基于HHT运动想象脑电模式识别研究

孙会文; 伏云发; 熊馨; 杨俊; 刘传伟; 余正涛

doi:10.16383/j.aas.2015.c150007

基于HHT运动想象脑电模式识别研究

doi: 10.16383/j.aas.2015.c150007

1.
昆明理工大学信息工程与自动化学院昆明 650500

基金项目:

国家自然科学基金(81470084,61463024),云南省应用基础研究计划(2013FB026),云南省级人培项目(KKSY201303048),云南省教育厅重点项目(2013Z130),昆明理工大学脑信息处理与脑机交互融合控制(学科方向团队建设经费)资助

详细信息

作者简介:
孙会文昆明理工大学信息工程与自动化学院硕士研究生.主要研究方向为脑信息处理与脑机交互控制,模式识别与智能控制.E-mail:sunien@163.com

熊馨昆明理工大学信息工程与自动化学院讲师.主要研究方向为医学图像处理与模式识别,脑网络连通性,脑信息处理与脑机交互.E-mail:xiongxin840826@163.com

杨俊昆明理工大学信息工程与自动化学院实验师.主要研究方向为脑机交互控制与通信,脑网络连通性.E-mail:paradisewolf@126.com

刘传伟昆明理工大学信息工程与自动化学院硕士研究生.主要研究方向为脑信息处理与脑机交互控制,模式识别与智能控制.E-mail:binghe111@126.com

余正涛昆明理工大学信息工程与自动化学院教授.主要研究方向为智能信息处理.E-mail:ztyu@hotmail.com

通讯作者:
伏云发昆明理工大学信息工程与自动化学院副教授.主要研究方向为模式识别与智能控制,脑信息处理与脑控机器人接口,脑-机交互控制理论和方法,脑网络连通性.本文通信作者.E-mail:fyf@ynu.edu.cn

计量
- 文章访问数: 2284
- HTML全文浏览量: 181
- PDF下载量: 1664
- 被引次数: 37
出版历程
- 收稿日期: 2015-01-08
- 修回日期: 2015-05-28
- 刊出日期: 2015-09-20

Identification of EEG Induced by Motor Imagery Based on Hilbert-Huang Transform

1.
Faculty of Information Engineering and Automation, Kunming University Science and Technology, Kunming 650500

Funds:

Supported by National Natural Science Foundation of China (81470084, 61463024), Research Project for Application Foundation of Yunnan Province (2013FB026), Cultivation Program of Talents of Yunnan Province (KKSY201303048), Focal Program for Education Office of Yunnan Province (2013Z130), and Brain Information Processing and Brain-computer Interaction Fusion Control of Kunming University Science and Technology (Fund of Discipline Direction Team)

