Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person’s emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%.

1. [1] X. Li et al., “Exploring EEG features in cross-subject emotion recognition,” Frontiers in Neuroscience, Vol.12, Article No.162, 2018. https://doi.org/10.3389/fnins.2018.00162
2. [2] A. Iyer, S. S. Das, R. Teotia, S. Maheshwari, and R. R. Sharma, “CNN and LSTM based ensemble learning for human emotion recognition using EEG recordings,” Multimedia Tools and Applications, Vol.82, No.4, pp. 4883-4896, 2023. https://doi.org/10.1007/s11042-022-12310-7
3. [3] D. Li, L. Xie, Z. Wang, and H. Yang, “Brain emotion perception inspired EEG emotion recognition with deep reinforcement learning,” IEEE Trans. on Neural Networks and Learning Systems, 2023. https://doi.org/10.1109/tnnls.2023.3265730
4. [4] T. Alotaiby, F. E. A. El-Samie, S. A. Alshebelli, and I. Ahmad, “A review of channel selection algorithms for EEG signal processing,” EURASIP J. on Advances in Signal Processing, Vol.2015, Article No.66, 2015. https://doi.org/10.1186/s13634-015-0251-9
5. [5] A. Al-Nafjan, M. Hosny, Y. Al-Ohali, and A. Al-Wabil, “Review and classification of emotion recognition based on EEG brain-computer interface system research: A systematic review,” Applied Sciences, Vol.7, No.12, Article No.1239, 2017. https://doi.org/10.3390/app7121239
6. [6] M. Niknazar, S. R. Mousavi, B. V. Vahdat, and M. Sayyah, “A new framework based on recurrence quantification analysis for epileptic seizure detection,” IEEE J. of Biomedical and Health Informatics, Vol.17, No.3, pp. 572-578, 2013. https://doi.org/10.1109/JBHI.2013.2255132
7. [7] S.-J. Choi and B.-G. Kang, “Prototype design and implementation of an automatic control system based on a BCI,” Wireless Personal Communications, Vol.79, No.4, pp. 2551-2563, 2014. https://doi.org/10.1007/s11277-014-1861-5
8. [8] W. Zheng, “Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis,” IEEE Trans. on Cognitive and Developmental Systems, Vol.9, No.3, pp. 281-290, 2017. https://doi.org/10.1109/TCDS.2016.2587290
9. [9] M. L. R. Menezes et al., “Towards emotion recognition for virtual environments: An evaluation of EEG features on benchmark dataset,” Personal and Ubiquitous Computing, Vol.21, No.6, pp. 1003-1013, 2017. https://doi.org/10.1007/s00779-017-1072-7
10. [10] K. S. Kamble and J. Sengupta, “EVNCERS: An integrated eigenvector centrality-variational nonlinear chirp mode decomposition-based EEG rhythm separation for automatic emotion recognition,” IEEE Sensors J., Vol.23, No.18, pp. 21661-21669, 2023. https://doi.org/10.1109/JSEN.2023.3304891
11. [11] J. W. Li, S. Barma, S. H. Pun, M. I. Vai, and P. U. Mak, “Emotion recognition based on EEG brain rhythm sequencing technique,” IEEE Trans. on Cognitive and Developmental Systems, Vol.15, No.1, pp. 163-174, 2023. https://doi.org/10.1109/TCDS.2022.3149953
12. [12] G. Rodríguez-Bermúdez and P. J. García-Laencina, “Analysis of EEG signals using nonlinear dynamics and chaos: A review,” Applied Mathematics & Information Sciences, Vol.9, No.5, pp. 2309-2321, 2015. https://doi.org/10.12785/amis/090512
13. [13] Y. Lu et al., “Entropy-based pattern learning based on singular spectrum analysis components for assessment of physiological signals,” Complexity, Vol.2020, Article No.4625218, 2020. https://doi.org/10.1155/2020/4625218
14. [14] N. Zhuang et al., “Emotion recognition from EEG signals using multidimensional information in EMD domain,” BioMed Research Int., Vol.2017, Article No.8317357, 2017. https://doi.org/10.1155/2017/8317357
15. [15] Y. Zhang, X. Ji, and S. Zhang, “An approach to EEG-based emotion recognition using combined feature extraction method,” Neuroscience Letters, Vol.633, pp. 152-157, 2016. https://doi.org/10.1016/j.neulet.2016.09.037
16. [16] A. Mert and A. Akan, “Emotion recognition from EEG signals by using multivariate empirical mode decomposition,” Pattern Analysis and Applications, Vol.21, No.1, pp. 81-89, 2018. https://doi.org/10.1007/s10044-016-0567-6
