基于可調(diào)Q因子小波變換的識別左右手運動想象腦電模式研究
doi: 10.11999/JEIT171191
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吉林大學通信工程學院 ??長春 ??130012
基金項目: 中央高?；究蒲袑ｍ椯Y金(451170301193)，吉林省科技發(fā)展計劃自然基金項目(20150101191JC)，吉林省產(chǎn)業(yè)技術(shù)研發(fā)項目(2016C025)
Research of Discrimination Between Left and Right Hand Motor Imagery EEG Patterns Based on Tunable Q-Factor Wavelet Transform
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College of Communication Engineering, Jilin University, Changchun 130012, China
Funds: The Fundamental Research Foundation for the Central Universities (451170301193), The Natural Science Foundation in the Science and Technology Development of Jilin Province (20150101191JC), The Industrial Technology Research and Development Project in Jilin Province (2016C025)
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摘要:
針對識別左右手運動想象腦電圖信號(EEG)模式精度和互信息不高的問題，該文采用基于可調(diào)Q因子小波變換(TQWT)算法來處理腦電信號。首先，利用TQWT對腦電圖信號進行分解；隨后，提取子頻帶信號的小波系數(shù)能量、自回歸模型(AR)系數(shù)以及分形維數(shù)；最后，利用線性判別分析(LDA)對提取的腦電特征進行識別。采用BCI2003和BCI2005競賽數(shù)據(jù)對所提出的算法進行驗證，4名受試者的最高識別率分別為88.11%, 89.33%, 77.13%和78.80%，最大互信息分別為0.95, 0.96, 0.43和0.45。實驗結(jié)果表明，所提算法取得了高分類精度及互信息值，驗證了其有效性。
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關(guān)鍵詞:
- 腦電圖 /
- 運動想象 /
- 可調(diào)Q因子小波變換 /
- 線性判別分析
Abstract:In view of the problem of low accuracy and mutual information in left and right hand motor imagery-based ElectroEncephaloGram (EEG), a new approach based on Tunable Q-factor Wavelet Transform (TQWT) is proposed to handle with the binary-class motor imagery EEGs. Firstly, the TQWT is utilized to decompose the filtered EEG signal. Then, several sub-band signals are extracted and followed by calculating their energy, AutoRegressive (AR) model coefficients and fractal dimension. Finally, a Linear Discriminant Analysis (LDA) classifier is used to classify these EEGs. Two Graz datasets of BCI Competition 2003 and 2005 are employed to verify the proposed method. The maximum accuracy of classifying EEGs of four subjects is 88.11%, 89.33%, 77.13% and 78.80%, respectively, and the maximum mutual information is 0.95, 0.96, 0.43 and 0.45. The high accuracies and mutual information demonstrate eventually the effectiveness of the proposed method.
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表 1 不同受試者采用單一特征和組合特征所得平均識別率及最高識別率
受試者 特征組合 平均識別率(%) 最高識別率(%) F1 81.74 86.44 F2 80.95 85.66 F3 67.93 73.38 S1 F1+F2 86.16 86.90 F1+F3 84.76 85.47 F2+F3 85.03 86.89 F1+F2+F3 86.45 88.11 F1 84.20 89.04 F2 76.52 81.06 F3 55.87 61.20 S2 F1+F2 87.85 89.30 F1+F3 87.63 88.59 F2+F3 80.22 81.33 F1+F2+F3 87.96 89.33 F1 66.08 71.46 F2 66.30 68.93 F3 55.16 58.92 S3 F1+F2 75.61 76.99 F1+F3 71.40 73.08 F2+F3 71.49 72.72 F1+F2+F3 74.70 77.13 F1 73.24 77.87 F2 69.10 74.60 F3 52.36 58.34 S4 F1+F2 77.65 78.79 F1+F3 76.14 77.24 F2+F3 74.06 75.25 F1+F2+F3 76.73 78.80 
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表 5 本文方法的時耗統(tǒng)計(s)
TQWT過程 能量特征 AR系數(shù)特征 分形維數(shù)特征 分類 總時間 S1 0.0010 0.0012 0.0016 0.0559 0.0174 0.0771 S2 0.0022 0.0010 0.0015 0.0536 0.0166 0.0749 S3 0.0012 0.0010 0.0016 0.0533 0.0163 0.0734 S4 0.0014 0.0015 0.0018 0.0547 0.0171 0.0765
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