摘要

摘要: 脑机接口是一种变革性的人机交互, 其中基于运动想象(Motor imagery, MI)脑电的脑机接口是一类非常重要的脑机交互. 本文旨在探索有效的运动想象脑电特征模式提取方法. 采用在时域、频域同时具有很高分辨率的希尔伯特--黄变换(Hilbert-Huang transform, HHT),进而提取自回归(Auto regressive, AR)模型参数并计算运动想象脑电平均瞬时能量,从而构造特征向量, 最后利用能较好地适应运动想象脑电单次试验分类的支持向量机(Support vector machine, SVM)进行分类. 结果表明在Trial的5.5～7.5s期间, HHT特征提取方法平均分类正确率为81.08%, 具有良好的适应性;最高分类正确率为87.86%, 优于传统的小波变换特征提取方法和未经HHT的特征提取方法;在Trial的8～9s期间, HHT特征提取方法显著优于后两种特征提取方法. 本研究证实了HHT对运动想象脑电这一非平稳非线性信号具有很好的特征提取能力, 也再次验证了运动想象事件相关去同步(Event-related desynchronization, ERD)现象, 同时也表明运动想象脑电的脑--机交互系统性能与被试想象心理活动的质量密切相关. 本文可望为基于运动想象脑电的在线实时脑机交互控制系统的研究打下坚实的基础.
- 脑机交互控制 /
- 脑机接口 /
- 运动想象 /
- 脑电 /
- 希尔伯特--黄变换
Abstract: Brain-computer interface is a revolutionary human-computer interaction. The brain-computer interface based on electroencephalogram (EEG） induced by motor imagery (MI) is a very important kind of brain-computer interface. The purpose of this paper is to explore the effective features extraction method for EEG induced by motor imagery. Hilbert-Huang transform (HHT) is used, which has a high resolution both in time domain and frequency domain. Auto regressive (AR) parameters are then extracted and the average instantaneous energy of motor imagery is calculated. Thus structural feature vector is constructed. Finally, support vector machine (SVM) is used for classification of EEG induced by motor imagery. The results show that for the 5.5 to 7.5 seconds of the trial, the average classification accuracy of HHT feature extraction method is 81.08%, and thus this method has a good adaptability.　Moreover, the highest classification accuracy of 87.86% is achieved by HHT which is superior to the feature extraction methods using the traditional wavelet transform and without HTT. For the 8 to 9 seconds of the trial, HHT feature extraction method is also significantly better than other two feature extraction methods. This study confirms that HHT has good feature extraction ability for EEG induced by motor imagery which is nonstationary and nonlinear signal. It also confirms the event-related desynchronization (ERD) phenomenon of motor imagery. It is shown that the performance of brain-computer interaction system based on EEG induced by motor imagery is closely related to the performance of the subject's imagination mental activity. This paper can lay a solid foundation for research of online real-time brain-computer interaction control system based on motor imagery.
- Brain-computer interaction control /
- brain-computer interface /
- motor imagery (MI) /
- electroencephalogram (EEG) /
- Hilbert-Huang transform (HHT)

HTML全文

参考文献(35)

