Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. While assistive technology becomes more and more available to the people affected, there is still a big need for user interfaces suitable for people without functional hand movement. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to functionally move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice, to create an EMG-based user interface, and e.g., allow for control of assistive devices. In this paper, we enhance this user interface with additional EMG-features and an improved training paradigm in order to increase information extraction from recordings of strongly atrophic muscles. The interface was tested and validated by subjects with severe spinal muscular atrophy. Results show that the used methods improve the decoding and thereby allow for a considerable increase in performance when controlling a robotic manipulator in a 3D reaching task.