title: Advances in neuroprosthetic learning and control.
abstract: Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.
authors.affiliation: Department of Electrical Engineering and Computer Sciences, Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America. carmena@eecs.berkeley.edu
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D000882:Haplorhini; D006801:Humans; D058117:Neural Prostheses; D012371:Robotics; D014584:User-Computer Interface