Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram_Journal of Biomedical Engineering

Authors：

 XIE Songyun ¹ , LEI Lingjun ² , SUN Jiang ¹ , XU Jian ¹

1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, P. R. China;
2. Medical Research Institute, Northwestern Polytechnical University, Xi’an 710129, P. R. China;

Corresponding author：

XIE Songyun, Email: syxie@nwpu.edu.cn

Keywords：

Electroencephalogram; Emotion recognition; Whale optimization algorithm; Feature selection; Extreme learning machine

DOI：

10.7507/1001-5515.202303010

Video：

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Abstract Full text Figures/Tables Video References Cited by

Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.

Citation： XIE Songyun, LEI Lingjun, SUN Jiang, XU Jian. Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram. Journal of Biomedical Engineering, 2024, 41(1): 1-8. doi: 10.7507/1001-5515.202303010 Copy

1.	Hou H R, Zhang X N, Meng Q H. Odor-induced emotion recognition based on average frequency band division of EEG signals. J Neurosci Meth, 2020, 334: 108599.
2.	Xing M, Hu S, Wei B, et al. Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition. J Neurosci Meth, 2022, 376: 109624.
3.	柳素红, 孙晓, 李春彬. 基于位置信息重建与时频域信息融合的脑电信号情感识别. 计算机工程, 2021, 47(12): 95-102.
4.	Gao Y, Wang X, Potter T, et al. Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis. J Neurosci Meth, 2020, 346: 108904.
5.	冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述. 工程科学学报, 2021, 43(6): 745-753.
6.	杨晓敏. 改进灰狼算法优化支持向量机的网络流量预测. 电子测量与仪器学报, 2021, 35(3): 211-217.
7.	García-Martínez B, Martínez-Rodrigo A, Alcaraz R, et al. Nonlinear methodologies applied to automatic recognition of emotions: an EEG review// Ochoa S, Singh P, Bravo J. Proceedings of Ubiquitous Computing and Ambient Intelligence: 11th International Conference, UCAmI 2017. Cham: Springer International Publishing, 2017, 10586: 754-765.
8.	Shan X, Yang E H, Zhou J, et al. Neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality. Energ Buildings, 2019, 197: 188-195.
9.	Somol P, Pudil P, Kittler J. Fast branch & bound algorithms for optimal feature selection. IEEE Trans Pattern Anal Mach Intell, 2004, 26(7): 900-912.
10.	Ahmed M A, Deyu Q, Alshemmary E N. Electroencephalogram signal eye blink rejection improvement based on the hybrid stone blind origin separation and particle swarm optimization technique. IEEE Access, 2020, 8: 105671-105680.
11.	Ghorbanzadeh G, Nabizadeh Z, Karimi N, et al. DGAFF: Deep genetic algorithm fitness Formation for EEG Bio-Signal channel selection. Biomed Signal Proces, 2023, 79: 104119.
12.	Liu K, Dong W, Dong H, et al. A complex fault diagnostic approach of active distribution network based on SBS-SFS optimized multi-SVM. Math Probl Eng, 2020, 2020: 525-542.
13.	Martínez-Tejada L A, Puertas-González A, Yoshimura N, et al. Exploring EEG characteristics to identify emotional reactions under videogame scenarios. Brain Sci, 2021, 11(3): 378.
14.	Song T, Zheng W, Liu S, et al. Graph-embedded convolutional neural network for image-based EEG emotion recognition. IEEE T Emerg Top Com, 2021, 10(3): 1399-1413.
15.	Sarma P, Barma S. Emotion recognition by distinguishing appropriate EEG segments based on random matrix theory. Biomed Signal Proces, 2021, 70: 102991.
