为实现脑卒中患者下肢自主康复训练，外骨骼技术逐渐与脑机接口（brain computer interface, BCI）相结合，但多分类运动想象（motor imagery, MI）脑电信号（electroencephalogram, EEG）一直存在特征提取困难和识别准确率低的问题。故提出了一种基于小波独立成分分析（wavelet independent component correlation algorithm, WICA）和共空间模式（common spatial patterns, CSP）的脑电信号多分类优化支持向量机算法（support vector machine, SVM）。该方法使用基于粒子群优化（particle swarm optimization, PSO）的支持向量机进行分类识别。研究结果表明，该方法平均分类准确率相比于其他方法有较大提高，证明了该算法可以有效提取脑电特征，并具有较好的运动想象脑电信号识别效果。同时，通过运动想象与外骨骼装置结合，验证了在线实时进行脑电控制的可行性。

In order to achieve autonomous lower limb rehabilitation training for stroke patients, exoskeletons are gradually combined with BCI(brain computer interface). Still, multi-class MI(motor imagery) EEG(electroencephalogram) has always had characteristics of difficulty in extraction and low recognition accuracy. A multi-classification optimized support vector machine algorithm for EEG signals based on WICA(wavelet independent component analysis) and CSP(common spatial patterns) was proposed. PSO(particle swarm optimization) was used to train a support vector machine for classification. It proves that the algorithm can effectively extract EEG features and has a better recognition effect of motor imagery EEG signals. The feasibility of online and real-time EEG control was verified by combining motor imagery and exoskeleton devices.