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

CRCNS：剖析脑机接口：流形

• 批准号: 8336883
• 负责人: Aaron Paul Batista
• 金额: $ 30.9万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-23 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8336883
• 关键词: Accounting; Adopted; Algorithms; Amputees; Area; Behavioral; Biological; Chronic; Clinical; Clinical Trials; Collaborations; Complex; Computers; Data; Disabled Persons; Educational process of instructing; Electrodes; Engineering; Environment; Feedback; Goals; Individual; Laboratories; Learning; Limb structure; Maps; Methods; Modeling; Motor Cortex; Movement; Neurosciences; Paralysed; Patients; Pattern; Performance; Plants; Postdoctoral Fellow; Prosthesis; Quality of life; Research; Research Personnel; Students; System; Testing; Training; Translating; Translations; Work; analytical method; arm; base; brain computer interface; clinical practice; computerized data processing; design; feeding; improved; kinematics; motor control; motor learning; neural circuit; neuromechanism; neuroregulation; next generation; nonhuman primate; novel; programs; relating to nervous system; research study; sensory feedback; stem; theories

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.

描述(申请人提供)：脑机接口(BCI)可以帮助瘫痪个人和截肢者将他们的神经活动转化为BCI植物的运动，如计算机光标或假肢。多年来，该领域一直在寻找能够最好地将神经活动与手臂动作相对应的离线解码器。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，受试者接收关于BCI工厂状态的感觉反馈，并可以通过产生新的神经活动模式来补偿错误。要设计临床可行的闭环式脑-机接口系统，必须回答许多关于其性能的神经基础的基本问题。受试者能产生任意的神经活动模式来补偿错误吗？受试者是否形成了BCI工厂的内部模型，以便在存在噪声、延迟反馈的情况下实现熟练的控制？受试者是否利用从神经活动到脑机接口植物运动学的映射所固有的冗余性来最大化控制精度？回答这些问题的一个关键障碍是缺乏适当的统计框架，以逐个时间步长的基础上严格分析闭环系统BCI数据。我们建议在控制理论的启发下，紧密结合新的闭环式脑-机接口实验来开发这样的框架。我们将训练非人类灵长类动物使用慢性多电极阵列在初级运动皮质记录的神经活动来灵活控制BCI光标。我们将测试这一假设，即BCI学习依赖于基础神经电路施加的约束。同时，我们将开发和验证算法，通过考虑感觉反馈、受试者的BCI光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调查受试者是否可以在脑-机接口控制过程中利用神经冗余。更广泛的影响：我们设想了五个更广泛的影响领域。首先，BCI系统承诺极大地提高残疾患者的生活质量。临床试验正在进行中，因此有机会直接将我们的研究在短期内转化为临床实践。其次，我们对手臂运动控制的神经基础的理解仍然不完整，这在很大程度上是因为这个系统太复杂了。BCI提供了一个简化的运动控制系统，在该系统中，神经活动和运动之间存在明确的关系。因此，BCI为研究运动控制和学习的神经机制提供了一个新的实验试验台。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量化的领域。下一代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训环境。第五，我们的研究发现可以直接反馈到我们的课堂教学中。Yu在CMU教授神经信号处理，Batista在PIT教授神经科学中的控制理论；这两门课程都是研究生级别的年度课程。智力优势：在过去的十年里，几个小组已经展示了闭环式脑-机接口控制的令人信服的概念验证实验室演示。对于临床翻译来说，提高脑机接口系统的性能和稳健性是一个主要的挑战。为了实现这一飞跃，我们认为，严格研究现有系统是至关重要的，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于受试者的学习能力，以及iii)受试者为熟练控制而采用的神经策略。我们早就需要一个通用的统计框架来分析闭环式脑-机接口数据，我们建议开发这一框架。由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BCI系统，使受试者能够快速达到并保持高水平的熟练程度。