权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Robust Decoding of Neural Command for Real Time Human Machine Interactions

职业：实时人机交互的神经命令的鲁棒解码

基本信息

批准号：
2246162
负责人：
Xiaogang Hu
金额：
$ 54.95万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246162&HistoricalAwards=false
关键词：
CAREER Robust Decoding Neural Command

项目摘要

The human hand can produce complex dexterous movements, unmatched by any current robotic hand. Such sophisticated movements are often taken for granted. A majority of individuals with a stroke, however, tend to have persistent hand functional deficits, limiting their ability of living independently. Human-machine interactions hold great potential to restore motor functions of stroke survivors. Recently advanced rehabilitative or assistive techniques (e.g., hand exoskeletons) have the ability to substantially enhance motor functions. However, few of these state-of-the-art techniques have been successfully translated to end users, and one critical limiting factor is the challenge in controlling the many movement directions robustly. Therefore, there is an urgent need to develop non-invasive and robust neural decoding approaches for human-machine interactions that can directly translate to clinical applications. Accordingly, this project aims to decode the neural command sent from the brain that controls individual finger movements. This is accomplished by reading activities in the spinal cord using muscle electrical signals obtained from the skin surface. The decoded finger-specific neural command can then be used to control rehabilitation or assistive robots, which can substantially enhance the quality of human-machine interactions. This approach can also facilitate wide applications of robotic rehabilitation or assistance in stroke survivors. The non-invasive nature of the techniques has a great potential for readily clinical translations. The proposed research will be integrated with education through graduate and undergraduate research involvement and new course development. Summer projects and demonstration materials on human-machine interactions will be developed for K-12 students. Outreach programs will be organized to expose the proposed research topics to underrepresented students, highlight the opportunities in science and engineering, and promote students interests in choosing future STEM careers.The principal investigator's long-term research goal is to develop highly innovative non-invasive tools for human-machine interactions, with a particular interest in better understanding the neuromechanical properties of the upper extremity, and improve the functional performance in individuals with a central or peripheral injury. Toward this goal, this project aims to decode the descending neural command that controls individual finger movements by extracting spinal motoneuron discharge activities using source separation of high-density electromyogram signals (HD-EMG) from finger muscles. The non-invasive, robust, and real-time neural decoding technique developed will be easy to implement, can accommodate the different impairment levels of individual stroke survivors, and will substantially improve the control quality of exoskeleton or neuroprosthesis. The Research Plan is organized under three aims. The FIRST AIM is to develop non-invasive offline and real-time neural decoding approaches based on spinal motoneuron discharge probabilities at the population level that are directed at a designated finger. This aim addresses the need for non-invasive human-machine interface signals that allow robust and intuitive interaction between humans and machines. Surface EMG signals will be recorded over the targeted extrinsic muscles using an 8x16 channel electrode array with an inter-electrodedistance of 10 mm. Motoneuron discharge activities will be obtained from different independent component analysis (ICA)-based HD EMG decomposition methods that will be evaluated on both simulated and experimental EMG data obtained from stroke survivors and healthy control subjects. The decoding accuracy will be evaluated by comparing the decoded neural drive with finger force output and joint angles. Given that binary motoneuron discharge events are used, the decoded neural drive signals are expected to be robust to changes in action potential properties in the EMG signals, background noise, and motion artifacts. The evaluation of the performance and boundary conditions of different source separation algorithms can further ensure robust decoding performance in a variety of situations, especially in clinical populations. The SECOND AIM is to classify the neural command specific to individual finger movements. This aim addresses the need for effective control of individual/flexible finger movement in developing human-machine interactions. Surface EMG signals will be recorded over the extrinsic forearm muscles using an 8x16 channel HD EMG electrode array and over the intrinsic extensors muscles to fingers using an 8x4 channel grid. Different features from HD EMG activities and from motor unit (MU) distributions will be extracted. With macro and micro level features, different muscle activation regions will be identified for individual fingers using pattern classification approaches. The neural drive associated with specific finger movement will then be calculated based on MU discharge activities of a specific finger. The classified neural command signals can enable robust and flexible control of individual finger movements non-invasively, and dramatically enhance the dexterity of hand function in clinical populations. The THIRD AIM is to quantify the performance of the decoding technique by controlling a non-invasive neuroprosthesis for dexterous finger grasp patterns. A transcutaneous nerve stimulation technique developed in the PI's group will be used to elicit flexible individual and coordinated finger movements. The neural stimulation system targeting the affected hand of stroke survivors will be controlled by the decoded neural drive from the contralateral/unaffected arm (particularly if the stroke is severe) or from the affected arm, with time-sharing between stimulations and recordings. The force output (force absolute error and force variability) of neural drive controlled stimulation will be compared with the global EMG controlled stimulation to evaluate the performance of the neural decoding technique. The overall outcomes of the project are expected to ultimately allow stroke survivors to intuitively interact with rehabilitative/assistive devices in a robust and non-invasive manner.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.

