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.

摘要 脑控假肢的发展有望为瘫痪患者提供独立性。 然而，到目前为止，脑机接口（BCI）还没有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）。这种无能可以追溯到 至少有三个缺点。首先，当我们自然地密切协调手臂和手的运动时， 当前的BCI用户按顺序到达和掌握，这在很大程度上是由于BCI解码器的构建方式。第二、 现有的解码器使用神经元活动的分量 以推断运动意图。虽然这种方法即使对于控制 虽然它具有拟人化的机器人手臂和手，但它并没有利用所有与行为相关的M1活动。的确， 与行为有直接和直接关系的活动--所谓的产出-- 仅占总M1活性的一小部分。剩下的神经元活动--所谓的零输出 活动-在产生输出有效活动中起作用，但被标准解码方法忽略。 第三，虽然机器人手已经变得越来越复杂和拟人化，但没有现有的原型 接近人手的功能，无论是在致动或传感方面。 拟议项目的目标是通过建立更多的 仿生解码器-允许协调手臂和手部运动，更有效地驾驭M1 活动-并在灵活而逼真的虚拟现实平台中挑战他们。首先，我们将建立解码 支持手臂和手的协调运动的方法。为此，我们将训练解码器， 受试者接触并抓住形状、大小和方向不同的物体，迫使明显的手定向 以及在到达过程中的预成形。其次，我们将进一步详细说明这些解码器，以便它们利用这两个 有效输出和无效输出活动。为此，我们将利用最近对M1动态及其 与行为的关系来构建解码器，利用M1中所有与行为相关的活动。最后我们将 通过让受试者执行手臂和手部功能以及任务的标准测试， 模拟日常生活的复杂活动，并为这些VR场景开发性能指标。我们 作为植入3名受试者的BCI多中心临床试验的一部分， 在两个地点，现有的资金用于另外两个主题。