Golub Matthew D, Chase Steven M, Batista Aaron P, Yu Byron M
Department of Electrical and Computer Engineering, Carnegie Mellon University, United States; Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States.
Department of Biomedical Engineering, Carnegie Mellon University, United States; Center for the Neural Basis of Cognition, Carnegie Mellon University & University of Pittsburgh, United States.
Curr Opin Neurobiol. 2016 Apr;37:53-58. doi: 10.1016/j.conb.2015.12.005. Epub 2016 Jan 19.
Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.
感觉运动控制涉及预测、学习和多感觉整合等认知过程。由于我们目前仅记录了相关神经元的一小部分，手臂具有非线性动力学，且多种感觉反馈模式有助于控制，因此了解手臂伸展过程中这些认知过程背后的神经机制具有挑战性。脑机接口（BCI）是一个定义明确的感觉运动回路，具有关键的简化优势，能够应对上述每一项挑战，同时涉及相似的认知过程。因此，BCI正被公认为感觉运动控制基础科学研究的有力工具。在此，我们描述了BCI在基础科学研究中的优势，并回顾了最近的BCI研究，这些研究揭示了感觉运动控制背后神经机制的新见解。
