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（BCIs）的性能比过去有了显着提高 十年了，但这些设备对于家庭环境来说还不够强大，在家庭环境中它们可以真正改善 残疾人的生活质量。迄今为止，脑机接口一直依赖经验丰富的技术人员来运营 由多台计算机、信号处理器、神经记录组成的大型复杂系统 探头和定制软件。我们的实验室开发了一种便携式、电池供电的皮质内脑机接口 允许独立的家庭计算机访问的系统。然而，要实现真正的长期目标 临床可行性，BCI 还必须在控制不太良好的家庭中提供可靠和强大的功能性能 环境。我们使用仿生技术实现了对计算机光标的鲁棒且通用的控制 将基于范围的光标位置速度控制与基于抓取的鼠标点击控制相结合的方法 起始点和偏移量。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”（类似于携带物体）至“点击”功能 这通常在 BCI 中实现。独立家庭使用 BCI 系统将提供一个机会 在非结构化和多样化的任务中长期收集神经数据，从而能够量化 由于受试者状态（例如疲劳、疼痛或压力）而导致的上下文依赖性神经变异性以及由于可塑性 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。 稳健、通用且自主（即无需技术人员干预即可长时间运行）。 该项目将首先量化主体状态对运动相关神经活动和表现的影响 在家庭 BCI 使用期间。这种理解对于开发强大的 BCI 至关重要，从而消除对 即使在不受控制的环境中也能重新校准。主体状态信息的表示程度 运动皮层以及与 BCI 控制维度的重叠将为减轻以下影响的开发工作提供信息 这些令人讨厌的变量。参与者将使用 BCI 执行各种自选的计算机访问任务 数月的时间将挑战解码器的性能。该项目将研究运动学习 可能涉及促进绩效改进的机制，这些机制可推广到许多不同的领域 任务。实验将检验这一假设：稳定的群体水平神经活动会加强 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，参与者将经历 有针对性的神经训练，以确定运动学习是否可以加速，以及是否有不同的机制 神经重组致力于响应挑战速度和准确性特性的干预措施 解码器的不同方式。该项目将增进我们对神经可塑性机制的理解 在现实环境中扩展 BCI 使用期间。最终，这些知识将使稳定、高 独立家庭使用期间正常的 BCI 性能，这对于临床转化至关重要。