在与运动相关的脑机接口（BCI）中,安置不必要的电极可能会引入伪迹,不利于特征提取和分类.为此,文中提出一种基于共空间模式（CSP）和二进制粒子群优化（BPSO）的电极选择方法.该方法在提取高区分度特征的同时限制电极数量,并依据CSP滤波器的权值调整初始电极组合的生成概率,以提高BPSO的收敛速度.实验结果表明：采用文中方法,选择少数电极即可获得令人满意的分类精度.

For motion-related brain-computer interfaces（BCIs）,some artifacts may be imported due to the setting of unnecessary electrodes,which is unfavorable to the feature extraction and classification.In order to solve this problem,an electrode selection method based on the common spatial pattern（CSP） and the binary particle swarm optimization（BPSO） is proposed,which extracts features with high discriminant ability and limits the number of electrodes at the same time.Moreover,in order to accelerate the convergence of BPSO,the generation probability of initial electrode combinations is adjusted according to the weights of CSP filters.Experimental results show that high classification accuracies can be achieved only with few electrodes.