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

上肢的丧失通常是由创伤事件引起的，如工伤、道路交通事故和军事伤亡。与其他类型的截肢不同，几乎四分之三的上肢患者是年轻人（小于55岁）和其他健康个体。尽管经过了几十年的研究，商业活动假肢仍然使用60年代开发的技术，即通过表面电极进行肌电控制。简单地说，它们是通过使用少量表面电极从患者的残余前臂肌肉中记录的电脉冲来控制的。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自愿信息的限制。因此，即使是最先进的假肢也只允许少量预先定义的简单握把形状供用户选择。肌电义肢的使用者经常表示希望改善功能，更大范围的握把形状，以及更直观的比例控制。当今假肢的一个主要问题是，运动是通过分类器（即训练识别肌电信号模式的算法）解码的，必须针对特定的运动进行训练。在这个项目中，我们对开发新的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，该阵列记录了大量紧密间隔的电极，结合了最先进的信号处理和神经解码技术。hd-sEMG的使用允许通过访问单个运动单元动作电位来提取更多信息，这些动作电位可用于重建神经驱动，即编码发送到肌肉的运动任务信息的电脉冲（尖峰）序列。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，首席研究员是这方面的专家。这些算法的工作原理是，首先试图理解运动任务是如何在spike spike序列中“编码”的，然后还原这一过程，以便根据观察到的信号推断出最可能的运动任务。它们可以用任意动作进行训练，并且有可能解码训练中从未观察到的复杂动作。最终目标是拥有一个允许任意移动的控制器，而不是一组预定义的移动。在整个项目中，首席研究员和研究助理将受益于与机器人假肢和神经信号处理领域的世界专家合作。

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.