A person with tetraplegia was able to control a 7-degree-of-freedom (7D) robotic arm using a brain-machine interface (BMI). The study involved implanting two 96-channel intracortical microelectrodes in the motor cortex of a participant with tetraplegia. Over 13 weeks of training, the participant demonstrated the ability to control the robotic arm to perform complex movements, including reaching, grasping, and manipulating objects. The participant showed significant improvements in performance on target-based reaching tasks, with success rates increasing over time. The participant was able to perform skillful and coordinated movements that resulted in clinically significant gains in tests of upper-limb function. The BMI system used a neural decoder that translated neural activity into control signals for the robotic arm. The system was calibrated using a two-phase calibration process, with the participant controlling the robotic arm during the second phase. The participant was able to perform tasks with full brain control, achieving an average success rate of 91.6% after 13 weeks of training. The participant's performance was measured using clinical assessments, including the Action Research Arm Test (ARAT), which evaluated upper-limb function. The participant's ARAT score improved from 0 to 15–17 over the course of the study, demonstrating significant functional gains. The study demonstrated that a person with chronic tetraplegia can perform consistent, natural, and complex movements with an anthropomorphic robotic arm to regain clinically significant function. The results highlight the potential of BMI technology to restore upper-limb function in individuals with paralysis. The study was funded by the Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. The findings suggest that BMI technology has the potential to provide significant functional benefits for individuals with tetraplegia or upper-limb amputation.A person with tetraplegia was able to control a 7-degree-of-freedom (7D) robotic arm using a brain-machine interface (BMI). The study involved implanting two 96-channel intracortical microelectrodes in the motor cortex of a participant with tetraplegia. Over 13 weeks of training, the participant demonstrated the ability to control the robotic arm to perform complex movements, including reaching, grasping, and manipulating objects. The participant showed significant improvements in performance on target-based reaching tasks, with success rates increasing over time. The participant was able to perform skillful and coordinated movements that resulted in clinically significant gains in tests of upper-limb function. The BMI system used a neural decoder that translated neural activity into control signals for the robotic arm. The system was calibrated using a two-phase calibration process, with the participant controlling the robotic arm during the second phase. The participant was able to perform tasks with full brain control, achieving an average success rate of 91.6% after 13 weeks of training. The participant's performance was measured using clinical assessments, including the Action Research Arm Test (ARAT), which evaluated upper-limb function. The participant's ARAT score improved from 0 to 15–17 over the course of the study, demonstrating significant functional gains. The study demonstrated that a person with chronic tetraplegia can perform consistent, natural, and complex movements with an anthropomorphic robotic arm to regain clinically significant function. The results highlight the potential of BMI technology to restore upper-limb function in individuals with paralysis. The study was funded by the Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute. The findings suggest that BMI technology has the potential to provide significant functional benefits for individuals with tetraplegia or upper-limb amputation.