16.	Atkinson J, Campos D. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl, 2016, 47: 35-41.
17.	Liu H, Zhang Y, Li Y, et al. Review on emotion recognition based on electroencephalography. Front Comput Neurosc, 2021, 15: 84.
18.	Eid M M, Alassery F, Ibrahim A, et al. Metaheuristic optimization algorithm for signals classification of electroencephalography channels. Comput Mater Con, 2022, 71(3): 4627-4641.
19.	Lv Z, Zhang J, Epota Oma E. A novel method of emotion recognition from multi-band EEG topology maps based on ERENet. Sn Appl Sci, 2022, 12(20): 10273.
20.	Li Y, Zheng W. EEG processing in emotion recognition: inspired from a musical staff. MultimedTools Appl, 2023, 82(3): 4161-4180.
21.	Mohammed H M, Umar S U, Rashid T A. A systematic and meta-analysis survey of whale optimization algorithm. Comput Intel Neurosc, 2019, 2019: 2-14.
22.	Tong W. A hybrid algorithm framework with learning and complementary fusion features for whale optimization algorithm. Sci Programming, 2020, 2020: 1-25.
23.	Liang X, Xu S, Liu Y, et al. A modified whale optimization algorithm and its application in seismic inversion problem. Mob Inf Syst, 2022, 2022: 1-18.
24.	Kushwah R, Kaushik M, Chugh K. A modified whale optimization algorithm to overcome delayed convergence in artificial neural networks. Soft Comput, 2021, 25(15): 10275-10286.
25.	Li Y, Han T, Zhao H, et al. An adaptive whale optimization algorithm using Gaussian distribution strategies and its application in heterogeneous UCAVs task allocation. IEEE Access, 2019, 7: 110138-110158.
26.	Yıldırım H, Özkale M R. An enhanced extreme learning machine based on Liu regression. Neural Process Lett, 2020, 52: 421-442.
27.	Zhang G, Cui D, Mao S, et al. Unsupervised feature learning with sparse Bayesian auto-encoding based extreme learning machine. Int J Mach Learn Cyb, 2020, 11(7): 1557-1569.
28.	Liu T, Fan Q, Kang Q, et al. Extreme learning machine based on firefly adaptive flower pollination algorithm optimization. Processes, 2020, 8(12): 1583.
29.	Wang Z M, Hu S Y, Song H. Channel selection method for EEG emotion recognition using normalized mutual information. IEEE Access, 2019, 7: 143303-143311.
30.	Fang Y, Yang H, Zhang X, et al. Multi-feature input deep forest for EEG-based emotion recognition. Front Neurorobot, 2021, 14: 617531.
31.	Chen D, Miao R, Deng Z, et al. Sparse granger causality analysis model based on sensors correlation for emotion recognition classification in electroencephalography. Front Comput Neurosci, 2021, 15: 684373.
32.	Qiao W, Wang Y, Zhang J, et al. An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration. J Environ Manage, 2021, 289: 112438.
33.	Zheng W L, Lu B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE T Auton Ment De, 2015, 7(3): 162-175.
34.	Wang Z, Tong Y, Heng X. Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access, 2019, 7: 93711-93722.
35.	Li J, Pan W, Huang H, et al. STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition. Front Hum Neurosci, 2023, 17: 1169949.
36.	Gao Z, Wang X, Yang Y, et al. A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE T Cogn Dev Syst, 2020, 13(4): 945-954.

