotion recognition, which are quite competitive with the results of methods of comparison.

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References

  1. Alarcao SM, Fonseca MJ (2017) Emotions recognition using EEG signals: A survey. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2017.2714671

  2. Ali M, Mosa AH, Al Machot F, Kyamakya K (2016) EEG-based emotion recognition approach for e-healthcare applications. Proc. of ICUFN, pp. 946–950

  3. Atkinson J, Campos D (2016) Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl 47:35–41

    Article  Google Scholar 

  4. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27

    Article  Google Scholar 

  5. Chen J, Hu B, Moore P, Zhang X, Ma X (2015) Electroencephalogram-based emotion assessment system using ontology and data mining techniques. Appl Soft Comput 30:663–674

    Article  Google Scholar 

  6. Das AB, Bhuiyan MIH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Contrl 29:11–21

    Article  Google Scholar 

  7. Ekman P (1992) An argument for basic emotions. Cognit Emot 6(3):169–200

    Article  Google Scholar 

  8. Ekman P, Friesen WV, O’Sullivan M, Chan A et al (1987) Universals and cultural differences in the judgments of facial expressions of emotion. J Pers Soc Psychol 53(4):712–717

    Article  Google Scholar 

  9. Gao LL, Song JK, Liu XY, Shao JM, Liu JJ, Shao J (2017) Learning in high-dimensional multimedia data: the state of the art. Multimedia Systems 23:303–313

    Article  Google Scholar 

  10. Güntekin B, Başar E (2010) Event-related beta oscillations are affected by emotional eliciting stimuli. Neurosci Lett 483(3):173–178

    Article  Google Scholar 

  11. Guo Y, Tao D, Liu W, Cheng J (2017) Multiview Cauchy estimator feature embedding for depth and inertial sensor-based human action recognition. IEEE Trans Syst Man Cybernet 47(4):617–627

    Article  Google Scholar 

  12. Hatamikia S, Maghooli K, Nasrabadi AM (2014) The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalogram signals. J Med Sign Sens 4(3):194–201

    Google Scholar 

  13. Huang NE, Shen Z, Long SR, Wu ML, Shih HH, Zheng Q (1971) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, pp. 903–995

  14. Jie X, Cao R, Li L (2014) Emotion recognition based on the sample entropy of EEG. Biomed Mater Eng 24(1):1185–1192

    Google Scholar 

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

    Article  Google Scholar 

  16. Kroupi E, Vesin JM, Ebrahimi T (2016) Subject-independent odor pleasantness classification using brain and peripheral signals. IEEE Trans Affect Comput 7(4):422–434

    Article  Google Scholar 

  17. Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816

    Article  Google Scholar 

  18. Li X, Song D, Zhang P, Yu G, Hou Y, Hu B (2016) Emotion Recognition from Multi-Channel EEG Data through Convolutional Recurrent Neural Network. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 352–359

  19. Li M, Chen W, Zhang T (2017) Automatic epileptic EEG detection using DT-CWT-based non-linear features. Biomed Signal Process Contrl 34:114–125

    Article  Google Scholar 

  20. Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, Chen JH (2010) EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 57(7):1798–1806

    Article  Google Scholar 

  21. Liu Y, Sourina O (2013) Real-time subject-dependent EEG-based emotion recognition algorithm. Transactions on Computational Science XXIII, pp. 199–223

  22. Lu X, Li X, Mou L (2015) Semi-supervised multitask learning for scene recognition. IEEE Trans Cybernet 45(9):1967–1976

    Article  Google Scholar 

  23. McKeown G, Valstar MF, Cowie R, Pantic M (2010) The SEMAINE corpus of emotionally coloured character interactions. Proc. IEEE Int. Conf. Multimed. Expo., pp. 1079–1084

  24. Mohammadi Z, Frounchi J, Amiri M (2017) Wavelet-based emotion recognition system using EEG signal. Neural Comput & Applic 28(8):1985–1990

    Article  Google Scholar 

  25. Murugappan M, Ramachandran N, Sazali Y (2010) Classification of human emotion from EEG using discrete wavelet transform. J Biomed Sci Eng 3(4):390–396

    Article  Google Scholar 

  26. Nie D, Wang XW, Shi LC, Lu BL (2011) EEG-based emotion recognition during watching movies. Proc. of IEEE/EMBS Neural Engineering, pp. 667–670

  27. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. Proc. IEEE Int. Conf. Multimed. Expo., pp. 317–321

  28. Petrantonakis P, Hadjileontiadis L (2011) A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition. IEEE Trans Inf Technol Biomed 15(5):737–746

    Article  Google Scholar 

  29. Pham TD, Tran D, Ma W, Tran NT (2015) Enhancing performance of EEG-based emotion recognition systems using feature smoothing. Arik S et al. (Eds.): ICONIP 2015, Part IV, LNCS 9492, pp. 95–102

  30. Priestley MB (1994) Spectral Analysis and Time Series. Academic Press, London

    MATH  Google Scholar 

  31. Russell JA (2003) Core affect and the psychological construction of emotion. Psychol Rev 110(1):145–150

    Article  Google Scholar 

  32. Sanchez-Mendoza D, Masip D, Lapedriza A (2015) Emotion recognition from mid-level features. Pattern Recogn Lett 67:66–74

    Article  Google Scholar 

  33. Soleymani M, Asghari-Esfeden S, Fu Y, Pantic S (2016) Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans Affect Comput 7(1):17–28

    Article  Google Scholar 

  34. Song JK, Gao LL, Nie FP, Shen HT, Yan Y, Sebe N (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25(11):4999–5011

    Article  MathSciNet  Google Scholar 

  35. Tao D, Guo Y, Song M, Li Y, Yu Z, Tang Y (2016) Person re-identification by dual-regularized KISS metric learning. IEEE Trans Image Process 25(6):2726–2738

    Article  MathSciNet  Google Scholar 

  36. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  37. Vijayan A, Sen D, Sudheer A (2015) EEG-Based emotion recognition using statistical measures and auto-regressive modeling. Proc. of CICT, pp. 587–591

  38. Wang XH, Gao LL, Wang P, Sun XS, Liu XL (2017) Two-stream 3D convNet fusion for action recognition in videos with arbitrary size and length. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2017.2749159

  39. Xu D, Ricci E, Yan Y, Song JK, Sebe N (2015) Learning deep representations of appearance and motion for anomalous event detection. Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12

  40. Yoon HJ, Chung SY (2013) EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm. Comput Biol Med 43(12):2230–2237

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by National Natural Science Foundation of China (No. 61772252 and No. 61373127), and Program for Liaoning Innovative Talents in University.

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116081, China

    Yong Zhang, Suhua Zhang & Xiaomin Ji

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Yong Zhang

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  1. Yong Zhang
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  2. Suhua Zhang
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  3. Xiaomin Ji
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Correspondence to Yong Zhang.

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Zhang, Y., Zhang, S. & Ji, X. EEG-based classification of emotions using empirical mode decomposition and autoregressive model. Multimed Tools Appl 77, 26697–26710 (2018). https://doi.org/10.1007/s11042-018-5885-9

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  • DOI: https://doi.org/10.1007/s11042-018-5885-9

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