Abstract: For intracortical brain-computer interface (BCI) controlled neuroprosthetic arms to become a valuable assistive technology for people with upper-limb paralysis they will need to be able to adjust to a number of changes in neural activity that have not previously been well characterized. I quantified the rate at which recorded units become unstable within and between days to inform the design of self-recalibrating decoders. Using the quantification of stability, I then examined whether unit characteristics could predict how long a unit would be stable. I also addressed difficulties with using the hand to interact with objects. Finally, I studied the representation of desired grasp force in primary motor cortex to enable BCI users to grasp a variety of objects, from light, fragile objects to heavy, sturdy objects. All of these studies improve the reliability and utility of BCI controlled neuroprosthetic arms, with the goal of creating technology for people with upper-limb paralysis to use in their daily lives.