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Injuries of the cervical spinal cord can interrupt the neural pathways controlling the muscles of the arm, resulting in complete or partial paralysis. For individuals unable to reach due to high-level injuries, neuroprostheses can restore some of the lost function. Natural, multidimensional control of neuroprosthetic devices for reaching remains a challenge. Electromyograms (EMGs) from muscles that remain under voluntary control can be used to communicate intended reach trajectories, but when the number of available muscles is limited control can be difficult and unintuitive. We combined shoulder EMGs with target estimates obtained from gaze. Natural gaze data were integrated with EMG during closed-loop robotic control of the arm, using a probabilistic mixture model. We tested the approach with two different sets of EMGs, as might be available to subjects with C4and C5-level spinal cord injuries. Incorporating gaze greatly improved control of reaching, particularly when there were few EMG signals. We found that subjects naturally adapted their eye-movement precision as we varied the set of available EMGs, attaining accurate performance in both tested conditions. The system performs a near-optimal combination of both physiological signals, making control more intuitive and allowing a natural trajectory that reduces the burden on the user.