AUTONOMOUS BRAIN-MACHINE INTERFACE

摘要

A reinforcement learning brain-machine interface (RL-BMI) can have a policy that governs how detected signals, emanating from a motor cortex of a subject's brain, are translated into action. The policy can be improved by detecting a motor signal having a characteristic and emanating from the motor cortex. The system can provide, to a device and based on (i) the motor signal and (ii) an instruction policy, a command signal resulting in a first action by a device. Additionally, an evaluation signal, emanating from the motor cortex in response to the first action, can also be detected. With the foregoing information, the system can adjust the policy based on the evaluation signal such that a subsequent motor signal, from the subject's brain, having the characteristic results in a second action, by the device, different from the first action, as needed.