  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.
