Quantifying neural variability and learning during real world brain-computer interface use

量化现实世界脑机接口使用过程中的神经变异和学习

• 批准号: 10363903
• 负责人: Jennifer L. Collinger
• 金额: $ 61.35万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10363903
• 关键词: Automobile Driving; Biomimetics; Clinical; Cognitive; Complement; Computer software; Computers; Custom; Data; Development; Devices; Dimensions; Disabled Persons; Elements; Environment; Environmental Risk Factor; Fatigue; Goals; Home; Home environment; Intervention; Knowledge; Laboratories; Laboratory Study; Learning; Lighting; Limb structure; Longitudinal Studies; Measures; Monitor; Motion; Motor Cortex; Movement; Mus; Neuronal Plasticity; Noise; Output; Pain; Participant; Pattern; Performance; Physiological; Population; Positioning Attribute; Posture; Property; Quality of life; Self-Help Devices; Signal Transduction; Source; Speed; Stable Populations; Stress; System; Technology; Testing; Time; Training; Translating; Translations; base; brain computer interface; clinical translation; design; distraction; environmental change; experience; experimental study; grasp; home test; improved; motor learning; neural patterning; neuromechanism; neurotransmission; population based; portability; relating to nervous system; response; skills; visual tracking

The performance of intracortical brain-computer interfaces (BCIs) has advanced substantially over the last decade, but these devices are not yet robust enough for the home environment, where they can truly improve quality of life for individuals with disabilities. To date, BCIs have depended on experienced technicians to operate large and complicated systems comprised of multiple computers, signal processors, neural recording headstages, and custom software. Our laboratory has developed a portable, battery-powered intracortical BCI system that enables independent in-home computer access. However, to achieve the long-term goal of true clinical viability, BCIs must also offer reliable and robust functional performance in the less well-controlled home environment. We have achieved robust and generalizable control of a computer cursor using a biomimetic approach that combines reach-based velocity control of cursor position with grasp-based control of mouse click onset and offset. This transient-based neural decoder allows for generalized click function, adding the ability to ‘click-and-drag’ when accessing a computer (similar to carrying an object) to the ‘point-and-click’ functionality that is typically implemented in BCIs. Independent home use of the BCI system will provide an opportunity to collect neural data over long periods of time during unstructured and varied tasks, enabling quantification of context-dependent neural variability due to subject-state (e.g., fatigue, pain, or stress) as well as plasticity due to learning. Understanding how neural signals vary over time will be critical for clinical BCI systems that must be robust, generalizable, and autonomous (i.e., operate for extended periods without technician intervention). This project will first quantify the impact of subject-state on movement-related neural activity and performance during in-home BCI use. This understanding is critical to developing robust BCIs that eliminate the need for recalibration even in uncontrolled environments. The extent to which subject-state information is represented in motor cortex and overlaps with BCI control dimensions will inform development efforts to mitigate the impact of these nuisance variables. Participants will use the BCI for a variety of self-selected computer access tasks over periods of many months that will challenge decoder performance. This project will investigate motor learning mechanisms that may be engaged to facilitate improvements in performance that generalize to many different tasks. Experiments will test the hypothesis that stable population-level neural activity emerges to strengthen movement-related activity while minimizing non-task-related neural variability. Finally, participants will undergo targeted neural training to determine if motor learning can be accelerated and whether different mechanisms of neural reorganization are engaged in response to interventions that challenge the speed and accuracy properties of the decoder in different ways. This project will improve our understanding of neural plasticity mechanisms during extended BCI use in a real-world environment. Ultimately this knowledge will enable stable, high- functioning BCI performance during independent home-use, which is critical for clinical translation.

皮质内脑机接口（BCI）的性能在过去几年中有了很大的进步。 十年，但这些设备还不够强大的家庭环境，他们可以真正改善 残疾人的生活质量。到目前为止，BCI依赖于经验丰富的技术人员来操作 由多台计算机、信号处理器、神经记录器组成的大型复杂系统 云台和定制软件我们的实验室开发了一种便携式，电池供电的脑皮层内脑机接口 支持独立家庭计算机访问的系统。然而，要实现真正的长期目标， 为了确保临床可行性，BCI还必须在控制不佳的家庭中提供可靠和强大的功能性能 环境我们已经实现了强大的和通用的控制计算机光标使用仿生 一种结合了基于到达的光标位置速度控制和基于抓取的鼠标点击控制的方法 起始和偏移。这种基于瞬态的神经解码器允许广义点击功能，增加了 访问计算机时的“点击并拖动”（类似于携带对象）到“指向并点击”功能 这通常在BCI中实现。BCI系统的独立家庭使用将提供一个机会， 在非结构化和不同的任务中长时间收集神经数据，从而量化 由于受试者状态引起的上下文相关神经变异性（例如，疲劳、疼痛或压力）以及由于 学习。了解神经信号如何随时间变化对于临床BCI系统至关重要， 鲁棒的、可推广的和自主的（即，在没有技术人员干预的情况下长时间运行）。 本计画将首先量化主体状态对运动相关神经活动与表现的影响 在家庭BCI使用期间。这种理解对于开发强大的BCI至关重要， 即使在不受控制的环境中也能进行重新校准。受试者状态信息在多大程度上被表示在 运动皮层和与BCI控制维度的重叠将为开发工作提供信息， 这些讨厌的变量。参与者将使用BCI进行各种自选的计算机访问任务， 这将挑战解码器的性能。这个项目将研究运动学习 这些机制可以用于促进性能的改进，这些性能的改进概括为许多不同的 任务实验将检验这一假设，即稳定的群体水平的神经活动出现加强 运动相关的活动，同时最大限度地减少非任务相关的神经变异。最后，参与者将接受 有针对性的神经训练，以确定是否可以加速运动学习，以及是否可以通过不同的机制， 神经重组是为了响应挑战速度和准确性的干预措施， 解码器的不同方式。这个项目将提高我们对神经可塑性机制的理解 在现实世界环境中扩展BCI使用期间。最终，这些知识将使稳定，高- 在独立家庭使用期间，BCI的性能正常，这对临床翻译至关重要。