[1]	Schmidt E M, McIntosh J S, Durelli L, Bak M J. Fine control of operantly conditioned firing patterns of cortical neurons. Experimental Neurology, 1978, 61(2): 349-369
[2]	Gao Shang-Kai. Comments on recent progress and challenges in the study of brain-computer interface. Chinese Journal of Biomedical Engineering, 2007, 26(6): 801-809(高上凯. 浅谈脑--机接口的发展现状与挑战. 中国生物医学工程学报, 2007, 26(6): 801-809)
[3]	Wang Xing-Yu, Jin Jing, Zhang Yu, Wang Bei. Brain control: human-computer integration control based on brain-computer interface. Acta Automatica Sinica, 2013, 39(3): 208-221(王行愚, 金晶, 张宇, 王蓓. 脑控: 基于脑--机接口的人机融合控制. 自动化学报, 2013, 39(2): 208-221)
[4]	Fu Yun-Fa, Wang Yue-Chao, Li Hong-Yi, Xu Bao-Lei, Li Yong-Cheng. Direct brain-controlled robot interface technology. Acta Automatica Sinica, 2012, 38(8): 1229-1246(伏云发, 王越超, 李洪谊, 徐保磊, 李永程. 直接脑控机器人接口技术. 自动化学报, 2012, 38(8): 1229-1246)
[5]	Yao De-Zhong, Liu Tie-Jun, Lei Xu, Yang Ping, Xu Peng, Zhang Yang-Song. Electroencephalogram based brain-computer interface: key techniques and application prospect. Journal of University of Electronic Science and Technology of China, 2009, 38(5): 550-554(尧德中, 刘铁军, 雷旭, 杨平, 徐鹏, 张杨松. 基于脑电的脑--机接口: 关键技术和应用前景. 电子科技大学学报, 2009, 38(5): 550-554)
[6]	Fu Y F, Xu B L, Li Y C, Wang Y C, Yu Z T, Li H Y. Single-trial decoding of imagined grip force parameters involving the right or left hand based on movement-related cortical potentials. Chinese Science Bulletin, 2014, 59(16): 1907-1916
[7]	Li Ming-Ai, Cui Yan, Yang Jin-Fu. Research on removing ocular artifact automatically from EEG signals. Acta Electronica Sinica, 2013, 41(6): 1207-1213 (李明爱, 崔燕, 杨金福. 脑电信号中眼电伪迹自动去除方法的研究. 电子学报, 2013, 41(6): 1207-1213)
[8]	Zeng H, Song A G, Yan R Q, Qin H Y. EOG artifact correction from EEG recording using stationary subspace analysis and empirical mode decomposition. Sensors, 2013, 13(11): 14839-14859
[9]	Lehnertz K. Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy---an overview. International Journal of Psychophysiology, 1999, 34(1): 45-52
[10]	Schlögl A, Flotzinger D, Pfurtscheller G. Adaptive autoregressive modeling used for single-trial EEG classification. Biomedical Engineering-Biomedizinische Technik, 1997, 42(6): 162-167
[11]	D'Croz-Baron D, Ramirez J M, Baker M, Alarcon-Aquino V, Carrera O. A BCI motor imagery experiment based on parametric feature extraction and fisher criterion. In: Proceedings of the 22nd International Conference on Electrical Communications and Computers (CONIELECOMP). Cholula, Puebla: IEEE, 2012. 257-261
[12]	Zhou Z X, Wan B K. Wavelet packet-based independent component analysis for feature extraction from motor imagery EEG of complex movements. Clinical Neurophysiology, 2012, 123(9): 1779-1788
[13]	Hsu W Y. EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. Journal of Neuroscience Methods, 2010, 189(2): 295-302
[14]	Yang H, Wu S. EEG classification for BCI based on CSP and SVM-GA. Applied Mechanics and Materials, 2013, 459: 228-231
[15]	Zhang R, Xu P, Liu T J, Zhang Y S, Guo L J, Li P Y, Yao D Z. Local temporal correlation common spatial patterns for single trial EEG classification during motor imagery. Computational and Mathematical Methods in Medicine, 2013, 2013: Article ID 591216
[16]	Fu K, Qu J F, Chai Y, Dong Y. Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomedical Signal Processing and Control, 2014, 13: 15-22
[17]	Gong P, Chen M Y, Zhang L. EEMD-based selection of time-frequency patterns for motor imagery EEG. Journal of Computational Information Systems, 2013, 9(22): 9211-9218
[18]	Bajaj V, Pachori R B. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(6): 1135-1142
[19]	Pachori R B, Bajaj V. Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer Methods and Programs in Biomedicine, 2011, 104(3): 373-381
[20]	Li S F, Zhou W D, Yuan Q, Geng S J, Cai D M. Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology and Medicine, 2013, 43(7): 807-816
[21]	Pfurtscheller G, Neuper C. Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neuroscience Letters, 1994, 174(1): 93-96
[22]	Wolpaw J R, Birbaumer N, McFarland D J, Pfurtscheller G, Vaughan T M. Brain-computer interfaces for communication and control. Clinical Neurophysiology, 2002, 113(6): 767-791
[23]	Alvarez-Meza A M, Velasquez-Martinez L F, Castellanos-Dominguez G. Time-series discrimination using feature relevance analysis in motor imagery classification. Neurocomputing, 2015, 151(1): 122-129
[24]	Lemm S, Schafer C, Curio G. BCI competition 2003---data set III: probabilistic modeling of sensorimotor μ rhythms for classification of imaginary hand movements. IEEE Transactions on Biomedical Engineering, 2004, 51(6): 1077-1080
[25]	Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995
[26]	Huang N E. Review of empirical mode decomposition. In: Proceedings of the 2001 SPIE, Wavelet Applications VIII. Orlando, FL: SPIE, 2001. 71-80
[27]	Li Ming-Ai, Cui Yan, Yang Jin-Fu, Hao Dong-Mei. An adaptive multi-domain fusion feature extraction with method HHT and CSSD. Acta Electronica Sinica, 2013, 41(12): 2479-2486 (李明爱, 崔燕, 杨金福, 郝冬梅. 基于HHT和CSSD的多域融合自适应脑电特征提取方法. 电子学报, 2013, 41(12): 2479-2486)
[28]	Fu Yun-Fa, Xu Bao-Lei, Li Yong-Cheng, Li Hong-Yi, Wang Yue-Chao, Yu Zheng-Tao. Recognition of actual grip force movement modes based on movement-related cortical potentials. Acta Automatica Sinica, 2014, 40(6): 1045-1057 (伏云发, 徐保磊, 李永程, 李洪谊, 王越超, 余正涛. 基于运动相关皮层电位握力运动模式识别研究. 自动化学报, 2014, bf 40(6): 1045-1057)
[29]	Zhou S M, Gan J Q, Sepulveda F. Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface. Information Sciences, 2008, 178(6): 1629-1640
[30]	Chen M Y, Fang Y H, Zheng X F. Phase space reconstruction for improving the classification of single trial EEG. Biomedical Signal Processing and Control, 2014, 11: 10-16
[31]	Wang J, Xu G Z, Wang J, Yang S, Yan W L. Application of Hilbert-Huang transform for the study of motor imagery tasks. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver, BC: IEEE, 2008. 3848-3851
[32]	Decety J. The neurophysiological basis of motor imagery. Behavioural Brain Research, 1996, 77(1-2): 45-52
[33]	Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 2001, 89(7): 1123-1134
[34]	Pfurtscheller G, Lopes da Silva F H. Event-related EEG/ MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 1999, 110(11): 1842-1857
[35]	Neuper C, Wörtz M, Pfurtscheller G. ERD/ERS patterns reflecting sensorimotor activation and deactivation. Progress in Brain Research, 2006, 159: 211-222

