  Myoelectric control is one of the leading areas of research in the field of
robotic prosthetics. We present our research in surface electromyography (sEMG)
signal classification, where our simple and novel attention-based approach now
leads the industry, universally beating more complex, state-of-the-art models.
Our novel attention-based model achieves benchmark leading results on multiple
industry-standard datasets including 53 finger, wrist, and grasping motions,
improving over both sophisticated signal processing and CNN-based approaches.
Our strong results with a straightforward model also indicate that sEMG
represents a promising avenue for future machine learning research, with
applications not only in prosthetics, but also in other important areas, such
as diagnosis and prognostication of neurodegenerative diseases, computationally
mediated surgeries, and advanced robotic control. We reinforce this suggestion
with extensive ablative studies, demonstrating that a neural network can easily
extract higher order spatiotemporal features from noisy sEMG data collected by
affordable, consumer-grade sensors.
