中文摘要：

为实现运动功能障碍患者的运动意愿和基于脑机接口技术的实际康复运动的一致性，进一步改善康复效果，以想象右手食指屈伸运动为例，对身体相同或相近部位的不同运动想象方式产生的脑电信号（记为EEGs）的特征提取方法进行研究。针对食指屈伸运动想象EEGs的事件相关去同步化现象（event．relateddesynchronization，ERD）不显著及发生的时间及频段的个体差异等特点，提出了基于小波包和熵准则的最优频段提取方法。该方法首先利用小波包分析对右手食指屈、伸运动想象EEGs进行分解；其次，利用熵准则对特征频段的可分度进行度量，从而选取相对明显的频段小波包组合，并以相应的小波包系数构成特征矢量；最后，结合支持向量机实现最优频段的选取。实验结果表明，该特征提取方法能够自适应提取右手食指屈伸运动想象EEGs的ERD现象差异性较大的频段特征，最高分类正确率为81．75％，验证了所提方法的有效性。

英文摘要：

The implementation of the consistency between motor intention and practical rehabilitation exercise based on brain-computer interface technology is necessary to improve the rehabilitation effect for people with dyskinesia. Taking the flexion and extension motor imagery of index finger as an example, the feature extraction method for the electroencephalogram produced by the same or similar body parts under different motor imagery tasks （labelled as EEGs） is studied in this paper. Aiming at the characteristics of EEGs, including its weak phenomenon of event-related desynchronization（ERD） and large individual differences of time and frequency bands where ERD appears, an optimal frequency band extraction method is proposed based on wavelet packet decomposition and entropy criterion. The EEGs of the flexion and extension motor imagery of index finger are decomposed with wavelet packet analysis firstly. Then, the separability values of the characteristic frequency bands are measured with entropy criterion. Furthermore, some clearer wavelet packets are selected to form a combination, and corresponding wavelet packet coeffi- cients are used to construct the feature vectors. Lastly, the optimal band is obtained with support vector machine. Experiment results show that the feature extraction method can choose the feature bands with large difference in ERD phenomenon of the EEGs, and the highest classification accuracy is 81.75% , which verifies the correctness and validity of the presented method.