Departments of Electrical Engineering, Bioengineering & Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California 94305, USA.
Department of Electrical Engineering and Computer Sciences and Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, California 94704, USA.
Neuron. 2014 Nov 19;84(4):665-80. doi: 10.1016/j.neuron.2014.08.038.
Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system.
脑机接口 (BMI) 的目标是通过解码与运动相关的神经信号为导向计算机光标、假肢手臂和其他辅助设备的控制信号来帮助瘫痪的人。尽管有令人信服的实验室实验和正在进行的 FDA 试点临床试验，但系统性能、鲁棒性和泛化仍然是挑战。我们提供了一个视角，说明如何将两个互补的研究方向结合起来，这两个方向主要分别关注解码器设计和神经适应，以推进 BMI。这种 BMI 范式也应该为神经系统的功能和功能障碍提供新的科学见解。
