Teo Wei-Peng, Chew Effie
School of Medical and Applied Sciences, Central Queensland University, Bruce Highway, Rockhampton, Queensland, 4702, Australia(∗).
Division of Neurology and Yong Loo Lin School of Medicine, National University Health Systems, Singapore(†).
PM R. 2014 Aug;6(8):723-8. doi: 10.1016/j.pmrj.2014.01.006. Epub 2014 Jan 12.
In the past 3 decades, interest has increased in brain-computer interface (BCI) technology as a tool for assisting, augmenting, and rehabilitating sensorimotor functions in clinical populations. Initially designed as an assistive device for partial or total body impairments, BCI systems have since been explored as a possible adjuvant therapy in the rehabilitation of patients who have had a stroke. In particular, BCI systems incorporating a robotic manipulanda to passively manipulate affected limbs have been studied. These systems can use a range of invasive (ie, intracranial implanted electrodes) or noninvasive neurophysiologic recording techniques (ie, electroencephalography [EEG], near-infrared spectroscopy, and magnetoencephalography) to establish communication links between the brain and the BCI system. Trials are most commonly performed on EEG-based BCI in comparison with the other techniques because of its high temporal resolution, relatively low setup costs, portability, and noninvasive nature. EEG-based BCI detects event-related desynchronization/synchronization in sensorimotor oscillatory rhythms associated with motor imagery (MI), which in turn drives the BCI. Previous evidence suggests that the process of MI preferentially activates sensorimotor regions similar to actual task performance and that repeated practice of MI can induce plasticity changes in the brain. It is therefore postulated that the combination of MI and BCI may augment rehabilitation gains in patients who have had a stroke by activating corticomotor networks via MI and providing sensory feedback from the affected limb using end-effector robots. In this review we examine the current literature surrounding the feasibility of EEG-based MI-BCI systems in stroke rehabilitation. We also discuss the limitations of using EEG-based MI-BCI in patients who have had a stroke and suggest possible solutions to overcome these limitations.
在过去30年里，脑机接口（BCI）技术作为一种辅助、增强和恢复临床人群感觉运动功能的工具，受到了越来越多的关注。BCI系统最初被设计为一种用于部分或全身损伤的辅助设备，此后人们开始探索将其作为中风患者康复治疗的一种可能的辅助疗法。特别是，已经对结合机器人操作器以被动操纵受影响肢体的BCI系统进行了研究。这些系统可以使用一系列侵入性（即颅内植入电极）或非侵入性神经生理学记录技术（即脑电图[EEG]、近红外光谱和脑磁图）来建立大脑与BCI系统之间的通信联系。与其他技术相比，试验最常基于脑电图的BCI进行，因为它具有高时间分辨率、相对较低的设置成本、便携性和非侵入性。基于脑电图的BCI检测与运动想象（MI）相关的感觉运动振荡节律中的事件相关去同步/同步，进而驱动BCI。先前的证据表明，运动想象过程优先激活与实际任务表现相似的感觉运动区域，并且重复进行运动想象可以诱导大脑的可塑性变化。因此，据推测，运动想象和BCI的结合可能通过运动想象激活皮质运动网络，并使用末端执行器机器人从受影响肢体提供感觉反馈，从而增强中风患者的康复效果。在这篇综述中，我们研究了围绕基于脑电图的运动想象 - BCI系统在中风康复中可行性的当前文献。我们还讨论了在中风患者中使用基于脑电图的运动想象 - BCI的局限性，并提出了克服这些局限性的可能解决方案。