17. [17] Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: A noise-assisted data analysis method,” Advances in Adaptive Data Analysis, Vol.1, No.1, pp. 1-41, 2009. https://doi.org/10.1142/S1793536909000047
18. [18] J.-R. Yeh, J.-S. Shieh, and N. E. Huang, “Complementary ensemble empirical mode decomposition: A novel noise data analysis method,” Advances in Adaptive Data Analysis, Vol.2, No.2, pp. 135-156, 2010. https://doi.org/10.1142/S1793536910000422
19. [19] M. A. Colominas, G. Schlotthauer, and M. E. Torres, “Improved complete ensemble EMD: A suitable tool for biomedical signal processing,” Biomedical Signal Processing and Control, Vol.14, pp. 19-29, 2014. https://doi.org/10.1016/j.bspc.2014.06.009
20. [20] M. Zhang, B. Hu, X. Zheng, and T. Li, “A novel multidimensional feature extraction method based on VMD and WPD for emotion recognition,” 2020 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), pp. 1216-1220, 2020. https://doi.org/10.1109/BIBM49941.2020.9313220
21. [21] P. Pandey and K. R. Seeja, “Subject independent emotion recognition from EEG using VMD and deep learning,” J. of King Saud University – Computer and Information Sciences, Vol.34, No.5, pp. 1730-1738, 2022. https://doi.org/10.1016/j.jksuci.2019.11.003
22. [22] S. K. Khare and V. Bajaj, “An evolutionary optimized variational mode decomposition for emotion recognition,” IEEE Sensors J., Vol.21, No.2, pp. 2035-2042, 2021. https://doi.org/10.1109/JSEN.2020.3020915
23. [23] W.-L. Chu, M.-W. Huang, B.-L. Jian, and K.-S. Cheng, “Analysis of EEG entropy during visual evocation of emotion in schizophrenia,” Annals of General Psychiatry, Vol.16, Article No.34, 2017. https://doi.org/10.1186/s12991-017-0157-z
24. [24] J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American J. of Physiology – Heart and Circulatory Physiology, Vol.278, No.6, pp. H2039-H2049, 2000. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
25. [25] C. Bandt and B. Pompe, “Permutation entropy: A natural complexity measure for time series,” Physical Review Letters, Vol.88, No.17, Article No.174102, 2002. https://doi.org/10.1103/PhysRevLett.88.174102
26. [26] Y. Xia et al., “Application of permutation entropy and permutation min-entropy in multiple emotional states analysis of RRI time series,” Entropy, Vol.20, No.3, Article No.148, 2018. https://doi.org/10.3390/e20030148
27. [27] K. Tung, P.-K. Liu, Y.-C. Chuang, S.-H. Wang, and A.-Y. A. Wu, “Entropy-assisted multi-modal emotion recognition framework based on physiological signals,” 2018 IEEE-EMBS Conf. on Biomedical Engineering and Sciences (IECBES), pp. 22-26, 2018. https://doi.org/10.1109/IECBES.2018.8626634
28. [28] H. Azami, A. Fernández, and J. Escudero, “Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis,” Medical & Biological Engineering & Computing, Vol.55, No.11, pp. 2037-2052, 2017. https://doi.org/10.1007/s11517-017-1647-5
29. [29] B. Fadlallah, B. Chen, A. Keil, and J. Príncipe, “Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information,” Physical Review E, Vol.87, No.2, Article No.022911, 2013. https://doi.org/10.1103/physreve.87.022911
30. [30] H. Azami et al., “Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases,” IEEE Access, Vol.7, pp. 68748-68733, 2019. https://doi.org/10.1109/ACCESS.2019.2918560
31. [31] X.-W. Ding, Z.-T. Liu, D.-Y. Li, Y. He, and M. Wu, “Electroencephalogram emotion recognition based on dispersion entropy feature extraction using random over-sampling imbalanced data processing,” IEEE Trans. on Cognitive and Developmental Systems, Vol.14, No.3, pp. 882-891, 2022. https://doi.org/10.1109/TCDS.2021.3074811
32. [32] S. Liu et al., “Subject-independent emotion recognition of EEG signals based on dynamic empirical convolutional neural network,” IEEE/ACM Trans. on Computational Biology and Bioinformatics, Vol.18, No.5, pp. 1710-1721, 2020. https://doi.org/10.1109/TCBB.2020.3018137
