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）： 开发技术以帮助严重运动障碍，瘫痪，锁定综合征或肢体截肢者的技术是退伍军人事务部的优先事项。 ALS（Lou Gehrig的氏病），导致完全麻痹，在退伍军人人口中的发生率较高。由于最近的冲突中遭受的伤害，对上肢截肢的退伍军人的照顾正在增加。 VA具有先进的辅助技术，包括针对ALS，革命性假肢和瘫痪的四肢的功能电刺激（FES）的通信软件的直接神经控制。 Braingate2（IDE）正在进行的试验临床试验（IDE）的证据是一种可植入的脑信号传感器，并结合神经解码系统，表明麻痹的个体可以使用其脑信号来控制软件或机器人/假体臂几年后几年后的脊髓损伤，脑干中风或ALS的发作。尽管直接的大脑控制有望有效，可以控制能力技术，但当前的神经解码算法在麻烦的，不动的计算机系统上运行。将这些平台减少到紧凑的可穿戴设备将达到基石的进步，这可以使麻痹的人独立地在轮椅上移动自己的轮椅，允许综合症中有锁定的人 - 在任何地方进行神经控制的交流工具，可以在任何地方访问具有肢体肢体的肢体距离，或者允许与他们的远距离伸出来的人，或者与他们的距离伸出来，或者将自己的思想伸出来，或者将自己的思想伸出来，或者将自己的想法交往，或使用脑控制的FES自己的肌肉。 拟议的研究通过利用商业上可用的移动微系统技术来实现这一目标，以生产能够无线接收，处理和解码大脑信号并生成在附近运行移动辅助技术的命令的电池供电，高性能的可穿戴设备。首先，强大的新一代可编程门阵列（FPGA）允许当前在Windows计算机上运行的神经解码算法软件将转换为硬件说明语言（HDL），该语言（HDL）在指甲尺寸的FPGA芯片上运行的数量级更快。对于研究，可以轻松地重新编程FPGA以测试新型神经解码算法。其次，为移动应用程序开发的高性能，低功率处理器提供了必要的数据管理和无线通信功能，以接收神经信号和传输命令。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，但可以用可穿戴的电池供电包装。将开发界面软件，以允许工程师快速重新配置设备，并允许残疾人（或其助手）调整设备Durig的使用。功能将在Braingate仿真环境中进行验证。然后，在Braingate2试验中患有四边形的个体将在控制辅助软件和假肢设备时对其进行测试。 研究将在Providence VA医学中心和布朗大学进行。该研究团队在创新的微电子和神经假体系统的发展方面拥有丰富的专业知识。多年来，主要研究者在神经假体研究和行业开发创新的微电子系统中指导了布雷格酸盐系统的工程和开发。 PI和共同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能力，以提供有意义的前沿研究，这正在改变神经假体技术，以帮助有严重运动残疾的人。通过集成此专业知识，当前的建议将产生可穿戴设备，并带有前所未有的数据吞吐量和处理性能的神经信号所需的处理性能，以使残疾用户能够控制启用的移动辅助设备。