基于多域特征与随机子空间集成的脑电分类

doi:10.16182/j.issn1004731x.joss.19-0038

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (9): 1787-1798.doi: 10.16182/j.issn1004731x.joss.19-0038

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

基于多域特征与随机子空间集成的脑电分类

邓欣, 龙灿, 米建勋^*, 张博宪, 孙开伟, 王进

重庆邮电大学计算机科学与技术学院数据工程与可视计算重点实验室, 重庆 400065

收稿日期:2019-01-22 修回日期:2019-12-03 出版日期:2020-09-18 发布日期:2020-09-18
通讯作者: 米建勋(1982-),男,重庆,博士,副教授,研究方向为模式识别。
作者简介:邓欣(1981-),男,重庆,博士,副教授,研究方向为脑机接口;龙灿(1994-),女,重庆,硕士,研究方向为智能信息处理。
基金资助:
国家自然科学基金(61806033),重庆市重点产业共性关键技术创新专项(cstc2017zdcy-zdyfX 0012),国家社会科学基金西部项目(18XGL013)

EEG Classification Based on Multi-domain Features and Random Subspace Ensemble

Deng Xin, Long Can, Mi Jianxun^*, Zhang Boxian, Sun Kaiwei, Wang Jin

Key Laboratory of Data Engineering and Visual Computing, College of Computer Science and Technology Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2019-01-22 Revised:2019-12-03 Online:2020-09-18 Published:2020-09-18

摘要/Abstract

摘要： 针对脑机接口(Brain-Computer Interface,BCI)中脑电信号预处理、特征提取、分类识别等过程,提出一种基于多域特征的随机子空间集成方法实现运动想象脑电分类。该方法的基本思想是通过事件相关同步/事件相关去同步特性分析,提取出最佳时频段的多域特征作为特征向量,结合交叉验证自适应地选择特征随机子空间的集成规模,集成线性判别分析分类器实现脑电信号分类。实验结果表明,多域特征和随机子空间集成分类正确率可达90.71%、Kappa系数可达0.63,均优于BCI竞赛第一名成绩,从而证明了该算法在脑电分类中的有效性和实用性。

关键词: 脑电信号, 运动想象, 多域特征, 集成学习, 随机子空间

Abstract: Aiming at the preprocessing feature extraction and classification recognition in BCI system, a method for EEG classification of motion imagery based on random subspaces ensemble learning of multi-domain features is proposed. Based on the analysis on the ERD/ERS characteristics of motion imagery (MI) signals, the multi-domain features of best effective time and frequency bands are extracted as the feature vectors, and the scale of the random subspace ensemble with cross-validation is adaptively chosen, and the EEG classification is realized by using linear discriminant analysis (LDA) classifiers ensemble. The test results show that the accuracy of the multi-domain features and random subspace ensemble can reach 90.71% and the Kappa coefficient can be 0.63,which are better than those of the first place in the competition, and thus prove the algorithm's effectiveness and progressiveness.

Key words: EEG signal, motion imagery, multi-domain features, ensemble learning, random subspace

中图分类号:

TP391.9

邓欣, 龙灿, 米建勋, 张博宪, 孙开伟, 王进. 基于多域特征与随机子空间集成的脑电分类[J]. 系统仿真学报, 2020, 32(9): 1787-1798.

Deng Xin, Long Can, Mi Jianxun, Zhang Boxian, Sun Kaiwei, Wang Jin. EEG Classification Based on Multi-domain Features and Random Subspace Ensemble[J]. Journal of System Simulation, 2020, 32(9): 1787-1798.

