Robust neural decoding for dexterous control of robotic hand kinematics

•Deep learning-based mapping of high-density electromyogram features to neural-drive signals.•Efficient and robust neural decoding approaches for real-time control of a prosthetic hand.•Consistently accurate prediction of joint angles with minimal errors.•Stable performance over time and robust to variations in electromyogram signals.