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.

心脏内脑机构界面（BCI）的性能在最后一个方面已大大提高 十年，但是这些设备尚不适合家庭环境，它们可以真正改善 残疾人的生活质量。迄今为止，BCI依靠经验丰富的技术来运作 大型且复杂的系统完成了多台计算机，信号处理器，神经记录 头段和自定义软件。我们的实验室已经开发了一种便携式，电池供电的室内BCI 实现独立的家庭计算机访问的系统。但是，要实现真实的长期目标 临床生存能力，BCIS还必须在不太控制的房屋中提供可靠且可靠的功能性能 环境。我们已经使用仿生剂实现了对计算机光标的强大和可推广的控制 结合了光标位置的基于触及的速度控制与基于鼠标鼠标的控制的方法 发作和偏移。这种基于瞬态的神经解码器允许广义点击功能，从而增加了 访问计算机（类似于携带对象）到“点点击”功能时，“单击拖拉”功能 这通常在BCIS中实现。 BCI系统的独立家庭使用将为 在非结构化和多样化的任务期间，在长期内收集神经元数据，以量化 主体状态（例如疲劳，疼痛或压力）以及应有的可塑性引起的上下文依赖性神经变异性 学习。了解神经信号随着时间的变化如何对于必须是临床BCI系统至关重要 强大的，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）。 该项目将首先量化主题国家对运动相关的神经活动和性能的影响 在家庭BCI使用期间。这种理解对于开发强大的BCI至关重要，而BCI消除了需求 即使在不受控制的环境中进行重新校准。主题状态信息在多大程度上表示 运动皮层和与BCI控制维度的重叠将为发展努力提供帮助，以减轻 这些滋扰变量。参与者将使用BCI进行各种自选择的计算机访问任务 数月的时期将挑战解码器的性能。该项目将调查运动学习 可能涉及促进绩效改善的机制，这些机制推广到许多不同的机制 任务。实验将检验以下假设，即稳定的人口水平中性活性呈现为强度 与运动相关的活动，同时最大程度地减少与任务相关的神经变异性。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可以加速 神经重组是为了响应挑战速度和准确性属性的干预措施 解码器的不同方式。该项目将提高我们对神经可塑性机制的理解 在实际环境中使用BCI期间。最终，这些知识将使稳定，高 在独立家庭使用过程中发挥BCI性能，这对于临床翻译至关重要。