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基于相關(guān)性和稀疏表示的運(yùn)動(dòng)想象腦電通道選擇方法

孟明 董芝超 高云園 孔萬增

孟明, 董芝超, 高云園, 孔萬增. 基于相關(guān)性和稀疏表示的運(yùn)動(dòng)想象腦電通道選擇方法[J]. 電子與信息學(xué)報(bào), 2022, 44(2): 477-485. doi: 10.11999/JEIT210778
引用本文: 孟明, 董芝超, 高云園, 孔萬增. 基于相關(guān)性和稀疏表示的運(yùn)動(dòng)想象腦電通道選擇方法[J]. 電子與信息學(xué)報(bào), 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

基于相關(guān)性和稀疏表示的運(yùn)動(dòng)想象腦電通道選擇方法

doi: 10.11999/JEIT210778
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61871427, 61971168, U20B2074)
詳細(xì)信息
    作者簡(jiǎn)介:

    孟明:男,1975年生,副教授,碩士生導(dǎo)師,研究方向?yàn)槟X機(jī)接口、機(jī)器人智能控制

    董芝超:男,1997年生,碩士生,研究方向?yàn)槟J阶R(shí)別與腦機(jī)接口

    高云園:女,1980年生,副教授,碩士生導(dǎo)師,研究方向?yàn)樯镄盘?hào)處理、腦機(jī)接口

    孔萬增:男,1980年生,教授,博士生導(dǎo)師,研究方向?yàn)槿斯ぶ悄芘c模式識(shí)別、腦機(jī)交互與認(rèn)知計(jì)算

    通訊作者:

    孟明 mnming@hdu.edu.cn

  • 中圖分類號(hào): 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: 在基于運(yùn)動(dòng)想象(MI)的腦機(jī)接口(BCI)中,通常采用較多通道的腦電信號(hào)(EEG)來提高分類精度,但其中會(huì)有包含與MI任務(wù)無關(guān)或冗余信息的通道,從而影響B(tài)CI的性能提升。該文針對(duì)運(yùn)動(dòng)想象腦電分類中的通道選擇問題,提出一種采用相關(guān)性和稀疏表示對(duì)通道進(jìn)行選擇的方法(CSR-CS)。首先計(jì)算訓(xùn)練樣本每個(gè)通道的皮爾遜相關(guān)系數(shù)來選擇顯著通道,然后提取顯著通道所在區(qū)域的濾波器組共空間模式特征拼接成字典,利用由字典所得到的非零稀疏系數(shù)的個(gè)數(shù)表征每個(gè)區(qū)域的分類能力,選出顯著區(qū)域所包含的顯著通道作為最優(yōu)通道,最后采用共空間模式和支持向量機(jī)分別進(jìn)行特征提取與分類。在對(duì)BCI第3次競(jìng)賽數(shù)據(jù)集IVa和BCI第4次競(jìng)賽數(shù)據(jù)集I兩個(gè)二分類MI任務(wù)的分類實(shí)驗(yàn)中,平均分類精度達(dá)到了88.61%和83.9%,表明所提通道選擇方法的有效性和魯棒性。
  • 圖  1  CSR-CS方法框圖

    圖  2  稀疏表示方法

    圖  3  單次實(shí)驗(yàn)時(shí)間軸

    圖  4  通道區(qū)域劃分

    圖  5  數(shù)據(jù)集Ⅱ電極分布

    圖  6  選擇通道區(qū)域個(gè)數(shù)對(duì)分類精度的影響

    圖  7  受試者aa在CSR-CS和AC-CSP方法上獲得的最顯著的兩個(gè)特征的分布

    圖  8  選擇顯著通道或區(qū)域與否對(duì)分類精度的影響

    表  1  數(shù)據(jù)集Ⅰ、數(shù)據(jù)集Ⅱ分類精度比較

    受試者方法
    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
    下載: 導(dǎo)出CSV

    表  2  通道選擇與否對(duì)分類準(zhǔn)確率的影響

    方法數(shù)據(jù)集Ⅰ數(shù)據(jù)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
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2021-08-04
  • 修回日期:  2021-12-09
  • 錄用日期:  2021-12-13
  • 網(wǎng)絡(luò)出版日期:  2021-12-25
  • 刊出日期:  2022-02-25

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