CRCNS:Dissecting brain-computer interfaces:a manifold & feedback-control approach

描述（由申请人提供）：脑机接口（BCI）可以通过将瘫痪者和截肢者的神经活动转化为脑机接口装置（如计算机光标或假肢）的运动来帮助他们。多年来，该领域一直在寻找能够最好地将神经活动映射到手臂运动的离线解码器。人们越来越认识到，设计一个有效的在线闭环解码器是一个相当不同的挑战。一个关键的区别是，在闭环设置中，受试者接收到关于脑机接口植物状态的感官反馈，并可以通过产生新的神经活动模式来补偿错误。为了设计临床可行的闭环脑机接口系统，必须回答有关其性能的神经基础的许多基本问题。受试者能产生任意的神经活动模式来补偿错误吗？受试者是否形成脑机接口工厂的内部模型，以便在有噪声、延迟反馈的情况下实现熟练控制？受试者是否利用从神经活动到BCI工厂运动学映射中固有的冗余来最大化控制精度？回答这些问题的一个关键障碍是缺乏一个适当的统计框架来逐步严格分析闭环BCI数据。我们建议开发这样一个受控制理论启发的框架，并与新颖的闭环脑机接口实验密切结合。我们将训练非人类灵长类动物灵巧地控制脑机接口游标，使用慢性多电极阵列记录初级运动皮层的神经活动。我们将测试脑机接口学习依赖于底层神经回路施加的约束的假设。同时，我们将开发和验证算法，通过考虑感官反馈、受试者的BCI游标内部模型和行为任务目标，来解释每个时间步观察到的高维神经活动。然后，我们将利用开发的算法来研究受试者是否可以在BCI控制期间利用神经冗余。更广泛的影响：我们设想了五个更广泛的影响领域。首先，脑机接口系统有望显著改善残疾患者的生活质量。临床试验正在进行中，因此存在将我们的研究直接或在短期内转化为临床实践的机会。其次，我们对手臂运动控制的神经基础的理解仍然不完整，很大程度上是因为系统太复杂了。脑机接口提供了一个简化的运动控制系统，其中神经活动和运动之间存在明确的关系。因此，脑机接口为研究运动控制和学习的神经机制提供了一个新的实验测试平台。第三，我们开发的统计框架可能适用于其他领域的反馈控制系统的研究。第四，随着大规模神经记录的出现，系统神经科学正在成为一个更加定量的领域。下一代的研究人员必须精通计算和生物学原理。我们相信我们的合作为我们的学生和博士后提供了一个良好的双重培养环境。第五，我们的研究发现可以直接应用到课堂教学中。Yu在CMU教授神经信号处理，Batista在Pitt教授神经科学控制理论；这两门课程都是每年一次的研究生课程。智力优势：在过去的十年中，几个小组已经展示了闭环脑机接口控制的令人信服的概念验证实验室演示。对于临床翻译，主要挑战之一是提高脑机接口系统的性能和稳健性。为了实现这一飞跃，我们认为严格研究现有系统至关重要，以了解i)为什么一些BCI解码器比其他解码器工作得更好，ii)我们可以在多大程度上依赖受试者的学习能力，以及iii)受试者为熟练控制而采用的神经策略。对于分析闭环脑机接口数据的一般统计框架，我们提出了一个早就需要的需求。通过开发的方法实现的发现将帮助我们和该领域的其他人设计高性能，临床可行的脑机接口系统，使受试者能够快速达到并保持高水平的熟练程度。

DESCRIPTION (provided by applicant): Brain-computer interfaces (BCI) can assist paralyzed individuals and amputees by translating their neural activity into movements of a BCI plant, such as a computer cursor or prosthetic limb. For many years, the field sought offline decoders that could best map neural activity to arm movements. It has become increasingly recognized that designing an effective online, closed-loop decoder is quite a different challenge. A key difference is that, in a closed-loop setting, the subject receives sensory feedback about the state of the BCI plant and can compensate for errors by generating new neural activity patterns. To engineer clinically-viable, closed-loop BCI systems, many fundamental questions about the neural underpinnings of their performance must be answered. Can subjects generate arbitrary neural activity patterns to compensate for errors? Do subjects form an internal model of the BCI plant to achieve proficient control in the presence of noisy, delayed feedback? Do subjects exploit the redundancy inherent in the mapping from neural activity to BCI plant kinematics to maximize control accuracy? A critical roadblock for answering these questions is the lack of an appropriate statistical framework to rigorously analyze closed-loop BCI data on a timestep-by-timestep basis. We propose to develop such a framework inspired by control theory, in close conjunction with novel closed-loop BCI experiments. We will train non-human primates to perform dextrous control of a BCI cursor using neural activity recorded in primary motor cortex with chronic, multi-electrode arrays. We will test the hypothesis that BCI learning depends on constraints imposed by the underlying neural circuitry. In parallel, we will develop and validate algorithms to explain the observed, high-dimensional neural activity at each timestep by accounting for the sensory feedback, subject's internal model of the BCI cursor, and behavioral task goals. We will then leverage the developed algorithms to investigate whether subjects can exploit neural redundancy during BCI control. Broader Impact: We envision five areas of broader impact. First, BCI systems promise to dramatically improve the quality of life for disabled patients. Clinical trials are ongoing, so opportunities exist to translate our research directly and in the near term into clinical practice. Second, our understanding of the neural basis for arm movement control is still incomplete, in large part because the system is so complex. BCIs provide a simplified motor control system, where a well-defined relationship exists between neural activity and movement. As such, BCIs provide a novel experimental testbed to investigate the neural mechanisms of motor control and learning. Third, the statistical framework we develop may be applicable to the study of feedback control systems in other domains. Fourth, with the advent of large-scale neural recordings, systems neuroscience is becoming a far more quantitative field. The next generation of researchers must be well-versed in computational and biological principles. We believe that our collaboration provides an excellent dual-training environment for our students and postdocs. Fifth, our research discoveries can directly feed into our classroom teaching. Yu teaches Neural Signal Processing at CMU and Batista teaches Control Theory in Neuroscience at Pitt; both are annual, graduate-level courses. Intellectual Merit: In the last decade, several groups have demonstrated compelling proof-of- concept laboratory demonstrations of closed-loop BCI control. For clinical translation, one of the major challenges is to improve the performance and robustness of BCI systems. To make this leap, we believe that it is critical to rigorously study existing systems to understand i) why some BCI decoders work better than others, ii) to what extent we can depend on the subjects' ability to learn, and iii) the neural strategies adopted by the subjects for proficient control. There is a long-overdue need for a general statistical framework for dissecting closed-loop BCI data, which we propose to develop. Discoveries enabled by the developed methods will help us and others in the field to design high-performance, clinically-viable BCI systems that allow the subject to quickly reach and maintain a high level of proficiency.