基于可调Q因子小波变换的识别左右手运动想象脑电模式研究

针对识别左右手运动想象脑电图信号(EEG)模式精度和互信息不高的问题，该文采用基于可调Q因子小波变换(TQWT)算法来处理脑电信号。首先，利用TQWT对脑电图信号进行分解；随后，提取子频带信号的小波系数能量、自回归模型(AR)系数以及分形维数；最后，利用线性判别分析(LDA)对提取的脑电特征进行识别。采用BCI2003和BCI2005竞赛数据对所提出的算法进行验证，4名受试者的最高识别率分别为88.11%, 89.33%, 77.13%和78.80%，最大互信息分别为0.95, 0.96, 0.43和0.45。实验结果表明，所提算法取得了高分类精度及互信息值，验证了其有效性。

Research of Discrimination Between Left and Right Hand Motor Imagery EEG Patterns Based on Tunable Q-Factor Wavelet Transform

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.