Improved Brain-Computer Interface Decoding for Activities of Daily Life

摘要 脑控假臂的开发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体的能力 以足够的可靠性和灵活性开展日常生活活动。这种无能是可以追踪到的 回到至少三个不足之处。首先，虽然我们自然而然地密切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。第二, 现有的解码器使用神经元活动的组件，该组件与 马达输出以推断马达意图。虽然这种方法即使对于控制 拟人化的机械臂和手，它并不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--所谓的产出力活动-- 只占总M1活动的一小部分。剩余的神经元活动-所谓的输出-无效 活动-在生成输出有效的活动中发挥作用，但被标准解码方法忽视。 第三，虽然机械手已经变得越来越复杂和拟人化，但没有现有的原型 接近人类手的功能，无论是在驱动方面还是在感觉方面。 拟议项目的目标是通过建立更多 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-并通过在灵活和逼真的虚拟现实平台中挑战他们。首先，我们将构建解码 支持手臂和手的协调运动的方法。为此，我们将培训解码员，同时 受试者触及并抓住形状、大小和方向不同的物体，迫使他们进行重要的手部定向 以及在到达过程中的预成型。其次，我们将进一步详细说明这些解码器，以便它们同时利用 输出有效和输出为零的活动。为此，我们将利用对M1动态的最新见解及其 与行为的关系，以建立解码器，利用M1中所有与行为相关的活动。最后，我们会 通过让受试者执行手臂和手功能以及任务的标准测试，在VR中测试新型解码器 它模拟复杂的日常生活活动，并为这些VR场景开发性能指标。我们是 作为BCI多点临床试验的一部分，植入了3名受试者，能够很好地实现这些目标 跨两个地点，现有资金用于另外两个科目。

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.