中文摘要：

为解决偏瘫患者在主动康复训练中对上肢外骨骼的控制难题,提出一种基于单次运动想象的脑电信号分类方法,并将其应用于自主研发上肢外骨骼的实时控制中。针对脑电信号信噪比低、个体差异较大的问题,提出一种改进的共同空间模式（Common spatial pattern,CSP）特征提取算法,并结合支持向量机（Support vector machine,SVM）分类器,实现对单次运动想象脑电信号的分类;使用此分类方法对两种不同试验范式建立分类模型,并对其分类表现进行评估;将较好分类表现的分类模型应用于上肢外骨骼的实时控制中,验证方法的可行性。所有被试对上肢外骨骼控制的平均成功率达到87.12%±2.03%。试验结果表明,基于所提出的运动想象分类方法,可以实现上肢外骨骼的准确控制,并为面向康复训练的脑机接口技术提供了理论依据和实践基础。

英文摘要：

For solving the problem how the hemiplegic patients control the upper-limb exoskeleton during the active rehabilitation training, this study proposed an EEG classification method based on single-trial motor imagery. And this method in the real-time control of an upper-limb exoskeleton developed is applied. Aiming at the low noise-signal ratio and large individual differences of EEG, an advanced CSP algorithm for feature extraction is proposed. Combining this algorithm with SVM classifier, the single-trial motor imagery EEG is classified. Then, this method to construct classification models in two different paradigms is used, and evaluated the classification performance of two models. The classification model which had a better performance is applied in the real-time control of an upper-limb exoskeleton, to verify the feasibility of this method. The average accuracy is 87.12%±2.03% across all subjects in real-time control. The results demonstrate that the upper-limb exoskeleton can be controlled accurately based on the proposed method, and this study is provided the theory evidence and practical basis for BCI technology used in the rehabilitation training.