施引文献

期刊类型引用(15)

1.	耿艳利，刘松岳，王希瑞，宣伯凯. 电动下肢假肢多路况控制策略研究. 中国康复医学杂志. 2024(02): 226-231 . 百度学术
2.	汪晓铭，黎林荣，陈长龙，孙洁，张哲文，孟巧玲，喻洪流. 人-机-环境共融的智能假肢膝关节研究进展. 中国生物医学工程学报. 2023(04): 486-501 . 百度学术
3.	韩亚丽，韩子，金壮壮，徐闽海，吴应达. 一种主动型踝关节助力外骨骼设计及性能实验. 仪器仪表学报. 2023(11): 109-118 . 百度学术
4.	刘克平，滕召纬，孙中波，李婉婷. 基于自适应模糊神经网络的下肢关节角度估计. 计算机仿真. 2022(09): 456-461 . 百度学术
5.	盛敏，唐少波. 融合统计和几何特征的运动意图识别方法. 安庆师范大学学报(自然科学版). 2021(01): 34-39 . 百度学术
6.	刘作军，许长寿，陈玲玲，张燕. 智能假肢膝关节的研发要点及其研究进展综述. 包装工程. 2021(10): 54-63 . 百度学术
7.	崔建伟，李志钢，曹尔凡，杜韩，陆普东. 基于人体上肢运动信息的助残手抓取意图识别方法. 中国惯性技术学报. 2021(02): 250-255 . 百度学术
8.	刘磊，杨鹏，刘作军，宋寅卯. 基于多核学习极限学习机的助行机器人运动相容性识别. 高技术通讯. 2021(09): 978-985 . 百度学术
9.	苏本跃，倪钰，盛敏，赵丽丽. 基于改进卷积神经网络的动力下肢假肢运动意图识别. 控制与决策. 2021(12): 3031-3038 . 百度学术
10.	黄品高，黄剑平，黄博俊，方鹏，刘志远，李光林. 实现下肢假肢智能仿生控制的神经功能重建及行走意图识别方法. 中国科学基金. 2021(S1): 227-235 . 百度学术
11.	滕召纬，孙中波，刘克平. 基于自适应模糊神经网络的下肢关节运动意图估计. 长春工业大学学报. 2020(06): 558-562 . 百度学术
12.	盛敏，刘双庆，王婕，苏本跃. 基于GMM-HMM模型的智能下肢假肢运动意图识别. 仪器仪表学报. 2019(05): 169-178 . 百度学术
13.	刘双庆. 基于LSTM深度学习模型的智能下肢假肢运动意图识别. 合肥学院学报(综合版). 2019(05): 96-104 . 百度学术
14.	杨帆，王远，郭鹏程. 针对下肢残障人士的无障碍卫浴产品设计分析. 工业设计. 2019(11): 48-49 . 百度学术
15.	刘磊，陈增强，杨鹏，刘作军. 采用鱼群算法进化极限学习机的假肢步态识别. 振动.测试与诊断. 2019(06): 1225-1231+1360-1361 . 百度学术