Qian Cai, Guo-Chong Cui, Hai-Xian Wang. EEG-based Emotion Recognition Using Multiple Kernel Learning. Machine Intelligence Research, vol. 19, no. 5, pp.472-484, 2022. https://doi.org/10.1007/s11633-022-1352-1
Citation: Qian Cai, Guo-Chong Cui, Hai-Xian Wang. EEG-based Emotion Recognition Using Multiple Kernel Learning. Machine Intelligence Research, vol. 19, no. 5, pp.472-484, 2022. https://doi.org/10.1007/s11633-022-1352-1

EEG-based Emotion Recognition Using Multiple Kernel Learning

doi: 10.1007/s11633-022-1352-1
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  • Author Bio:

    Qian Cai received the B. Sc. and M. Sc. degrees in mathematics from Anhui University, China in 2000 and 2003, respectively. Currently, she is with School of Statistics and Data Science, Nanjing Audit University, China. Her research interests include statistical pattern recognition and data science.E-mail: caiq@nau.edu.cn ORCID iD: 0000-0001-7255-6321

    Guo-Chong Cui received the B. Sc. degree in biomedical engineering from Yanshan University, China in 2019. He is a master student in biomedical engineering at Department of Biomedical Engineering, School of Biological Science & Medical Engineering, Southeast University, China. His research interests include EEG-based emotion recognition, machine learning and neural information processing. E-mail: gc_cui@seu.edu.cn (Corresponding author) ORCID iD: 0000-0002-1929-0154

    Hai-Xian Wang received the B. Sc. and M. Sc. degrees in statistics and the Ph. D. degree in computer science from Anhui University, China in 1999, 2002 and 2005, respectively. Currently, he is with Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, China. His research interests include biomedical signal processing, brain-computer interfaces, and machine learning. E-mail: hxwang@seu.edu.cn (Corresponding author) ORCID iD: 0000-0001-8220-9737

  • Received Date: 2022-03-31
  • Accepted Date: 2022-06-20
  • Publish Online: 2022-09-03
  • Publish Date: 2022-10-01
  • 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.


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