Vilela Marco, Hochberg Leigh R
School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, United States.
School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, United States; Center for Neurorestoration and Neurotechnology, Veterans Affairs Medical Center, Providence, RI, United States; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Handb Clin Neurol. 2020;168:87-99. doi: 10.1016/B978-0-444-63934-9.00008-1.
Brain-computer interfaces (BCIs) have the potential to improve the quality of life of individuals with severe motor disabilities. BCIs capture the user's brain activity and translate it into commands for the control of an effector, such as a computer cursor, robotic limb, or functional electrical stimulation device. Full dexterous manipulation of robotic and prosthetic arms via a BCI system has been a challenge because of the inherent need to decode high dimensional and preferably real-time control commands from the user's neural activity. Nevertheless, such functionality is fundamental if BCI-controlled robotic or prosthetic limbs are to be used for daily activities. In this chapter, we review how this challenge has been addressed by BCI researchers and how new solutions may improve the BCI user experience with robotic effectors.
脑机接口(BCIs)有潜力改善严重运动障碍患者的生活质量。脑机接口捕捉用户的大脑活动,并将其转化为用于控制效应器的指令,如电脑光标、机器人肢体或功能性电刺激设备。由于需要从用户的神经活动中解码高维且最好是实时的控制指令,通过脑机接口系统对机器人手臂和假肢进行完全灵活的操控一直是一项挑战。然而,如果要将脑机接口控制的机器人或假肢用于日常活动,这种功能是至关重要的。在本章中,我们将回顾脑机接口研究人员是如何应对这一挑战的,以及新的解决方案如何改善脑机接口用户使用机器人效应器的体验。