Decoding the neural drive for finer and more intuitive control of a myoelectric robotic hand

解码神经驱动，以更精细、更直观地控制肌电机器人手

• 批准号: EP/N031806/1
• 负责人: Luca Citi
• 金额: $ 12.86万
• 依托单位: University of Essex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN031806%2F1
• 关键词: Decoding; neural; drive; finer; more

The loss of an upper limb is often caused by traumatic events, such as work-related injuries, road traffic accidents, and military casualties. Unlike other types of amputation, almost three quarters of upper limb referrals are young (less than 55 years) and otherwise healthy individuals. Despite decades of research, commercial active prostheses still use technology developed in the sixties, namely myoelectric control via superficial electrodes. Stated simply, they are controlled by electrical impulses recorded from the patient's residual forearm muscles using a small number of surface electrodes. Their functionality is constrained by the limited amount of voluntary information that can be extracted from the small number of surface electrodes adopted. As a result, even the most advanced of these prostheses only allow a small number of pre-defined simple grip shapes that the user can select from. Users of myoelectric prostheses often express a desire for improved functionality, wider range of grip shapes, and more intuitive proportional control. A main problem of today's prostheses is that the movements are decoded through classifiers (i.e. algorithms trained to recognize patterns in the electromyographic signal), which must be trained for the specific movement.Within this project, we conduct ambitious research into the development of novel decoding algorithms to make the control of myoelectric hand prostheses more natural, intuitive, and accurate. Our approach uses recently developed high-density surface electromyographic (hd-sEMG) arrays, which record from a high number of closely spaced electrodes, combined with the most advanced signal processing and neural decoding techniques.The use of hd-sEMG allows the extraction of more information by giving access to individual motor unit action potentials which can be used to reconstruct the neural drive, i.e. the train of electrical pulses (spikes) that encode the information on the motor task sent to the muscles.Access to these spike trains allows the use of a type of statistical models and algorithms, called "point processes", of which the principal investigator is an expert. These algorithms work by first trying to understand how the motor task is "encoded" in the spike spike trains and then reverting this process in order to infer the most likely motor task given the observed signal. They can be trained with arbitrary movements and have the potential to decode complex movements that were never observed during training. The ultimate goal is to have a controller that allows arbitrary movements rather than a set of pre-defined movements.Throughout the project, the principal investigator and the research assistant will benefit from collaborating with world experts in the field of robotic prosthetic hands and neural signal processing.

上肢的丧失通常是由创伤性事件引起的，如工伤、道路交通事故和军事伤亡。与其他类型的截肢不同，近四分之三的上肢转诊是年轻人（不到55岁）和其他健康的人。尽管经过数十年的研究，商业活动假肢仍然使用六十年代开发的技术，即通过表面电极进行肌电控制。简单地说，它们是由使用少量表面电极从患者的残余前臂肌肉记录的电脉冲控制的。它们的功能受到可以从所采用的少量表面电极中提取的有限数量的自愿信息的限制。因此，即使是这些假体中最先进的，也只允许用户可以选择的少量预定义的简单抓握形状。肌电假肢的使用者经常表达对改进的功能、更宽范围的抓握形状和更直观的比例控制的期望。目前的假肢存在的主要问题是，动作是通过分类器（即经过训练以识别肌电信号模式的算法）进行解码的，而分类器必须针对特定的动作进行训练。在本项目中，我们对开发新型解码算法进行了雄心勃勃的研究，以使肌电假肢的控制更加自然、直观和准确。我们的方法使用了最近开发的高密度表面肌电图（hd-sEMG）阵列，它记录了大量密集的电极，结合了最先进的信号处理和神经解码技术。使用hd-sEMG可以通过访问单个运动单元动作电位来提取更多的信息，这些动作电位可以用于重建神经驱动，即电脉冲序列（尖峰），其编码发送到肌肉的关于运动任务的信息。访问这些尖峰序列允许使用一种称为“点过程”的统计模型和算法，主要研究者是这方面的专家。这些算法首先试图理解运动任务如何在尖峰尖峰序列中“编码”，然后恢复这个过程，以便在给定观察到的信号的情况下推断出最可能的运动任务。它们可以接受任意动作的训练，并有可能解码训练期间从未观察到的复杂动作。最终目标是拥有一个控制器，允许任意运动，而不是一组预定义的运动。在整个项目中，首席研究员和研究助理将受益于与世界各地的专家在机器人假手和神经信号处理领域的合作。

项目成果

Converging Clinical and Engineering Research on Neurorehabilitation III - Proceedings of the 4th International Conference on NeuroRehabilitation (ICNR2018), October 16-20, 2018, Pisa, Italy

神经康复的融合临床与工程研究 III - 第四届神经康复国际会议 (ICNR2018) 论文集，2018 年 10 月 16-20 日，意大利比萨

• DOI: 10.1007/978-3-030-01845-0_27
• 发表时间: 2019
• 作者: Siadatnejad S