参考文献

[1] Wolpaw J R, Birbaumer N, Heetderks W J, et al.Brain-computer interface technology: a review of the first international meeting[J]. International Conference of the IEEE Engineering in Medicine and Biology Society (S1063-6528), 2000, 8(2): 164-173.
[2] 陈超, 平尧, 郝斌, 等. 基于脑机接口技术的写字系统建模仿真与实现[J]. 系统仿真学报, 2018, 30(12): 4499-4505.
Chen Chao, Ping Yao, Hao Bin, et al.Modeling, Simulation and Realization of Writing System Based on BCI Technology[J]. Journal of System Simulation, 2018, 30(12): 4499-4505.
[3] Chavez M, Grosselin F, Bussalb A, et al.Surrogate-Based Artifact Removal From Single-Channel EEG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering (S1534-4320), 2018, 26(3): 540-550.
[4] 闫国强, 周宁宁, 张少白. 基于MMTD与小波硬阈值的脑电信号去噪方法[J]. 系统仿真学报, 2018, 30(4): 1490-1495.
Yan Guoqiang, Zhou Ningning, Zhang Shaobai.De-noising Method of EEG Signal Based on MMTD and Wavelet Hard-threshold[J]. Journal of System Simulation, 2018, 30(4): 1490-1495.
[5] Zhang Yu, Wang Bei, Jing Jin, et al.A Comparison Study on Multidomain EEG Features for Sleep Stage Classification[J]. Computational Intelligence and Neuroscience (S1687-5265), 2017: 1-8.
[6] Lin S, Guo S, Huang Z, et al.Determining AR order for BCI based on motor imagery[C]. Biomedical Engineering and Informatics. Shenyang: IEEE Press, 2015: 174-178.
[7] Song Y J, Sepulveda F.A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering (S1534-4320), 2018(99): 1.
[8] 裴一飞, 杨淑娟. 运动想象脑电信号算法研究进展[J].北京生物医学工程, 2018, 37(2): 208-214.
Pei Yifei, Yang Shujuan.Research progress on motor imagery EEG signals[J]. Beijing Biomedical Engineering, 2018, 37(2): 208-214.
[9] Wang L, Xu G, Wang J, et al.Motor Imagery BCI Research Based on Sample Entropy and SVM[C]. International Conference on Electromagnetic Field Problems and Applications. Dalian: IEEE Press, 2012: 1-4.
[10] 孟建军, 盛鑫军, 姚林, 等. 基于共空域频谱模式的少通道运动想象分类[J]. 中国生物医学工程学报, 2013, 32(5): 553-561.
Meng Jianjun, Sheng Xinjun, Yao Lin, et al.Common Spatial Spectral Pattern for Motor Imagery Tasks in Small Channel Configuration[J]. Chinese Journal of Biomedical Engineering, 2013, 32(5): 553-561.
[11] Tan C Q, Sun F C, Zhang W C, et al.Spatial and spectral features fusion for EEG classification during motor imagery in BCI[C]. 2017 IEEE EMBS International Conference on Biomedical Health Informatics (BHI). Orlando: IEEE Press, 2017: 309-312.
[12] 王月茹, 李昕, 李红红, 等. 基于时-频-空间域的运动想象脑电信号特征提取方法研究[J]. 生物医学工程学杂志, 2014, 31(5): 955-961.
Wang Yueru, Li Xin, Li Honghong, et al.Feature Extraction of Motor Imagery Electroencephalography Based on Time-frequency-space Domanis[J]. Journal of Biomedical Engineering, 2014, 31(5): 955-961.
[13] 徐宝国, 宋爱国, 费树岷. 在线脑机接口中脑电信号的特征提取与分类方法[J]. 电子学报, 2011, 39(5): 1025-1030.
Xu Baoguo, Song Aiguo, Fei Shumi.Feature Extraction and Classification of EEG in Online Brain-Computer Interface[J]. Acta E1 Ecrromca Simca, 2011, 39(5): 1025-1030.
[14] Bhattacharyya S, Konar A, Tibarewala D N, et al.Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal[C]. International Conference on Control Instrumentation Energy Communication. Calcutta: IEEE Press, 2014: 712-716.
[15] 李明爱, 杨林豹, 杨金福. 具有在线自学习能力的脑电信号分类方法[J]. 计算机测量与控制, 2011, 19(11): 2763-2765, 2784.
Li Mingai, Yang Linbao, Yang Jinfu.A online Self--learning Approach to EEG Classification[J]. Computer Measurement & Control, 2011, 19(11): 2763-2765, 2784.
[16] Nicolasalonso L F, Corralejo R, Gomezpilar J, et al.Ensemble learning for classification of motor imagery tasks in multiclass brain computer interfaces[C]. Computer Science and Electronic Engineering Conference. Colchester: IEEE Press, 2014: 79-84.
[17] 王夏爽, 龚光红, 李妮. 视觉诱发脑电信号的处理研究[J]. 系统仿真学报, 2017, 29(增1): 146-154.
Wang Xiashuang, Gong Guanghong, Li Ni.Study and Processing of Visual Evoked EEG[J]. Journal of System Simulation, 2017, 29(S1): 146-154.
[18] Kevric J, Subasi A.Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system[J]. Biomedical Signal Processing and Control (S1746-8094), 2017, 31: 398-406.
[19] Jirayucharoensak S, Israsena P, Panngum S, et al.Online EEG artifact suppression for neurofeedback training systems[C]. Biomedical Engineering International Conference. Amphur Muang: IEEE Press, 2013: 1-5.
[20] Hosseini M, Hajisami A, Pompili D, et al.Real-Time Epileptic Seizure Detection from EEG Signals via Random Subspace Ensemble Learning[C]. International Conference on Autonomic Computing. Wurzburg: IEEE Press, 2016: 209-218.

基于多域特征与随机子空间集成的脑电分类

EEG Classification Based on Multi-domain Features and Random Subspace Ensemble

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	金炜东, 陈春利. 基于集成深度学习的雷达信号分选研究[J]. 系统仿真学报, 2019, 31(9): 1868-1874.
[2]	吴迎年, 贺梦嘉, 项伟. 基于集成学习的人眼定位和头部状态检测研究[J]. 系统仿真学报, 2019, 31(11): 2366-2373.
[3]	闫国强, 周宁宁, 张少白. 基于MMTD与小波硬阈值的脑电信号去噪方法[J]. 系统仿真学报, 2018, 30(4): 1490-1495.
[4]	冯建奎, 金晶, 王蓓, 牛玉刚, 王行愚. 基于SVM_RFE的多任务导联选择算法建模[J]. 系统仿真学报, 2018, 30(12): 4506-4512.