Groupe de Recherche sur le Système Nerveux Central, Département de Physiologie, Université de Montréal, C.P. 6128, Succursale Centre-ville, Montréal, Québec H3C 3J7, Canada.
Trends Neurosci. 2011 Feb;34(2):61-75. doi: 10.1016/j.tins.2010.11.003. Epub 2010 Dec 20.
Brain-computer interfaces (BCIs) extract signals from neural activity to control remote devices ranging from computer cursors to limb-like robots. They show great potential to help patients with severe motor deficits perform everyday tasks without the constant assistance of caregivers. Understanding the neural mechanisms by which subjects use BCI systems could lead to improved designs and provide unique insights into normal motor control and skill acquisition. However, reports vary considerably about how much training is required to use a BCI system, the degree to which performance improves with practice and the underlying neural mechanisms. This review examines these diverse findings, their potential relationship with motor learning during overt arm movements, and other outstanding questions concerning the volitional control of BCI systems.
脑机接口（BCI）从神经活动中提取信号，以控制从计算机光标到类似肢体的机器人等远程设备。它们有很大的潜力，可以帮助严重运动障碍的患者在没有护理人员持续帮助的情况下完成日常任务。了解受试者使用 BCI 系统的神经机制可以导致改进的设计，并为正常运动控制和技能获取提供独特的见解。然而，关于使用 BCI 系统需要多少培训、随着练习的进行性能提高的程度以及潜在的神经机制，报告差异很大。本综述考察了这些不同的发现，它们与明显手臂运动期间运动学习的潜在关系，以及与 BCI 系统的意愿控制有关的其他悬而未决的问题。
