中文摘要：

针对脑-机接口系统在训练样本较少的情况下,存在脑电（EEG）信号特征值稳定性低、特征向量区分度差等不足,提出一种脑电特征提取方法,即正则化共空域子空间分解法（R-CSSD）.该方法在传统共空域子空间分解（CSSD）算法的基础上引入正则化思想,通过正则化参数将目标实验者的训练数据与其他实验者（称为辅助实验者）的同类型训练数据进行有效结合,以构造正则化空间滤波器,完成对目标实验者运动想象EEG信号的特征提取,并进一步选用K近邻（KNN）算法实现脑电数据的分类.实验结果表明：在小训练样本情况下,R-CSSD方法有效提高了脑电信号特征值的稳定性,在提高分类正确率、降低时间消耗方面具有良好的性能.

英文摘要：

In brain-computer interface （BCI） systems with a small number of training samples, a method called as regularized common special subspace decomposition （R-CSSD） algorithm was proposed to solve the problems such as low stability of the eigenvalues and poor discriminative ability of eigenvectors in electroencephalography （EEG） recognition process： In R-CSSD, regularization was introduced based on the traditional common special subspace decomposition （CSSD） algorithm. The presented method was composed of three steps ： First, the training samples of the specific subject could be effectively combined with those of the other ancillary subjects by two regularization parameters; Second, a regularized special filter was built, and then the feature information of the specific subject＇ s EEG was extracted ; Finally, K- nearest neighbor （KNN） algorithm was used to identify motor imagery EEG. Under small-sample condition, the experimental results show that R-CSSD algorithm not only can effectively improve the stability of the eigenvalues of EEG, but also can produce high classification accuracy and less time consumption.