Muscle activity is widely measured to assess muscle condition in post-stroke patients. While many clinical researchers have relied on time-series analysis of muscle activity, the frequency domain could offer additional insight on motor impairment. Our previous work has characterized movement capabilities in stroke survivors across endpoint and joint kinematic variables while performing a self-directed motor exploration task. Our solution to managing such large volumes of data is to create personalized statistical profiles using multivariate probability distributions. In this study, we present frequency domain analysis of EMG distributions for chronic post-stroke survivors (N = 6) and healthy subjects (N = 5) to identify between group differences in muscle activity. Comparing probability density of muscle activity magnitudes, differences from healthy were most evident at 275 Hz. Unique aspects of each patient's deficits were most evident at 125 Hz. This is the first study to explore distributions of EMG in specific frequency bands for this patient population. Such identifiability could pinpoint specific motor deficits and track progress in neurologically impaired individuals that often have widely differing disease states.