Systems of equations are often derived to model fundamental behaviors of physical processes, such as a swinging pendulum.  For many applications in the life sciences, however, such equations are incomplete or unknown entirely, and we rely on system measurements to reveal underlying dynamic features.  In this dissertation, we apply techniques from data-driven dynamical systems to intracranial recordings from human subjects performing a perceptual bistability task and identify neural features correlated with the subjects’ reported perception.