33. [33] H. Li, C. Qing, X. Xu, and T. Zhang, “A novel DE-PCCM feature for EEG-based emotion recognition,” 2017 Int. Conf. on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 389-393, 2017. https://doi.org/10.1109/SPAC.2017.8304310
34. [34] V. M. Joshi and R. B. Ghongade, “Optimal number of electrode selections for EEG-based emotion recognition using linear formulation of differential entropy,” Biomedical and Pharmacology J., Vol.13, No.2, pp. 645-653, 2020. https://doi.org/10.13005/bpj/1928
35. [35] Y.-J. Liu et al., “Real-time movie-induced discrete emotion recognition from EEG signals,” IEEE Trans. on Affective Computing, Vol.9, No.4, pp. 550-562, 2017. https://doi.org/10.1109/TAFFC.2017.2660485
36. [36] S.-J. Hu, Z.-T. Liu, and X.-W. Ding, “Electroencephalogram emotion recognition using variational modal decomposition based dispersion entropy feature extraction,” 2021 40th Chinese Control Conf. (CCC), pp. 3323-3326, 2021. https://doi.org/10.23919/CCC52363.2021.9549884
37. [37] J. Lian, Z. Liu, H. Wang, and X. Dong, “Adaptive variational mode decomposition method for signal processing based on mode Characteristic,” Mechanical Systems and Signal Processing, Vol.107, pp. 53-77, 2018. https://doi.org/10.1016/j.ymssp.2018.01.019
38. [38] V. Gupta, M. D. Chopda, and R. B. Pachori, “Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals,” IEEE Sensors J., Vol.19, No.6, pp. 2266-2274, 2018. https://doi.org/10.1109/JSEN.2018.2883497
39. [39] S. Koelstra et al., “DEAP: A database for emotion analysis; using physiological signals,” IEEE Trans. on Affective Computing, Vol.3, No.1, pp. 18-31, 2012. https://doi.org/10.1109/T-AFFC.2011.15
40. [40] P. Arnau-González, M. Arevalillo-Herráez, and N. Ramzan, “Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals,” Neurocomputing, Vol.244, pp. 81-89, 2017. https://doi.org/10.1016/j.neucom.2017.03.027
41. [41] Y. Ding, N. Robinson, S. Zhang, Q. Zeng, and C. Guan, “TSception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition,” IEEE Trans. on Affective Computing, Vol.14, No.3, pp. 2238-2250, 2023. https://doi.org/10.1109/TAFFC.2022.3169001
42. [42] C. Li, B. Chen, Z. Zhao, N. Cummins, and B. W. Schuller, “Hierarchical attention-based temporal convolutional networks for EEG-based emotion recognition,” 2021 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 1240-1244, 2021. https://doi.org/10.1109/ICASSP39728.2021.9413635
43. [43] N. Zhuang et al., “Emotion recognition from EEG signals using multidimensional information in EMD domain,” BioMed Research Int., Vol.2017, Article No.8317357, 2017. https://doi.org/10.1155/2017/8317357
44. [44] Z.-T. Liu, S.-J. Hu, J. She, Z. Yang, and X. Xu, “Electroencephalogram Emotion recognition using combined features in variational mode decomposition domain,” IEEE Trans. on Cognitive and Developmental Systems, Vol.15, No.3, pp. 1595-1604, 2023. https://doi.org/10.1109/TCDS.2022.3233858
45. [45] Y. Huang, J. Yang, S. Liu, and J. Pan, “Combining facial expressions and electroencephalography to enhance emotion recognition,” Future Internet, Vol.11, No.5, Article No.105, 2019. https://doi.org/10.3390/fi11050105
46. [46] Z.-M. Wang, S.-Y. Hu, and H. Song, “Channel selection method for EEG emotion recognition using normalized mutual information,” IEEE Access, Vol.7, pp. 143303-143311, 2019. https://doi.org/10.1109/ACCESS.2019.2944273
47. [47] Q. Li, Y. Song, J. Zhang, and V. S. Sheng, “Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering,” Expert Systems with Applications, Vol.147, Article No.113152, 2020. https://doi.org/10.1016/j.eswa.2019.113152
48. [48] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Trans. on Knowledge and Data Engineering, Vol.21, No.9, pp. 1263-1284, 2009. https://doi.org/10.1109/TKDE.2008.239
49. [49] W. Lu, H. Hou, and J. Chu, “Feature fusion for imbalanced ECG data analysis,” Biomedical Signal Processing and Control, Vol.41, pp. 152-160, 2018. https://doi.org/10.1016/j.bspc.2017.11.010