A bioelectric neural interface towards intuitive prosthetic control for amputees
A bioelectric neural interface towards intuitive prosthetic control for amputees
复制标题
一种面向截肢者直观假肢控制的生物电神经接口
DOI：
10.1088/1741-2552/abc3d3
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发表时间：
2020-12-01
影响因子：
4
通讯作者：
Yang, Zhi
中科院分区：
文献类型：
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作者：
Anh Tuan Nguyen;Xu, Jian;Yang, Zhi
目标。虽然具有独立驱动手指的假手已经商业化，但最先进的人机界面(HMI)只允许控制有限的抓握模式，这无法使截肢者在日常活动中体验到足够的改善，从而使主动假肢变得有用。接近。在这里，我们提出了一个技术平台，结合了完全集成的生物电子学，可植入的束内微电极和基于深度学习的人工智能(AI)，通过利用周围神经复杂的运动控制信号来促进这座缺失的桥梁。生物电神经接口包括一个超低噪声神经记录系统，用于从植入残留神经的微电极阵列中感测神经电(ENG)信号，以及采用递归神经网络(RNN)结构的人工智能模型，以解码受试者的运动意图。主要结果。一项试验性的人体研究已经在一名横过桡骨的截肢者身上进行。我们证明，由所提出的神经接口建立的信息通道足以为高达15个自由度(DOF)的假手提供高精度控制。该界面直观，因为它直接将复杂的假肢运动映射到患者的真实意图。意义重大。我们的研究不仅为现代神经假体的健壮和灵巧的控制策略奠定了基础，使其接近于能手的水平，而且还为通过周围神经通路连接人类大脑和机器的直观管道奠定了基础。临床试验：通过Fascular靶向的灵巧手控制(DIFT)。标识：NCT02994160。
Objective. While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. Approach. Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention. Main results. A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention. Significance. Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways.Clinical trial: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.