Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Université de Bordeaux, France.
J Neural Eng. 2018 Apr;15(2):026006. doi: 10.1088/1741-2552/aa87cf.
To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls.
We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner.
First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations.
The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional myoelectric contexts are discussed.
为了补偿截肢失去的肢体，肌电假肢使用残留在肌肉中的表面肌电图（EMG）来控制假肢。尽管取得了相当大的进展，但肌电控制仍然与我们正常控制运动的方式有很大的不同，并且需要用户进行强烈的适应。为了克服这一点，我们的目标是探索正在脑机接口领域发展并开始用于肌电控制的并行机器协同适应技术。
我们将简化的肌电控制与一种已被很好地描述和建模的人为适应的扰动相结合，以便以有原则的方式探索协同适应的设置。
首先，我们在简化的肌电环境中重现了在经典的视觉运动旋转范式中获得的结果，其中我们旋转用于从 EMG 重建手腕力的肌肉拉动向量。然后，使用针对方向误差的人类适应模型来模拟各种协同适应设置，其中同时对肌肉拉动向量施加扰动和机器协同适应。这些模拟表明，机器协同适应的相对低增益（将最终误差最小化）会导致缓慢且不完全的适应，而较高的增益会增加适应速度，但也会通过放大噪声导致更多的误差。在对真实受试者进行实验验证之后，我们测试了一个可变增益，该增益累积了两者的优点，并使用类似于用于模拟人类适应的定向调谐神经元来实现。这使机器协同适应能够局部改善肌电控制，并吸收更具挑战性的扰动。
这里使用的简化环境使我们能够在模拟和实验中探索协同适应设置，并提出了一些重要的考虑因素，例如需要局部编码可变增益。讨论了将这种方法扩展到更复杂和功能性肌电环境的好处和限制。
