PROJECT SUMMARY/ABSTRACT Simple tasks such as using a computer, feeding oneself, personal hygiene, and grabbing objects are impossible for high level tetraplegics without assistance. Remarkable advances in brain-machine interfaces (BMIs) in the past 20 years, however, have demonstrated that paralyzed individuals can exert good control over assistive robotic devices derived from neural recordings using electrodes implanted in the brain. Despite the promise of such BMIs, there nevertheless remain significant shortcomings, including 1) the duration over which neural recordings remain viable is limited to a couple of years, and 2) the procedure requires invasive surgery associated with substantial risks and costs. Most individuals with high level paralysis, however, retain the ability to voluntarily move their head and tongue, activate facial muscles, and can speak. It seems reasonable to hypothesize, therefore, that signals derived from these actions could be used to control movements of a robotic arm accurately and intuitively. The main goal of this project, therefore, is to evaluate the utility of non-invasive methods to supply the inputs needed to control movements of a robotic limb to perform a variety of tasks. Toward this goal, we will carry out three specific aims: 1) evaluate control types (position and velocity) and input modalities (head, face/head EMG, tongue, voice) for regulating robotic arm position, 2) assess various methods to control robot arm grasping, 3) characterize improvements in robotic arm control performing standardized real-world tasks with practice. Importantly, data collected here using non- invasive methods during standardized tasks will provide a crucial benchmark needed for evaluation of, and justification for using BMIs developed in the future. Moreover, this project will provide a major advance toward the development of non-invasive and readily controlled assistive robotic arms that could greatly increase the independence and well-being of individuals stricken with high-level paralysis.