Abstract: Disclosed herein is a system and method for training a subject to self-regulate neural variability. The system and method implements a prefrontal cortex brain-computer interface (BCI) and a method that allows subjects to use neurofeedback to produce a desired neural activity by regulation of their arousal levels to stabilize their neural activity across a timescale of seconds or minutes. The system comprises a prefrontal cortex brain-computer interface (BCI) to train subjects to use neurofeedback to produce desired neural activity. Subjects used the disclosed system and method, which includes BCI feedback, to self-regulate their arousal levels and successfully stabilize their neural activity.

Abstract: A brain machine interface for decoding neural signals for control of a machine is provided. The brain machine interface estimates and then combines information from two classes of neural activity. A first estimator decodes movement plan information from neural signals representing plan activity. In one embodiment the first estimator includes an adaptive point-process filter or a maximum likelihood filter. A second estimator decodes peri-movement information from neural signals representing peri-movement activity. Each estimator is designed to estimate different aspects of movement such as movement goal variables or movement execution variables.