脑中风后运动障碍的康复很重要，与传统康复方法不同，基于运动想象的脑机交互是一种新兴的可用于运动障碍康复的技术。然而，传统的单侧肢体简单的运动想象具有局限性，不易为患者理解并且康复效果有限。本项目针对下肢步态运动协调障碍，提出一种新的基于运动协调想象的脑机交互范式，可望用于该类运动障碍的康复。目前，运动协调想象的脑机交互机制尚不清楚。大量传统的研究量化运动想象活动的方法是基于探测感觉运动节律（α和β频带）的调制，本项目基于时间分辨率高的EEG，拟采用运动相关皮层电位（MRCPs）探查运动协调想象的神经加工过程，并计算基于ＥＥＧ的动态脑网络；进而基于空间分辨率高的fMRI探查运动协调想象的脑区激活和脑功能网络。最后，基于上述探查结果，构建基于运动协调想象的脑机交互驱动下肢运动协调康复机器人的原型系统，通过最优化的神经反馈诱导神经可塑性。本项目的实施可望为该类脑机交互用于运动康复打下坚实的基础。

Rehabilitation for Movement disorders after stroke is very important. Being different from the traditional rehabilitation methods, Motor imagery based brain–computer interaction is an emerging technology for rehabilitating motor deficits. However, the traditional simple motor imagery involving unilateral limb has limitations because of its not being easily understood by patients and limited rehabilitation effect. The study aims at gait movement coordination disorder of lower limb and a new paradigm based on motor coordination imagery for brain-computer interaction is proposed and expected to be used in the rehabilitation of movement disorders. At present, the mechanism of brain-computer interaction based on motor coordination imagery remains unknown. A large number of traditional methods being used for quantization of brain activity related to motor imagery are to detect the modulation of sensorimotor rhythm (alpha and beta band). The neural processing of motor coordination imagery by MRCPs is explored and the dynamic brain network based on EEG is calculated by EEG having a high time resolution. Brain activation and brain functional network of motor coordination imagery are explored by fMRI having a high spatial resolution. Finally, with the results, a prototype system of rehabilitation robot for lower limbs motion coordination actuated by brain-computer interaction based on motor coordination imagery and the neural plasticity is induced by the optimization of the neural feedback. This project will lay a solid foundation for movement rehabilitation based on this BCI.