为提高运动想象脑机接口识别准确率，结合共空间模式（common spatial pattern, CSP）和卷积神经网络（convolutional neural network, CNN）方法，提出一种改进滤波器组共空间模式（filter bank common spatial pattern, FBCSP）和CNN的算法，用于多分类运动想象脑电信号识别任务。信号预处理后，使用包含重叠频带的FBCSP计算空间投影矩阵，数据经过投影得到更有区分度的特征序列。然后将特征序列以二维排列方式输入搭建的CNN模型中进行分类。所提出方法在脑机接口竞赛数据集2a和Ⅲa上验证，并和其他文献方法对比。结果表明，本文方法一定程度上提高了运动想象脑电信号的分类准确率，为运动想象研究提供了一个有效办法。

In order to improve the recognition accuracy of motor imagery brain-computer interface, common spatial pattern(CSP) and convolutional neural network (CNN) methods were combined, and an improved filter bank common spatial pattern (FBCSP) and CNN algorithm was proposed for multi-classification motor imagery EEG signal recognition tasks. After the signal was preprocessed, the spatial projection matrix was calculated using the FBCSP containing the overlapping frequency band, and the data was projected to obtain a more discriminative feature sequence. Then the feature sequence was input into the constructed CNN model in a two-dimensional arrangement for classification. The proposed method was verified on the brain-computer interface competition datasets 2a and IIIa, and compared with other literature methods. The results show that the proposed method improves the classification accuracy of motor imagery EEG signals to a certain extent, and provides an effective method for motor imagery research.