Improved Brain-Computer Interface Decoding for Activities of Daily Life

改进日常生活活动的脑机接口解码

• 批准号: 10744925
• 负责人: John E Downey
• 金额: $ 69.04万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10744925
• 关键词: Action Research; Activities of Daily Living; Address; Algorithms; Artificial Arm; Back; Behavior; Behavioral; Biomimetics; Brain; Clinical Trials; Complex; Data; Development; Digit structure; Distal; Eating; Feedback; Funding; Generations; Goals; Hand; Hand functions; Home; Human; Impairment; Implant; Individual; Learning; Life; Limb structure; Liquid substance; Location; Mechanics; Modeling; Monkeys; Motion; Motor; Motor Cortex; Motor output; Movement; Multi-Institutional Clinical Trial; Neurologic; Neurons; Output; Paralysed; Participant; Pattern; Performance; Persons; Physics; Population; Positioning Attribute; Procedures; Prosthesis; Robotics; Role; Shapes; Signal Transduction; Site; Somatosensory Cortex; Source; Structure; Tactile; Testing; Time; Training; Upper Extremity; User-Computer Interface; arm; arm function; arm movement; brain computer interface; cooking; design; experimental study; falls; flexibility; grasp; improved; insight; kinematics; microstimulation; motor control; neural; neuron component; next generation; novel; performance tests; prototype; sensor; virtual; virtual reality; virtual reality environment; virtual reality simulation

ABSTRACT The development of brain-controlled prosthetic arms promises to provide independence to people with paralysis. To date, however, Brain-Computer Interfaces (BCIs) have not conferred on users the ability to use the prosthesis to carry out activities of daily living (ADLs) with adequate reliability and flexibility. This inability can be traced back to at least three shortcomings. First, while we naturally closely coordinate arm and hand movements, current BCI users reach and grasp sequentially, in large part due to the way BCI decoders are built. Second, existing decoders use the component of the neuronal activity that has a direct and immediate relationship with motor output to infer motor intent. While this approach has been successful even for control of an anthropomorphic robotic arm and hand, it does not harness all the behaviorally relevant M1 activity. Indeed, activity that has a direct and immediate relationship with behavior – the so-called output-potent activity – constitutes only a small fraction of the total M1 activity. The remaining neuronal activity – so-called output-null activity – plays a role in generating the output-potent activity but is overlooked by standard decoding approaches. Third, while robotic hands have become increasingly sophisticated and anthropomorphic, no existing prototype approaches the functionality of a human hand, either in terms of actuation or sensorization. The goal of the proposed project is to address each of the aforementioned limitations by building more biomimetic decoders – that allow for coordinated arm and hand movements and more effectively harness M1 activity – and by challenging them in a flexible and realistic virtual reality platform. First, we will build decoding approaches that support coordinated movements of the arm and hand. To this end, we will train decoders while subjects reach to and grasp objects that differ in shape, size, and orientation, forcing significant hand orienting and pre-shaping during reaching. Second, we will further elaborate these decoders so that they leverage both output-potent and output-null activity. To this end, we will leverage recent insights into M1 dynamics and their relationship to behavior to build decoders that harness all the behaviorally relevant activity in M1. Finally, we will test novel decoders in VR by having subjects perform standard tests of arm and hand function as well as tasks that mimic complex activities of daily living and develop performance metrics for these VR scenarios. We are well positioned to achieve these objectives as part of a multi-site clinical trial on BCI with 3 subjects implanted across two locations, with existing funding for two more subjects.

抽象的 大脑控制假肢的开发有望为瘫痪患者提供独立性。 然而，迄今为止，脑机接口（BCI）尚未赋予用户使用假肢的能力 以足够的可靠性和灵活性进行日常生活活动（ADL） 这种能力是可以追踪的。 回到至少三个缺点，虽然我们自然地紧密协调手臂和手的动作， 目前的 BCI 用户是按顺序接触和掌握的，这在很大程度上是由于 BCI 解码器的构建方式所致。 现有的解码器使用与以下内容有直接和直接关系的神经活动组成部分： 虽然这种方法即使对于控制也很成功。 的确， 与行为有直接和直接关系的活动——所谓的产出有效活动—— 仅占 M1 总活动的一小部分，即所谓的输出空。 活动——在产生有效输出活动中发挥作用，但被标准解码方法忽视。 第三，虽然机械手变得越来越复杂和拟人化，但还没有现有的原型 在驱动或传感方面接近人手的功能。 拟议项目的目标是通过构建更多的解决方案来解决提到的每个限制 仿生解码器 – 允许协调手臂和手的运动并更有效地利用 M1 活动 - 通过在灵活且逼真的虚拟现实平台中挑战它们，首先，我们将构建解码。 为此，我们将在训练解码器的同时支持手臂和手的协调运动。 受试者伸手抓住形状、大小和方向不同的物体，迫使手进行显着的定向 其次，我们将进一步详细说明这些解码器，以便它们充分利用两者。 为此，我们将利用对 M1 动态及其的最新见解。 与行为的关系来构建利用 M1 中所有行为相关活动的解码器。 通过让受试者执行手臂和手功能以及任务的标准测试来测试 VR 中的新型解码器 模拟日常生活中的复杂活动并为这些场景开发性能指标。 作为植入 3 名受试者的 BCI 多中心临床试验的一部分，我们有能力实现这些目标 跨越两个地点，现有资金用于另外两个科目。