Mobile Signal Processing System for Broadband Neural Decoding

用于宽带神经解码的移动信号处理系统

• 批准号: 9000722
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9000722
• 关键词: Algorithmic Software; Algorithms; Amputation; Amyotrophic Lateral Sclerosis; Android; Artificial Arm; Brain; Brain Stem; Caring; Clinical; Clinical Trials; Communication; Communication Tools; Computer Systems; Computer software; Computers; Conflict (Psychology); Coupled; Data; Development; Devices; Disabled Persons; Electric Stimulation; Engineering; Environment; Epilepsy; Foundations; Future; Generations; Goals; Hand; Health; Home environment; Incidence; Individual; Industry; Injury; Language; Limb structure; Link; Locked-In Syndrome; Medical; Medical center; Motor; Movement; Muscle; Neurosciences; Operating System; Paralysed; Participant; Pattern; Performance; Persons; Population; Process; Productivity; Prosthesis; Quadriplegia; Research; Research Personnel; Robotics; Running; Self-Help Devices; Signal Transduction; Spinal cord injury; Stream; Stroke; Structure of nail of finger; System; Tablets; Technology; Testing; Thinking; Time; Universities; Upper Extremity; Validation; Veterans; Wheelchairs; Wireless Technology; Work; application programming interface; arm; brain machine interface; computer science; computerized data processing; data management; design; disability; graphical user interface; grasp; handheld mobile device; innovation; interoperability; laptop; light weight; limb amputation; microsystems; mind control; mobile application; mobile computing; models and simulation; neural prosthesis; neuroregulation; neurorestoration; neurotechnology; neurotransmission; novel; programs; relating to nervous system; sensor; signal processing; simulation; software development; user-friendly; wireless fidelity

DESCRIPTION (provided by applicant): Developing technologies to assist persons with severe movement disabilities, paralysis, locked-in syndrome or limb amputations is a priority for the Department of Veterans Affairs. ALS (Lou Gehrig's disease), which results in complete paralysis, has a disproportionately high incidence among the veteran population. Due to injuries sustained in recent conflicts, care for veterans with upper extremity amputation is increasing. The VA has advanced assistive technologies including direct neural control of communication software for persons with ALS, revolutionary prosthetic arms, and functional electrical stimulation (FES) of paralyzed limbs. Evidence from the ongoing pilot clinical trial of BrainGate2 (IDE), an implantable brain signal sensor coupled with a neural decoding system, indicates that individuals with paralysis can use their brain signals to control software or robotic/prosthetic arms years after spinal cord injury, brain stem stroke, or the onset of ALS. Although direct brain control promises effective, adept control of enabling technologies, current neural decoding algorithms run on cumbersome, immobile computer systems. Reducing these platforms to a compact, wearable device would achieve cornerstone advances that could enable persons with paralysis to move themselves in a wheelchair independently under brain control, allow persons with locked- in syndrome to access neurally-controlled communication tools anywhere, permit ambulatory individuals with limb amputation to control advanced prostheses such as the DEKA arm/hand with their own thoughts, or let people with upper limb paralysis reach and grasp with their own muscles using brain-controlled FES. The proposed research achieves this goal by exploiting commercially-available mobile microsystem technology to produce a battery-powered, high-performance wearable device capable of wirelessly receiving, processing, and decoding brain signals and generating commands to operate nearby mobile assistive technologies. First, a powerful new generation of programmable gate arrays (FPGAs) allows neural decoding algorithm software that currently runs on a Windows computer to be converted to a hardware description language (HDL) that runs orders of magnitude faster on a fingernail-sized FPGA chip. For research, FPGAs can readily be re-programmed to test novel neural decoding algorithms. Second, high-performance, low- power processors developed for mobile applications provide the requisite data management and wireless communication capabilities to receive neural signals and transmit commands. The integrated system will execute all signal processing and decoding functions required by the present BrainGate2 neural interface system yet in a wearable, battery powered package. Interface software will be developed to allow engineers to rapidly reconfigure the device and to allow individuals with disability (or their assistants) to adjust the device durig use. Functionality will be validated in the BrainGate simulation environment. Then, individuals with tetraplegia in the BrainGate2 trial will test it while controlling assistive software and prosthetic devices. Research will be performed at the Providence VA Medical Center and Brown University. This research team has well-establish expertise in the development of innovative microelectronics and neural prosthetic systems. The Principle Investigator has directed BrainGate systems engineering and development for years with productivity both in neural prosthetics research and in industry developing innovative microelectronic systems. The PI and co-investigators have demonstrated the consistent ability to integrate engineering, neuroscience, computer science, and clinical expertise to deliver meaningful leading-edge research that is transforming neural prosthetic technology to assist persons with severe movement disability. By integrating this expertise, the current proposal will yield a wearable device with the unprecedented data throughput and processing performance required for real-time decoding of neural signals to enable users with disability to control enabling, mobile assistive devices.

