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
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1.
杭州電子科技大學(xué)自動(dòng)化學(xué)院 杭州 310018
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2.
浙江省腦機(jī)協(xié)同智能重點(diǎn)實(shí)驗(yàn)室 杭州 310018
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61871427, 61971168, U20B2074)
Correlation and Sparse Representation Based Channel Selection of Motor Imagery Electroencephalogram
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1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
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Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
Funds: The National Natural Science Foundation of China (61871427, 61971168, U20B2074)
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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。
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關(guān)鍵詞:
- 腦機(jī)接口 /
- 運(yùn)動(dòng)想象 /
- 共空間模式 /
- 支持向量機(jī) /
- 通道選擇
Abstract: In Motor Imagery (MI) based Brain Computer Interface (BCI), more channels of ElectroEncephaloGram (EEG) signal are usually adopted to improve the classification accuracy. But there will be channels containing irrelevant or redundant information about MI tasks, which degenerate the performance improvement of BCI. A Channel Selection method based on Correlation and Sparse Representation (CSR-CS) is proposed for EEG classification. Firstly, the Pearson correlation coefficient of each channel of the training sample is calculated to select the significant channels. Then the filter bank common spatial pattern features of the region where the significant channels are located are extracted and spliced into a dictionary. The number of non-zero sparse coefficients obtained from the dictionary is used to characterize the classification ability of each region, and the significant channels contained in the significant regions are selected as the optimal channels. Finally, the common spatial pattern and support vector machine are employed for feature extraction and classification respectively. In the classification experiments of two categories of MI task with BCI competition III dataset IVa and BCI competition IV dataset I, the average classification accuracy reaches 88.61% and 83.9%, which indicates the effectiveness and robustness of the proposed channel selection method. -
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-RCSP CSP-R-MF FCCR CSR-CS aa 82.50 81.43 78.57 86.31 al 96.80 92.41 98.21 97.74 av 71.10 70.00 72.45 72.83 aw 92.90 83.57 87.05 90.48 ay 93.90 85.00 93.25 95.71 均值 87.44 82.48 85.91 88.61 a 85.50 81.50 83.50 92.00 b 67.00 63.00 72.50 62.50 f 79.50 79.00 81.00 86.30 g 94.50 87.50 83.50 94.70 均值 81.60 77.80 80.10 83.90 p-value 0.21 <0.01 0.16 – 
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 aa al av aw ay 均值 a b f g 均值 p-value AC-CSP 76.19 95.12 66.02 83.69 94.88 83.18 82.50 52.50 85.10 92.30 78.10 <0.01 CSR-CS 86.31 97.74 72.83 90.48 95.71 88.61 92.00 62.50 86.30 94.70 83.90 –
下載: 導(dǎo)出CSV
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