Zhang Yin, Chase Steve M
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.
Biomedical Engineering Department and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.
Neural Comput. 2018 May;30(5):1323-1358. doi: 10.1162/NECO_a_01076. Epub 2018 Mar 22.
Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.
脑机接口正处于从实验室走向临床的过程中。这些设备通过读取神经活动并利用其直接控制设备，比如电脑屏幕上的光标。该领域一个悬而未决的问题是如何将神经活动映射到设备运动上，以实现最熟练的控制。由于学习，尤其是伴随数周练习的长期技能学习，能让受试者随着时间推移提高表现，这个问题变得更加复杂。解决这个问题的典型方法试图最大化设备的仿生特性，以限制广泛训练的需求。然而，一旦受试者进行了长时间练习并学会如何使用非仿生设备，这种方法最终是否会优于使用非仿生设备可能达到的表现尚不清楚。在这里，我们运用最优控制理论的观点来解决这个问题。在大脑充当最优控制器的假设下，我们给出了设备可用性的形式化定义，并表明最优的学习后映射可以写成一个约束优化问题的解。然后我们推导了大多数脑机接口常见特定情况下的最优映射。我们的结果表明，考虑到学习因素时，创建仿生接口的常见方法可能并非最优。更广泛地说，我们的方法为一般控制理论背景下的最优设备设计提供了一个蓝图。
