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.

大脑皮质内脑机接口(BCI)的性能在过去的几年中有了很大的进步 十年，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。到目前为止，BCI一直依赖经验丰富的技术人员进行操作 由多台计算机、信号处理器、神经记录组成的大型复杂系统 前台和定制软件。我们的实验室已经开发出一种便携式的、由电池供电的大脑皮质内脑机接口 支持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还必须在控制不太好的家庭中提供可靠和强大的功能性能 环境。我们已经使用仿生体实现了对计算机光标的健壮和可推广的控制 基于REACH的光标位置速度控制和基于GRASH的鼠标点击控制相结合的方法 开始和偏移量。这种基于瞬变的神经解码器允许通用点击功能，增加了以下能力 当访问计算机(类似于携带对象)时，可以使用“点击”功能进行“点击并拖动” 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 在非结构化和多种多样的任务中收集长时间的神经数据，使量化 由于受试者状态(例如，疲劳、疼痛或压力)以及可塑性导致的上下文相关的神经变异性 为学习干杯。了解神经信号如何随时间变化对临床脑-机接口系统至关重要，因为临床脑-机接口系统必须 健壮、可推广和自主(即，在没有技术人员干预的情况下长时间运行)。 该项目将首先量化受试者状态对运动相关神经活动和表现的影响 在家中使用BCI时。这一理解对于开发强大的BCI至关重要，这样就不需要 即使在不受控制的环境中也可以重新校准。中表示主体状态信息的程度 运动皮质和与BCI控制维度的重叠将为开发工作提供信息，以减轻 这些讨厌的变数。参与者将使用BCI执行各种自选的计算机访问任务 长达数月的时间，这将对解码器的性能构成挑战。这个项目将调查运动学习。 可用于促进性能改进的机制，这些机制可概括为许多不同的 任务。实验将检验这一假设，即稳定的种群水平的神经活动出现并加强 与运动相关的活动，同时最小化与任务无关的神经变异性。最后，参与者将接受 有针对性的神经训练，以确定运动学习是否可以加速，以及不同的机制是否 神经重组是为了对挑战速度和准确性特性的干预做出反应 以不同的方式对解码器进行识别。这个项目将提高我们对神经可塑性机制的理解。 在真实环境中扩展BCI使用期间。最终，这种知识将使稳定、高度- 在独立家庭使用期间发挥脑机接口功能，这对临床翻译至关重要。