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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

孙会文, 伏云发, 熊馨, 杨俊, 刘传伟, 余正涛. 基于HHT运动想象脑电模式识别研究. 自动化学报, 2015, 41(9): 1686-1692. doi: 10.16383/j.aas.2015.c150007
引用本文: 孙会文, 伏云发, 熊馨, 杨俊, 刘传伟, 余正涛. 基于HHT运动想象脑电模式识别研究. 自动化学报, 2015, 41(9): 1686-1692. doi: 10.16383/j.aas.2015.c150007
SUN Hui-Wen, FU Yun-Fa, XIONG Xin, YANG Jun, LIU Chuan-Wei, YU Zheng-Tao. Identification of EEG Induced by Motor Imagery Based on Hilbert-Huang Transform. ACTA AUTOMATICA SINICA, 2015, 41(9): 1686-1692. doi: 10.16383/j.aas.2015.c150007
Citation: SUN Hui-Wen, FU Yun-Fa, XIONG Xin, YANG Jun, LIU Chuan-Wei, YU Zheng-Tao. Identification of EEG Induced by Motor Imagery Based on Hilbert-Huang Transform. ACTA AUTOMATICA SINICA, 2015, 41(9): 1686-1692. 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
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

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

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: 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.
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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

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