CAREER: Next-Generation Neural-Machine Interfaces for Electromyography-Controlled Neurorehabilitation

全球数百万患者的生命受到上肢丧失或损伤的严重影响。一种新兴的技术，基于肌电图（EMG）的神经机器接口（NMI），提供了巨大的潜力，在恢复功能，通过神经修复这一人群，包括截肢者，中风幸存者和脑瘫患者。该技术感测来自肌肉的生物电信号，解释它们以识别患者的预期运动，并做出决定以控制神经康复应用（例如，假肢）。虽然神经康复系统的设计在过去的几十年里取得了显著的进步，但目前没有一个系统能够满足商业和临床实施的所有期望的技术规范。该项目采用计算机工程方法来改善基于EMG的NMI技术的功能和鲁棒性。将开发用于管理传感器状态和实时响应的软件，并将实施新的计算平台，以处理响应性神经康复应用所需的大规模数据密集型计算。该项目通过几种途径整合研究和教育：增强本科课程与嵌入式研究经验，开发大规模开放式神经机器接口在线课程，并通过社区大学推广计划启动K-12。PI的长期职业目标是开发下一代NMI，将人们连接起来，并在神经康复研究中探索大数据和深度学习技术。为了实现这一目标，该项目的目标是（1）开发新的硬件和软件方法，以便在基于EMG的实时NMI中使用高密度网格传感技术，以提高NMI的功能性和鲁棒性和（2）开发新的计算技术，以便计算能力和存储容量不再成为NMI神经康复研究进步的障碍。PI将首先通过采用网格状态感知和响应引擎来密切监控EMD网格的状态并做出相应响应，从而解决将高密度EMG网格应用于实时NMI的挑战。高密度EMG网格所带来的计算负担问题将通过开发神经形态计算系统来解决。最后，将开发一个分层计算平台，提供足够的计算和存储能力，以实现实时响应和便携性。基于EMG的NMI设计的见解和进步将显着提高EMG控制神经康复系统的可靠性和功能。此外，开发的NMI方法和工具适用于神经康复应用以外的研究领域，例如脑机接口。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The lives of millions of patients worldwide are severely impacted by upper extremity loss or impairment. An emerging technology, electromyography (EMG)-based Neural Machine Interface (NMI), offers enormous potential in the restoration of function through neuroprosthetics for this population, including amputees, stroke survivors, and cerebral palsy patients. The technology senses bioelectrical signals from muscles, interprets them to identify the intended movement of the patient, and makes decisions to control neurorehabilitation applications (e.g., a prosthetic limb). While neurorehabilitation system design has progressed remarkably over several decades, no system is currently capable of meeting all desired technical specifications for commercial and clinical implementation. This project takes a computer engineering approach toward improving EMG-based NMI technology functionality and robustness. Software will be developed for managing the sensor status and real-time responses, and novel computing platforms will be implemented to handle the large-scale, data-intensive computations required for responsive neurorehabilitation applications. The project integrates research and education through several avenues: enhancement of undergraduate curricula with embedded research experiences, development of a massive open online course on neural machine interface, and initiation of a K-12 through community college outreach program. The PI's long-term career goal is to develop next-generation NMIs that will connect people and enable the exploration of big data and deep learning technologies in neurorehabilitation research. Toward this goal, the project's objectives are to (1) develop new hardware and software methods to enable the use of high-density grid sensing technology in real-time EMG-based NMIs to improve the functionality and robustness of the NMIs and (2) develop new computing technologies so that computing power and storage capacity are no longer barriers to the advancement of NMI neurorehabilitation research. The PI will first address the challenge of applying high-density EMG grids to real-time NMIs by employing a Grid Status Awareness and Response Engine to closely monitor the status of the EMD grids and respond accordingly. The issue of computational burden posed by the high-density EMG grids will then be tackled through the development of a neuromorphic computing system. Finally, a hierarchical computing platform that provides sufficient computational and storage capabilities to enable real-time response and portability will be developed. Insights and advancements made here in EMG-based NMI design will markedly improve reliability and functionality of EMG-controlled neurorehabilitation systems. Additionally, the developed NMI methods and tools are applicable to research fields beyond neurorehabilitation applications, such as brain-computer interfaces.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.