EEG-based Emotion Recognition Using Multiple Kernel Learning

doi:10.1007/s11633-022-1352-1

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	EEG-based Emotion Recognition Using Multiple Kernel Learning
	Qian Cai 3; Guo-Chong Cui 2; Hai-Xian Wang 1,2
刊名	Machine Intelligence Research
	2022
卷号	19 期号:5 页码:472-484
关键词	Emotion recognition electroencephalography (EEG) multiple kernel learning machine learning brain computer interface
ISSN号	2731-538X
DOI	10.1007/s11633-022-1352-1
英文摘要	Emotion recognition based on electroencephalography (EEG) has a wide range of applications and has great potential value, so it has received increasing attention from academia and industry in recent years. Meanwhile, multiple kernel learning (MKL) has also been favored by researchers for its data-driven convenience and high accuracy. However, there is little research on MKL in EEG-based emotion recognition. Therefore, this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition. Thus, we proposed a support vector machine (SVM) classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems. We designed two data partition methods, random division to verify the validity of the MKL method and sequential division to simulate practical applications. Then, tri-categorization experiments were performed for neutral, negative and positive emotions based on a commonly used dataset, the Shanghai Jiao Tong University emotional EEG dataset (SEED). The average classification accuracies for random division and sequential division were 92.25% and 74.37%, respectively, which shows better classification performance than the traditional single kernel SVM. The final results show that the MKL method is obviously effective, and the application of MKL in EEG emotion recognition is worthy of further study. Through the analysis of the experimental results, we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance. It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition. In summary, this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55957]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, China 2.Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China 3.School of Statistics and Data Science, Nanjing Audit University, Nanjing 211815, China
推荐引用方式 GB/T 7714	Qian Cai,Guo-Chong Cui,Hai-Xian Wang. EEG-based Emotion Recognition Using Multiple Kernel Learning[J]. Machine Intelligence Research,2022,19(5):472-484.
APA	Qian Cai,Guo-Chong Cui,&Hai-Xian Wang.(2022).EEG-based Emotion Recognition Using Multiple Kernel Learning.Machine Intelligence Research,19(5),472-484.
MLA	Qian Cai,et al."EEG-based Emotion Recognition Using Multiple Kernel Learning".Machine Intelligence Research 19.5(2022):472-484.