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Bagging RCSP脑电特征提取算法

张毅 尹春林 蔡军 罗久飞

张毅, 尹春林, 蔡军, 罗久飞. Bagging RCSP脑电特征提取算法. 自动化学报, 2017, 43(11): 2044-2050. doi: 10.16383/j.aas.2017.c160094
引用本文: 张毅, 尹春林, 蔡军, 罗久飞. Bagging RCSP脑电特征提取算法. 自动化学报, 2017, 43(11): 2044-2050. doi: 10.16383/j.aas.2017.c160094
ZHANG Yi, YIN Chun-Lin, CAI Jun, LUO Jiu-Fei. Bagging RCSP Algorithm for Extracting EEG Feature. ACTA AUTOMATICA SINICA, 2017, 43(11): 2044-2050. doi: 10.16383/j.aas.2017.c160094
Citation: ZHANG Yi, YIN Chun-Lin, CAI Jun, LUO Jiu-Fei. Bagging RCSP Algorithm for Extracting EEG Feature. ACTA AUTOMATICA SINICA, 2017, 43(11): 2044-2050. doi: 10.16383/j.aas.2017.c160094

Bagging RCSP脑电特征提取算法

doi: 10.16383/j.aas.2017.c160094
基金项目: 

重庆市教委科学技术项目 KJ1600428

重庆市科学技术委员会项目 cstc2015jcyjBX0066

重庆市科学技术委员会项目 cstc2017jcyjAX0033

详细信息
    作者简介:

    张毅 重庆邮电大学先进制造工程学院教授.主要研究方向为机器人及应用, 脑电信号处理.E-mail:zhangyi@cqupt.edu.cn

    蔡军  重庆邮电大学自动化学院副教授.主要研究方向为模式识别, 智能控制.E-mail:caijun@cqupt.edu.cn

    罗久飞  重庆邮电大学先进制造工程学院讲师.主要研究方向为信号处理, 机械故障诊断与模式识别.E-mail:luojf@cqupt.edu.cn

    通讯作者:

    尹春林 重庆邮电大学自动化学院硕士研究生.主要研究方向为脑电信号处理.本文通信作者.E-mail:yinchunlin0210@foxmail.com

Bagging RCSP Algorithm for Extracting EEG Feature

Funds: 

Chong-qing Municipal Education Commission KJ1600428

Chongqing Science and Technology Commission Project cstc2015jcyjBX0066

Chongqing Science and Technology Commission Project cstc2017jcyjAX0033

More Information
    Author Bio:

    Professor at the School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications. His research interest covers robot and its applications, the signal processing of EEG

    Associate professor at the School of Automation, Chongqing University of Posts and Telecommunications. His research interest covers pattern recognition, intelligent control

    Lecturer at the School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications. His research interest covers signal processing, mechanical fault diagnosis, and pattern recognition

    Corresponding author: YIN Chun-Lin Master student at the School of Automation, Chongqing University of Posts and Telecommunications. His main research interest is signal processing of EEG. Corresponding author of this paper
  • 摘要: 正则化共空间模式(Regularized common spatial pattern,RCSP)解决了共空间模式(Common spatial pattern,CSP)对噪声敏感的问题,但它在小样本脑电数据集中的表现并不理想.针对上述问题,本文提出了Bagging RCSP(BRCSP)算法,通过Bagging方法重复选取训练数据来构造一个个包,并提取RCSP特征,再利用线性判别分析(Linear discriminant analysis,LDA)将特征向量映射到低维空间中,最后采用最近邻(Nearest neighborhood classifier,NNC)算法判定分类结果.线下实验证明,相比较聚合正则化共空间模式(RCSP with aggregation,RCSP-A),BRCSP的平均准确率提高了2.92%,且方差更小,鲁棒性更好.最后,在智能轮椅平台上,10位受试者利用BRCSP算法实现左右手运动想象脑电信号控制轮椅完成"8"字形路径的实验,证明了该算法在脑电信号特征提取中的有效性.
    1)  本文责任编委 赵新刚
  • 图  1  基于Bagging RCSP的左右手运动想象脑电信号处理过程

    Fig.  1  The process of EEG induced by the left hand and right hand motor imagery based on Bagging RCSP

    图  2  单个信号采集过程

    Fig.  2  The process of signal sampling

    图  3  左右手运动想象脑电信号的特征值分布

    Fig.  3  The distribution of eigenvalue of EEG induced by the left hand and right hand motor imagery

    图  4  BCI系统架构

    Fig.  4  BCI system architecture

    图  5  电极安放位置

    Fig.  5  Distribution of electrodes

    图  6  实验路径

    Fig.  6  The experimental route

    图  7  基于两种方法的BCI系统轨迹

    Fig.  7  The wheelchair track of two kinds of BCI system

    表  1  CSP、RCSP和Bagging RCSP在BCI Competition Ⅲ数据集IVa上的识别率比较

    Table  1  The recognition rate comparison of CSP, RCSP and Bagging RCSP on Competition Ⅲ data set IVa (%)

    算法 aa al av aw ay 平均 耗时(s)
    CSP 66.1 98.2 59.2 88.4 61.1 74.6 5.5
    LW-CSP 69.6 100.0 56.6 93.3 67.1 77.3 17.6
    SSCSP 73.2 96.4 54.8 70.5 73.4 73.5 6.7
    RCSP-A 76.8 98.2 74.5 92.9 77.0 83.9 62.2
    FERCSP 79.5 96.4 77.6 94.2 82.5 86.0 300.3
    BRCSP 79.3 98.6 78.3 92.9 82.5 86.3 63.3
    下载: 导出CSV

    表  2  RCSP-A和BRCSP算法下的离线识别率(%)

    Table  2  The recognition rate of off-line based on RCSP-A and BRCSP (%)

    受试者 RCSP-A BRCSP
    A1 80.53 86.16
    A2 95.81 93.04
    A3 75.56 81.08
    A4 84.74 87.56
    A5 78.06 83.61
    A6 87.78 90.56
    A7 84.72 87.50
    A8 93.83 96.06
    A9 85.42 87.56
    A10 76.29 78.78
    下载: 导出CSV

    表  3  RCSP-A和BRCSP算法的t-test结果

    Table  3  The result of t-test based on RCSP-A and BRCSP

    RCSP-A BRCSP
    均值 0.842740 0.871910
    标准差 0.0691799 0.0523412
    相关系数 0.955
    t -3.741
    df 9
    sig. (双侧) 0.005
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-01-27
  • 录用日期:  2017-02-13
  • 刊出日期:  2017-11-20

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