人手可以做出复杂灵巧的动作，这是目前任何一台机器人手所无法比拟的。这种复杂的动作通常被认为是理所当然的。然而，大多数中风患者往往有持续的手部功能缺陷，限制了他们独立生活的能力。人机交互在恢复中风幸存者的运动功能方面具有很大的潜力。最近先进的康复或辅助技术（例如，手外骨骼）有能力大大增强运动功能。然而，这些最先进的技术很少成功地转化为最终用户，一个关键的限制因素是控制许多运动方向的挑战。因此，迫切需要开发非侵入性和鲁棒的神经解码方法，用于人机交互，可以直接转化为临床应用。因此，该项目旨在解码控制单个手指运动的大脑发出的神经指令。这是通过使用从皮肤表面获得的肌肉电信号来读取脊髓中的活动来完成的。解码后的手指特定神经指令可用于控制康复或辅助机器人，从而大大提高人机交互的质量。这种方法也可以促进机器人康复或帮助中风幸存者的广泛应用。该技术的非侵入性具有很大的临床应用潜力。建议的研究将通过研究生和本科生的研究参与和新课程开发与教育相结合。将为K-12学生开发人机交互的暑期项目和演示材料。将组织外展计划，向代表性不足的学生展示拟议的研究课题，突出科学和工程方面的机会，并提高学生选择未来STEM职业的兴趣。首席研究员的长期研究目标是开发高度创新的非侵入性人机交互工具，对更好地理解上肢的神经力学特性特别感兴趣，并改善中枢或外周损伤患者的功能表现。为了实现这一目标，本项目旨在通过使用来自手指肌肉的高密度肌电信号（HD-EMG）源分离提取脊髓运动神经元放电活动来解码控制单个手指运动的下行神经命令。所开发的非侵入性、鲁棒性和实时神经解码技术将易于实现，可以适应个体中风幸存者的不同损伤水平，并将大大提高外骨骼或神经假体的控制质量。研究计划有三个目标。第一个目标是开发非侵入性离线和实时神经解码方法，该方法基于针对指定手指的人群水平的脊髓运动神经元放电概率。这一目标解决了对非侵入性人机界面信号的需求，这些信号允许人与机器之间进行稳健和直观的交互。表面肌电信号将被记录在目标外部肌肉使用8 × 16通道电极阵列，电极间距为10毫米。运动神经元放电活动将通过不同的基于独立成分分析（ICA）的HD肌电信号分解方法获得，并将对脑卒中幸存者和健康对照者的模拟和实验肌电信号数据进行评估。通过将解码后的神经驱动与手指力输出和关节角度进行比较，来评估解码的准确性。考虑到使用二元运动神经元放电事件，解码的神经驱动信号有望对肌电信号中的动作电位特性变化、背景噪声和运动伪影具有鲁棒性。对不同信源分离算法的性能和边界条件的评估可以进一步确保在各种情况下，特别是在临床人群中具有鲁棒的解码性能。第二个目标是对特定于单个手指运动的神经指令进行分类。这一目标解决了在发展人机交互中有效控制个人/灵活手指运动的需要。表面肌电信号将使用8 × 16通道高清肌电信号电极阵列记录前臂外源性肌肉，并使用8 × 4通道网格记录手指内伸肌。将提取高清肌电图活动和运动单元（MU）分布的不同特征。结合宏观和微观层面的特征，使用模式分类方法可以识别单个手指的不同肌肉激活区域。然后根据特定手指的MU放电活动计算与特定手指运动相关的神经驱动。分类后的神经指令信号可以实现对单个手指运动的鲁棒性和灵活性的无创控制，极大地提高了临床人群手部功能的灵活性。第三个目标是通过控制非侵入性神经假体来量化解码技术的性能，用于灵巧的手指抓取模式。PI小组开发的经皮神经刺激技术将用于引发灵活的个人和协调的手指运动。针对中风幸存者受影响的手的神经刺激系统将由来自对侧/未受影响的手臂（特别是如果中风严重）或受影响的手臂的解码神经驱动来控制，刺激和记录之间是分时的。将神经驱动控制刺激的力输出（力的绝对误差和力的可变性）与全局肌电控制刺激进行比较，以评价神经解码技术的性能。该项目的总体结果预计最终将使中风幸存者能够以稳健和非侵入性的方式直观地与康复/辅助设备进行交互。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。