描述（由申请人提供）： 退伍军人事务部的首要任务是开发技术来帮助患有严重运动障碍、瘫痪、闭锁综合症或截肢的人。 ALS（卢伽雷氏病）会导致完全瘫痪，在退伍军人群体中发病率极高。由于最近的冲突中受伤，对上肢截肢退伍军人的护理正在增加。 VA 拥有先进的辅助技术，包括针对 ALS 患者的通信软件的直接神经控制、革命性的假肢以及瘫痪肢体的功能性电刺激 (FES)。 BrainGate2（IDE）是一种与神经解码系统相结合的植入式大脑信号传感器，正在进行的试点临床试验的证据表明，瘫痪患者可以在脊髓损伤数年后使用其大脑信号来控制软件或机器人/假肢手臂，大脑茎中风，或 ALS 发作。尽管直接大脑控制有望有效、熟练地控制使能技术，但当前的神经解码算法运行在笨重、不可移动的计算机系统上。将这些平台简化为紧凑的可穿戴设备将实现基石进步，使瘫痪者能够在大脑控制下独立地坐在轮椅上移动，允许闭锁综合症患者在任何地方使用神经控制的通信工具，允许走动的人肢体截肢，以自己的意念控制 DEKA 手臂/手等先进假肢，或者使用脑控 FES 让上肢瘫痪的人用自己的肌肉伸手抓握。 拟议的研究通过利用商用移动微系统技术来生产电池供电的高性能可穿戴设备来实现这一目标，该设备能够无线接收、处理和解码大脑信号并生成操作附近移动辅助技术的命令。首先，强大的新一代可编程门阵列 (FPGA) 允许将当前在 Windows 计算机上运行的神经解码算法软件转换为硬件描述语言 (HDL)，从而在指甲盖大小的 FPGA 芯片上运行速度提高几个数量级。对于研究而言，FPGA 可以轻松地重新编程以测试新颖的神经解码算法。其次，为移动应用开发的高性能、低功耗处理器提供了接收神经信号和传输命令所需的数据管理和无线通信功能。该集成系统将执行当前 BrainGate2 神经接口系统所需的所有信号处理和解码功能，但采用可穿戴、电池供电的封装。将开发接口软件，以允许工程师快速重新配置设备，并允许残疾人（或其助手）在使用过程中调整设备。功能将在 BrainGate 模拟环境中得到验证。然后，BrainGate2 试验中四肢瘫痪的个体将在控制辅助软件和假肢设备的同时进行测试。 研究将在普罗维登斯退伍军人医疗中心和布朗大学进行。该研究团队在创新微电子和神经修复系统的开发方面拥有完善的专业知识。首席研究员多年来一直指导 BrainGate 系统工程和开发，在神经修复研究和创新微电子系统的行业开发方面颇有成效。首席研究员和共同研究人员展示了整合工程、神经科学、计算机科学和临床专业知识的一贯能力，以提供有意义的前沿研究，这些研究正在改变神经假体技术，以帮助患有严重运动障碍的人。通过整合这些专业知识，当前的提案将产生一种可穿戴设备，该设备具有神经信号实时解码所需的前所未有的数据吞吐量和处理性能，使残疾用户能够控制移动辅助设备。