EEG-based Emotion Recognition Using Multiple Kernel Learning

作     者：Qian Cai Guo-Chong Cui Hai-Xian Wang Qian Cai;Guo-Chong Cui;Hai-Xian Wang

作者机构：School of Statistics and Data ScienceNanjing Audit UniversityNanjing 211815China Key Laboratory of Child Development and Learning Science of Ministry of EducationSchool of Biological Science&Medical EngineeringSoutheast UniversityNanjing 210096China Institute of Artificial Intelligence of Hefei Comprehensive National Science CenterHefei 230094China 

出 版 物：《Machine Intelligence Research》 (机器智能研究（英文版）)

年 卷 期：2022年第19卷第5期

页      面：472-484页

核心收录：

学科分类：0711[理学-系统科学] 1002[医学-临床医学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 100204[医学-神经病学] 10[医学] 

基　　金：supported by National Natural Science Foundation of China(No.62176054) University Synergy Innovation Program of Anhui Province,China(No.GXXT-2020-015) 

主　　题：Emotion recognition electroencephalography(EEG) multiple kernel learning machine learning brain computer interface 

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 ***,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high ***,there is little research on MKL in EEG-based emotion ***,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 ***,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 *** designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical ***,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 *** final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further *** 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 *** is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion *** summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EE