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: 10674504
• 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/10674504
• 关键词: 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; Maps; 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; Tablets; Techniques; Technology; Text; Time; Touch sensation; Translating; Upper Extremity; User-Computer Interface; Veterans; Wheelchairs; arm; assistive robot; autoencoder; brain computer interface; comparison control; deep learning; deep neural network; disability; feature extraction; finger movement; grasp; handheld mobile device; high dimensionality; improved; innovation; interest; kinematics; long short term memory; markov model; mobile application; neural; neurotransmission; phrases; preclinical study; prototype; recurrent neural network; recursive neural network; simulation; theories; two-dimensional; virtual; wireless; 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; Maps; 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; Tablets; Techniques; Technology; Text; Time; Touch sensation; Translating; Upper Extremity; User-Computer Interface; Veterans; Wheelchairs; arm; assistive robot; autoencoder; brain computer interface; comparison control; deep learning; deep neural network; disability; feature extraction; finger movement; grasp; handheld mobile device; high dimensionality; improved; innovation; interest; kinematics; long short term memory; markov model; mobile application; neural; neurotransmission; phrases; preclinical study; prototype; recurrent neural network; recursive neural network; 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.

心脏内脑机构界面（IBCIS）记录和处理从 植入皮层的电极阵列以快速，准确，直观的控制 因脊髓损伤，中风，患有麻痹的个体的个人的辅助技术 或肌萎缩性侧索硬化症（ALS）。使用心脏内BCI，四肢瘫痪的人患有 他们能够利用自己想象的手动作来指挥当点击动作 计算机，使用虚拟键盘键入，使用聊天等通信应用程序，然后浏览 网络。想象的运动也已用于控制包括DEKA在内的辅助设备 假肢，辅助机器人手臂，甚至是自己的瘫痪的肢体 瘫痪肌肉的电刺激。微型无线信号的最新发展 发射器和无线，紧凑的电池式神经信号处理器已提高 严重运动障碍的人使用轮椅安装的IBCI的潜力 在没有技术帮助的情况下独立在家中。成为可行的辅助技术， IBCI不仅必须是移动设备，而且还必须是高性能，可靠且直观的使用。这 研究通过翻译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证明并将其优化为稳定，高性能 在移动IBCI中解码。这项研究首先改变了高度准确响应的 运动型神经模型（一个深度学习递归神经元网络），可以在移动IBCI上运行 计算功能强大的嵌入式硬件。为了帮助稳定运动解码，随着时间的流逝 提高性能并轻松校准要求，然后研究 内在的神经歧管以降低维度（DR）技术适应高 - 维度，多尺度人类神经数据。接下来，最先进的数据科学方法是 与多类分析集成，以促进一组可靠，准确的分类 IBCI用户想象的离散手势。接下来，将DR方法评估为删除 简单的运动学和手势解码，以使平滑，更准确，不受干扰 IBCI控制。这些累积方法将被翻译成嵌入式硬件表格以运行 在功能强大的移动处理器上，以提供对移动和触摸的按需控制 使用类似鼠标的动作和手势的设备（例如滑动到滚动和捏 飞涨）。将独特的手势映射到其他功能将立即激活钥匙快捷方式或 手势到短语输出。使用此轮椅安装的IBCI，一个语音划定的个人 可以想象一个手势来产生文本到语音问候或寻求帮助。总体而言，这 研究利用最先进的机器学习创新， 可靠且多才多艺的IBCI可促进严重运动残疾人的独立性。