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基于相关性和稀疏表示的运动想象脑电通道选择方法

孟明 董芝超 高云园 孔万增

孟明, 董芝超, 高云园, 孔万增. 基于相关性和稀疏表示的运动想象脑电通道选择方法[J]. 电子与信息学报, 2022, 44(2): 477-485. doi: 10.11999/JEIT210778
引用本文: 孟明, 董芝超, 高云园, 孔万增. 基于相关性和稀疏表示的运动想象脑电通道选择方法[J]. 电子与信息学报, 2022, 44(2): 477-485. doi: 10.11999/JEIT210778
MENG Ming, DONG Zhichao, GAO Yunyuan, KONG Wanzeng. Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram[J]. Journal of Electronics & Information Technology, 2022, 44(2): 477-485. doi: 10.11999/JEIT210778
Citation: MENG Ming, DONG Zhichao, GAO Yunyuan, KONG Wanzeng. Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram[J]. Journal of Electronics & Information Technology, 2022, 44(2): 477-485. doi: 10.11999/JEIT210778

基于相关性和稀疏表示的运动想象脑电通道选择方法

doi: 10.11999/JEIT210778
基金项目: 国家自然科学基金(61871427, 61971168, U20B2074)
详细信息
    作者简介:

    孟明:男,1975年生,副教授,硕士生导师,研究方向为脑机接口、机器人智能控制

    董芝超:男,1997年生,硕士生,研究方向为模式识别与脑机接口

    高云园:女,1980年生,副教授,硕士生导师,研究方向为生物信号处理、脑机接口

    孔万增:男,1980年生,教授,博士生导师,研究方向为人工智能与模式识别、脑机交互与认知计算

    通讯作者:

    孟明 mnming@hdu.edu.cn

  • 中图分类号: TN911.7; TP391

Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram

Funds: The National Natural Science Foundation of China (61871427, 61971168, U20B2074)
  • 摘要: 在基于运动想象(MI)的脑机接口(BCI)中,通常采用较多通道的脑电信号(EEG)来提高分类精度,但其中会有包含与MI任务无关或冗余信息的通道,从而影响BCI的性能提升。该文针对运动想象脑电分类中的通道选择问题,提出一种采用相关性和稀疏表示对通道进行选择的方法(CSR-CS)。首先计算训练样本每个通道的皮尔逊相关系数来选择显著通道,然后提取显著通道所在区域的滤波器组共空间模式特征拼接成字典,利用由字典所得到的非零稀疏系数的个数表征每个区域的分类能力,选出显著区域所包含的显著通道作为最优通道,最后采用共空间模式和支持向量机分别进行特征提取与分类。在对BCI第3次竞赛数据集IVa和BCI第4次竞赛数据集I两个二分类MI任务的分类实验中,平均分类精度达到了88.61%和83.9%,表明所提通道选择方法的有效性和鲁棒性。
  • 图  1  CSR-CS方法框图

    图  2  稀疏表示方法

    图  3  单次实验时间轴

    图  4  通道区域划分

    图  5  数据集Ⅱ电极分布

    图  6  选择通道区域个数对分类精度的影响

    图  7  受试者aa在CSR-CS和AC-CSP方法上获得的最显著的两个特征的分布

    图  8  选择显著通道或区域与否对分类精度的影响

    表  1  数据集Ⅰ、数据集Ⅱ分类精度比较

    受试者方法
    CCS-RCSPCSP-R-MFFCCRCSR-CS
    aa82.5081.4378.5786.31
    al96.8092.4198.2197.74
    av71.1070.0072.4572.83
    aw92.9083.5787.0590.48
    ay93.9085.0093.2595.71
    均值87.4482.4885.9188.61
    a85.5081.5083.5092.00
    b67.0063.0072.5062.50
    f79.5079.0081.0086.30
    g94.5087.5083.5094.70
    均值81.6077.8080.1083.90
    p-value0.21<0.010.16
    下载: 导出CSV

    表  2  通道选择与否对分类准确率的影响

    方法数据集Ⅰ数据集Ⅱ
    aaalavaway均值abfg均值p-value
    AC-CSP76.1995.1266.0283.6994.8883.1882.5052.5085.1092.3078.10<0.01
    CSR-CS86.3197.7472.8390.4895.7188.6192.0062.5086.3094.7083.90
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-04
  • 修回日期:  2021-12-09
  • 录用日期:  2021-12-13
  • 网络出版日期:  2021-12-25
  • 刊出日期:  2022-02-25

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