In the past decade, there has been significant advancement in designing wearable neural interfaces for controlling neurorobotic systems, particularly bionic limbs. These interfaces function by decoding signals captured non-invasively from the skin's surface. Portable high-density surface electromyography (HD-sEMG) modules combined with deep learning decoding have attracted interest by achieving excellent gesture prediction and myoelectric control of prosthetic systems and neurorobots. However, factors like pixel-shape electrode size and unstable skin contact make HD-sEMG susceptible to pixel electrode drops. The sparse electrode-skin disconnections rooted in issues such as low adhesion, sweating, hair blockage, and skin stretch challenge the reliability and scalability of these modules as the perception unit for neurorobotic systems. This paper proposes a novel deep-learning model providing resiliency for HD-sEMG modules, which can be used in the wearable interfaces of neurorobots. The proposed 3D Dilated Efficient CapsNet model trains on an augmented input space to computationally `force' the network to learn channel dropout variations and thus learn robustness to channel dropout. The proposed framework maintained high performance under a sensor dropout reliability study conducted. Results show conventional models' performance significantly degrades with dropout and is recovered using the proposed architecture and the training paradigm.

翻译：过去十年中，用于控制神经机器人系统（尤其是仿生肢体）的可穿戴神经接口设计取得了显著进展。这些接口通过解码从皮肤表面非侵入式捕获的信号发挥作用。便携式高密度表面肌电图（HD-sEMG）模块与深度学习解码相结合，通过实现出色的手势预测和假肢系统及神经机器人的肌电控制而备受关注。然而，像素状电极尺寸和不稳定的皮肤接触等因素使得HD-sEMG容易受到像素电极脱落的影响。由于低附着力、出汗、毛发阻塞和皮肤拉伸等问题导致的稀疏电极-皮肤断连，挑战了这些模块作为神经机器人系统感知单元的可靠性和可扩展性。本文提出了一种新颖的深度学习模型，为HD-sEMG模块提供弹性，可应用于神经机器人的可穿戴接口。所提出的3D扩张高效CapsNet模型在增强输入空间上进行训练，以计算方式“强制”网络学习通道丢失变化，从而学习对通道丢失的鲁棒性。在所进行的传感器丢失可靠性研究中，所提出的框架保持了高性能。结果表明，传统模型的性能在丢失情况下显著下降，而利用所提出的架构和训练范式可恢复该性能。