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: 9186959
• 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 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9186959
• 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; Custom; Data; Development; Devices; Disabled Persons; Electric Stimulation; Engineering; Environment; Epilepsy; Foundations; Future; Generations; Goals; Hand; 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; portability; programs; prototype; public health relevance; 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）： 开发技术以帮助有严重行动残疾、瘫痪、闭锁综合征或截肢的人是退伍军人事务部的一个优先事项。导致完全瘫痪的ALS（卢伽雷氏病）在退伍军人中的发病率不成比例地高。由于在最近的冲突中受伤，上肢截肢的退伍军人的护理正在增加。VA拥有先进的辅助技术，包括ALS患者的直接神经控制通信软件，革命性的假肢和瘫痪肢体的功能性电刺激（FES）。BrainGate 2（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、脑干损伤多年后使用大脑信号来控制软件或机器人/假肢。中风或ALS发作。虽然直接的大脑控制承诺有效，熟练的控制使技术，目前的神经解码算法运行在笨重，固定的计算机系统。将这些平台简化为紧凑的可穿戴设备将实现基础性的进步，其可以使瘫痪的人能够在大脑控制下独立地在轮椅中移动自己，允许患有闭锁综合征的人在任何地方访问神经控制的通信工具，允许截肢的走动的个人用他们自己的思想控制先进的假肢，例如DEKA手臂/手，或者让上肢瘫痪的人用大脑控制的FES用他们自己的肌肉来达到和抓住。 拟议的研究通过利用商用移动的微系统技术来实现这一目标，以生产电池供电的高性能可穿戴设备，该设备能够无线接收、处理和解码大脑信号，并生成命令来操作附近的移动的辅助技术。首先，功能强大的新一代可编程门阵列（FPGA）允许将目前在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），该语言在指甲大小的FPGA芯片上运行速度更快。对于研究，FPGA可以很容易地重新编程，以测试新的神经解码算法。其次，为移动的应用开发的高性能、低功率处理器提供了必要的数据管理和无线通信能力，以接收神经信号和发送命令。集成系统将执行本BrainGate 2神经接口系统所需的所有信号处理和解码功能，但仍处于可穿戴的电池供电包中。将开发接口软件，使工程师能够快速重新配置设备，并允许残疾人（或其助手）在使用过程中调整设备。将在BrainGate模拟环境中确认功能。然后，BrainGate 2试验中的四肢瘫痪患者将在控制辅助软件和假肢设备的同时对其进行测试。 研究将在普罗维登斯VA医学中心和布朗大学进行。该研究团队在创新微电子和神经假体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，在神经修复研究和工业开发创新微电子系统方面都很有成效。PI和合作研究者已经证明了整合工程，神经科学，计算机科学和临床专业知识的一致能力，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，目前的提案将产生一种可穿戴设备，其具有实时解码神经信号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启用的移动的辅助设备。