Enhancement and optimization of a mobile iBCI for Veterans with paralysis

为瘫痪退伍军人增强和优化移动 iBCI

• 批准号: 10538008
• 负责人: John David Simeral
• 金额: --
• 依托单位: PROVIDENCE VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10538008
• 关键词: 3-Dimensional; Address; Adoption; Algorithms; Amyotrophic Lateral Sclerosis; Artificial Arm; Calibration; Classification; Clinical; Communication; Computer software; Computers; Data; Data Science; Development; Devices; Dimensions; Disabled Persons; Electric Stimulation; Evaluation; Exhibits; Fingers; Gestures; Goals; Hand; Home; Human; Imagery; Implanted Electrodes; Individual; Internet; Intuition; Limb structure; Link; Machine Learning; Methods; Motor; Motor Cortex; Movement; Mus; Muscle; Neurofibrillary Tangles; Non-linear Models; Outcome; Output; Paralysed; Participant; Pattern; Performance; Persons; Precentral gyrus; Process; Quadriplegia; Reporting; Research; Running; Sampling; Self-Help Devices; Signal Transduction; Speech; Speed; Spinal cord injury; Stream; Stroke; Supervision; Tablets; Techniques; Technology; Text; Time; Touch sensation; Translating; Upper Extremity; User-Computer Interface; Veterans; Wheelchairs; arm; assistive robot; autoencoder; brain computer interface; deep learning; deep neural network; disability; feature extraction; finger movement; handheld mobile device; high dimensionality; improved; innovation; interest; kinematics; long short term memory; markov model; mobile application; neurotransmission; phrases; preclinical study; prototype; recurrent neural network; recursive neural network; relating to nervous system; simulation; theories; two-dimensional; virtual; wireless

Intracortical brain-computer interfaces (iBCIs) record and process neural signals streaming from arrays of electrodes implanted in the cortex to enable fast, accurate and intuitive control of assistive technologies for individuals living with paralysis arising from spinal cord injury, stroke, or amyotrophic lateral sclerosis (ALS). Using an intracortical BCI, people with tetraplegia have been able to use their imagined hand movements to command point-and-click actions on a computer, type with a virtual keyboard, use communication apps such as chat, and browse the web. Imagined movements have also been used to control assistive devices including the DEKA prosthetic arm, assistive robotic arms and even one’s own paralyzed limb through patterned electrical stimulation of paralyzed muscles. Recent development of a miniature wireless signal transmitter and a wireless, compact, battery-operated neural signal processor has raised the potential for individuals with severe motor disability to use a wheelchair-mounted iBCI independently at home without technical assistance. To be a viable assistive technology, the iBCI must be not only mobile but also high-performance, reliable, and intuitive to use. This research enhances all of these aspects of a mobile iBCI by translating algorithmic innovations demonstrated in varied pre-clinical studies and optimizing them toward stable, high-performance decoding in a mobile iBCI. This research first transforms a highly accurate and responsive kinematic neural decoder (a deep learning recursive neural network) to run on the mobile iBCI’s computationally powerful embedded hardware. To help stabilize kinematic decoding over time, enhance performance, and ease calibration requirements, this research then looks to theories of intrinsic neural manifolds to adapt dimensionality reduction (DR) techniques to high- dimensional, multiscale human neural data. Next, state-of-the-art data science approaches are integrated with multiclass analyses to promote reliable, accurate classification of a large set of discrete hand gestures imagined by iBCI users. Next, DR methods are evaluated to disentangle simultaneous kinematic and gesture decoding for smoother, more accurate and unperturbed iBCI control. These cumulative approaches will be translated to embedded hardware form to run on the powerful mobile processor to provide on-demand control of mobile and touch-enabled devices using both mouse-like movements and gestures (such as swipe-to-scroll and pinch-to zoom). Mapping unique gestures to additional functions will instantly activate key shortcuts or gesture-to-phrase output. Using this wheelchair-mounted iBCI, a speech-disabled individual could imagine a hand gesture to generate a text-to-speech greeting or call for help. Overall, this research leverages state-of-the-art machine learning innovations toward a more capable, reliable, and versatile iBCI to promote independence for people with severe motor disability.

皮质内脑机接口 (iBCIs) 记录和处理来自 植入皮层的电极阵列可以快速、准确和直观地控制 为因脊髓损伤、中风、 或肌萎缩侧索硬化症（ALS）。使用皮质内脑机接口，四肢瘫痪的人可以 能够使用他们想象的手部动作来命令在某个物体上进行点击操作 电脑，使用虚拟键盘打字，使用聊天等通讯应用程序，并浏览 网络。想象的运动也被用来控制辅助设备，包括 DEKA 假肢、辅助机械臂，甚至是自己瘫痪的肢体，通过图案化 对瘫痪肌肉进行电刺激。微型无线信号的最新发展 发射器和无线、紧凑型、电池供电的神经信号处理器提高了 严重运动障碍人士使用安装在轮椅上的 iBCI 的潜力 无需技术援助即可在家独立进行。为了成为一种可行的辅助技术， iBCI 不仅必须具有移动性，而且还必须高性能、可靠且易于使用。这 研究通过转化算法创新来增强移动 iBCI 的所有这些方面 在各种临床前研究中得到证明，并对其进行优化以实现稳定、高性能 在移动 iBCI 中解码。这项研究首先将高度准确且响应迅速的 运动神经解码器（深度学习递归神经网络）在移动 iBCI 上运行 计算能力强大的嵌入式硬件。为了帮助随着时间的推移稳定运动解码， 增强性能并简化校准要求，本研究随后着眼于以下理论： 内在神经流形使降维（DR）技术适应高 多维、多尺度的人类神经数据。接下来，最先进的数据科学方法是 与多类分析集成，以促进对大量数据进行可靠、准确的分类 iBCI 用户想象的离散手势。接下来，评估 DR 方法以理清问题 同时进行运动学和手势解码，更流畅、更准确、更不受干扰 iBCI 控制。这些累积的方法将被转化为嵌入式硬件形式来运行 强大的移动处理器提供移动和触摸功能的按需控制 使用类似鼠标的移动和手势（例如滑动滚动和捏合操作）的设备 飞涨）。将独特的手势映射到附加功能将立即激活快捷键或 手势到短语的输出。使用这款安装在轮椅上的 iBCI，语言障碍人士 可以想象一个手势来生成文本到语音的问候语或寻求帮助。总体而言，这 研究利用最先进的机器学习创新来打造更有能力、 可靠且多功能的 iBCI 可促进严重运动障碍人士的独立性。