复合肢体想象动作脑-机接口中黎曼核支持向量机递归特征筛选方法

doi:10.3901/JME.2019.11.131

机械工程学报 ›› 2019, Vol. 55 ›› Issue (11): 131-137.doi: 10.3901/JME.2019.11.131

• 特邀专栏：共融机器人 • 上一篇下一篇

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复合肢体想象动作脑-机接口中黎曼核支持向量机递归特征筛选方法

陶学文, 奕伟波, 陈龙, 何峰, 綦宏志

天津大学精密仪器与光电子工程学院天津 300072

收稿日期:2018-09-17 修回日期:2018-11-26 出版日期:2019-06-05 发布日期:2019-06-05
通讯作者: 綦宏志(通信作者),男,1975年出生,博士,副教授。主要研究方向为生物医学信号处理、模式识别与脑-机接口。E-mail:qhz@tju.edu.cn
作者简介:陶学文,男,1995年出生。主要研究方向为脑-机接口与模式识别。E-mail:taoxuewen@tju.edu.cn;奕伟波,男,1987年出生,工程师。主要研究方向为脑-机接口、运动想象、脑力负荷等。E-mail:yiweibo1987@163.com;陈龙,男,1988年出生,博士研究生。主要研究方向为神经康复与脑-机接口。E-mail:cagor@tju.edu.cn;何峰,男,1971年出生,博士,副教授。主要研究方向为生物医学信号检测与处理、嵌入式医学仪器开发。E-mail:heaven@tju.edu.cn
基金资助:
国家自然科学基金资助项目（91648122）。

Riemann Kernel Support Vector Machine Recursive Feature Elimination in the Field of Compound Limb Motor Imagery BCI

TAO Xuewen, YI Weibo, CHEN Long, HE Feng, QI Hongzhi

School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072

Received:2018-09-17 Revised:2018-11-26 Online:2019-06-05 Published:2019-06-05

摘要/Abstract

摘要： 相对于传统的想象动作脑-机接口，复合肢体想象动作脑-机接口有效提升了指令复杂度，具有更好的中风后康复治疗潜力，但当前较低的识别精度限制了其临床应用。为提升复合肢体动作想象相关脑电信号特征的特异性并降低不同通道间的信息混淆，提出了一种基于脑电流形特征信息刻划的黎曼核支持向量机递归特征筛选方法（Riemann kernel support vector machine recursive feature elimination， RKSVM-RFE）。采集了10位被试在进行想象7种不同肢体部位动作时的脑电信号数据，利用RKSVM-RFE方法进行特征优化和建模，对脑电数据对应的运动意图进行识别。结果显示，基于所提方法的平均识别正确率达到了77%，相比于经典的CSP方法提高了近7%，并且能够消减近50%的脑电信息采集通道，可有效降低系统复杂性。研究结果为基于想象动作脑-机接口的康复技术发展提供了新的思路，值得进一步发展。

关键词: 流形, 脑-机接口, 特征筛选, 运动想象, 支持向量机

Abstract: Compound limb motor imagery brain-computer interface (CLMI-BCI) has better rehabilitative potential after stroke than traditional motor imagery brain-computer interface (MI-BCI), because of its high complexity of instructions. However, it's ability of using for clinical is limited due to the low recognition accuracy. To solve this problem, a new method named Riemann kernel support vector machine recursive feature elimination (RKSVM-RFE) is proposed based on the manifold information on electroencephalogram (EEG). The EEG data of 10 subjects are collected when they were imagining 7-class movements of different parts of the body. The data is modeled using RKSVM-RFE to recognize the motor intention corresponding to the EEG data. Results show that accuracy from our method is about 7% higher than the state-of-the-art method named CSP. And RKSVM-RFE can reduce complexity of system because it can decrease 50% EEG channels. The research provides a new idea about the development of rehabilitation technology based on MI-BCI, which is worthy of further development.

Key words: BCI, feature selection, manifold, motor imagery, SVM

中图分类号:

R318

陶学文, 奕伟波, 陈龙, 何峰, 綦宏志. 复合肢体想象动作脑-机接口中黎曼核支持向量机递归特征筛选方法[J]. 机械工程学报, 2019, 55(11): 131-137.

TAO Xuewen, YI Weibo, CHEN Long, HE Feng, QI Hongzhi. Riemann Kernel Support Vector Machine Recursive Feature Elimination in the Field of Compound Limb Motor Imagery BCI[J]. Journal of Mechanical Engineering, 2019, 55(11): 131-137.

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复合肢体想象动作脑-机接口中黎曼核支持向量机递归特征筛选方法

Riemann Kernel Support Vector Machine Recursive Feature Elimination in the Field of Compound Limb Motor Imagery BCI

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