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：
1847319
负责人：
Xiaogang Hu
金额：
$ 54.95万
依托单位：
University of North Carolina at Chapel Hill
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1847319&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.

人的手可以产生复杂的灵巧运动，这是任何当前机器人手无法比拟的。这种复杂的动作通常被认为是理所当然的。然而，大多数患有中风的人倾向于持续存在手工功能不足，从而限制了他们独立的生活能力。人机相互作用具有恢复中风幸存者运动功能的巨大潜力。最近先进的康复或辅助技术（例如，手部外骨骼）具有实质上增强运动功能的能力。但是，这些最先进的技术中很少有人成功地转化为最终用户，而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，以直接转化为临床应用。因此，该项目旨在解码从大脑中发送的神经命令，以控制单个手指的运动。这是通过使用从皮肤表面获得的肌肉电信号在脊髓中读取活动来完成的。然后，可以使用解码的手指特异性神经命令来控制康复或辅助机器人，这可以大大提高人机相互作用的质量。这种方法还可以促进机器人康复或中风幸存者的援助的广泛应用。该技术的无创性具有容易临床翻译的巨大潜力。拟议的研究将通过研究生和本科研究参与以及新课程发展与教育融合。将为K-12学生开发有关人机相互作用的夏季项目和演示材料。将组织外展计划将拟议的研究主题暴露给缺乏代表性的学生，强调科学和工程学的机会，并促进学生在选择未来的STEM职业方面的兴趣。主要研究者的长期研究目标是开发具有高度创新的非侵入性工具，以促进人类互动的特殊兴趣，以更好地理解具有神经机械性能的人的特殊性，并促进了善良的势力，并具有较高的极端性能，并具有一定的极端性能。为了实现这一目标，该项目旨在通过使用高密度肌电图信号（HD-EMG）从指手指肌肉中提取脊柱运动神经元放电活动来解码降序神经命令，该命令通过提取脊柱运动神经元排放活性来控制单个手指的运动。开发的非侵入性，鲁棒和实时的神经解码技术将很容易实施，可以适应单个中风幸存者的不同损伤水平，并将大大提高外骨骨骼或神经假体的控制质量。研究计划是根据三个目标组织的。第一个目的是基于针对指定的手指的人群水平的脊柱运动神经元排放概率开发非侵入性离线和实时神经解码方法。这个目的解决了对非侵入性人机接口信号的需求，该信号允许人与机器之间的稳健和直观的相互作用。将使用8x16通道电极阵列在目标外部肌肉上记录表面EMG信号，其电源间隔为10 mm。将从不同的独立组件分析（ICA）的HD EMG分解方法中获得运动神经元排放活性，该方法将对从中风幸存者和健康对照受试者获得的模拟和实验EMG数据进行评估。通过将解码的神经驱动与手指力输出和关节角度进行比较，可以评估解码精度。鉴于使用了二进制运动神经元排出事件，因此，解码的神经驱动信号有望与EMG信号，背景噪声和运动伪像的动作电位特性变化具有牢固的变化。对不同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，尤其是在临床人群中的强劲解码性能。第二个目的是将特定于各个手指运动的神经命令分类。该目的旨在有效地控制人机相互作用中的个体/灵活手指运动。使用8x16通道HD EMG电极阵列在外部前臂肌肉上记录表面EMG信号，并使用8x4通道网格在手指上固有延伸器肌肉。将提取HD EMG活动和运动单元（MU）分布的不同功能。使用宏观和微水平特征，将使用模式分类方法为单个手指确定不同的肌肉激活区域。然后，将根据特定手指的MU放电活性来计算与特定手指运动相关的神经驱动。机密的神经命令信号可以非侵入性地对单个手指运动进行稳健而灵活的控制，并显着增强临床种群中手功能的灵巧性。第三个目的是通过控制灵活的指手指抓握模式来量化解码技术的性能。 PI组中开发的经皮神经刺激技术将用于引起柔性个体和协调的手指运动。靶向中风幸存者手的神经刺激系统将由对侧/未影响的臂（尤其是中风很严重）或受影响的手臂的解码神经驱动器控制，并在刺激和记录之间进行时间间隔。神经驱动控制刺激的力输出（绝对误差和力变异性）将与全局EMG控制刺激进行比较，以评估神经解码技术的性能。预计该项目的总体结果最终将使中风幸存者以强大和非侵入性的方式与康复/辅助设备进行直观的互动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来获得支持的。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multichannel Nerve Stimulation for Diverse Activation of Finger Flexors

多通道神经刺激可多样化激活手指屈肌

DOI：
10.1109/tnsre.2019.2947785
发表时间：
2019
期刊：
IEEE Transactions on Neural Systems and Rehabilitation Engineering
影响因子：
4.9
作者：
Shin, Henry;Hu, Xiaogang
通讯作者：
Hu, Xiaogang

Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information

DOI：
10.1109/tbme.2021.3056930
发表时间：
2021-02
期刊：
IEEE Transactions on Biomedical Engineering
影响因子：
4.6
作者：
Yang Zheng;Xiaogang Hu
通讯作者：
Yang Zheng;Xiaogang Hu

Real-time finger force prediction via parallel convolutional neural networks: a preliminary study

通过并行卷积神经网络进行实时手指力预测：初步研究

DOI：
10.1109/embc44109.2020.9175390
发表时间：
2020
期刊：
Proceedings of IEEE Engineering in Medicine and Biology Society Annual Meeting
影响因子：
0
作者：
Xu, Feng;Zheng, Yang;Hu, Xiaogang
通讯作者：
Hu, Xiaogang

Assessment of Impaired Finger Independence of Stroke Survivors: A Preliminary study

中风幸存者手指独立性受损的评估：初步研究