1. Hou H R, Zhang X N, Meng Q H. Odor-induced emotion recognition based on average frequency band division of EEG signals. J Neurosci Meth, 2020, 334: 108599.
2. Xing M, Hu S, Wei B, et al. Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition. J Neurosci Meth, 2022, 376: 109624.
3. 柳素红, 孙晓, 李春彬. 基于位置信息重建与时频域信息融合的脑电信号情感识别. 计算机工程, 2021, 47(12): 95-102.
4. Gao Y, Wang X, Potter T, et al. Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis. J Neurosci Meth, 2020, 346: 108904.
5. 冯茜, 李擎, 全威, 等. 多目标粒子群优化算法研究综述. 工程科学学报, 2021, 43(6): 745-753.
6. 杨晓敏. 改进灰狼算法优化支持向量机的网络流量预测. 电子测量与仪器学报, 2021, 35(3): 211-217.
7. García-Martínez B, Martínez-Rodrigo A, Alcaraz R, et al. Nonlinear methodologies applied to automatic recognition of emotions: an EEG review// Ochoa S, Singh P, Bravo J. Proceedings of Ubiquitous Computing and Ambient Intelligence: 11th International Conference, UCAmI 2017. Cham: Springer International Publishing, 2017, 10586: 754-765.
8. Shan X, Yang E H, Zhou J, et al. Neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality. Energ Buildings, 2019, 197: 188-195.
9. Somol P, Pudil P, Kittler J. Fast branch & bound algorithms for optimal feature selection. IEEE Trans Pattern Anal Mach Intell, 2004, 26(7): 900-912.
10. Ahmed M A, Deyu Q, Alshemmary E N. Electroencephalogram signal eye blink rejection improvement based on the hybrid stone blind origin separation and particle swarm optimization technique. IEEE Access, 2020, 8: 105671-105680.
11. Ghorbanzadeh G, Nabizadeh Z, Karimi N, et al. DGAFF: Deep genetic algorithm fitness Formation for EEG Bio-Signal channel selection. Biomed Signal Proces, 2023, 79: 104119.
12. Liu K, Dong W, Dong H, et al. A complex fault diagnostic approach of active distribution network based on SBS-SFS optimized multi-SVM. Math Probl Eng, 2020, 2020: 525-542.
13. Martínez-Tejada L A, Puertas-González A, Yoshimura N, et al. Exploring EEG characteristics to identify emotional reactions under videogame scenarios. Brain Sci, 2021, 11(3): 378.
14. Song T, Zheng W, Liu S, et al. Graph-embedded convolutional neural network for image-based EEG emotion recognition. IEEE T Emerg Top Com, 2021, 10(3): 1399-1413.
15. Sarma P, Barma S. Emotion recognition by distinguishing appropriate EEG segments based on random matrix theory. Biomed Signal Proces, 2021, 70: 102991.
16. Atkinson J, Campos D. Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl, 2016, 47: 35-41.
17. Liu H, Zhang Y, Li Y, et al. Review on emotion recognition based on electroencephalography. Front Comput Neurosc, 2021, 15: 84.
18. Eid M M, Alassery F, Ibrahim A, et al. Metaheuristic optimization algorithm for signals classification of electroencephalography channels. Comput Mater Con, 2022, 71(3): 4627-4641.
19. Lv Z, Zhang J, Epota Oma E. A novel method of emotion recognition from multi-band EEG topology maps based on ERENet. Sn Appl Sci, 2022, 12(20): 10273.
20. Li Y, Zheng W. EEG processing in emotion recognition: inspired from a musical staff. MultimedTools Appl, 2023, 82(3): 4161-4180.
21. Mohammed H M, Umar S U, Rashid T A. A systematic and meta-analysis survey of whale optimization algorithm. Comput Intel Neurosc, 2019, 2019: 2-14.
22. Tong W. A hybrid algorithm framework with learning and complementary fusion features for whale optimization algorithm. Sci Programming, 2020, 2020: 1-25.
23. Liang X, Xu S, Liu Y, et al. A modified whale optimization algorithm and its application in seismic inversion problem. Mob Inf Syst, 2022, 2022: 1-18.
24. Kushwah R, Kaushik M, Chugh K. A modified whale optimization algorithm to overcome delayed convergence in artificial neural networks. Soft Comput, 2021, 25(15): 10275-10286.
25. Li Y, Han T, Zhao H, et al. An adaptive whale optimization algorithm using Gaussian distribution strategies and its application in heterogeneous UCAVs task allocation. IEEE Access, 2019, 7: 110138-110158.
26. Yıldırım H, Özkale M R. An enhanced extreme learning machine based on Liu regression. Neural Process Lett, 2020, 52: 421-442.
27. Zhang G, Cui D, Mao S, et al. Unsupervised feature learning with sparse Bayesian auto-encoding based extreme learning machine. Int J Mach Learn Cyb, 2020, 11(7): 1557-1569.
28. Liu T, Fan Q, Kang Q, et al. Extreme learning machine based on firefly adaptive flower pollination algorithm optimization. Processes, 2020, 8(12): 1583.
29. Wang Z M, Hu S Y, Song H. Channel selection method for EEG emotion recognition using normalized mutual information. IEEE Access, 2019, 7: 143303-143311.
30. Fang Y, Yang H, Zhang X, et al. Multi-feature input deep forest for EEG-based emotion recognition. Front Neurorobot, 2021, 14: 617531.
31. Chen D, Miao R, Deng Z, et al. Sparse granger causality analysis model based on sensors correlation for emotion recognition classification in electroencephalography. Front Comput Neurosci, 2021, 15: 684373.
32. Qiao W, Wang Y, Zhang J, et al. An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration. J Environ Manage, 2021, 289: 112438.
33. Zheng W L, Lu B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE T Auton Ment De, 2015, 7(3): 162-175.
34. Wang Z, Tong Y, Heng X. Phase-locking value based graph convolutional neural networks for emotion recognition. IEEE Access, 2019, 7: 93711-93722.
35. Li J, Pan W, Huang H, et al. STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition. Front Hum Neurosci, 2023, 17: 1169949.
36. Gao Z, Wang X, Yang Y, et al. A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE T Cogn Dev Syst, 2020, 13(4): 945-954.

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