Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

ABSTRACT: Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to

SUBMITTER: Pohlmeyer EA 

PROVIDER: S-EPMC3907465 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature