Accurate neuroprosthetic control through latent state transition training
Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following understanding of cortical activity during arm reaches, hand BCI studies have focused on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas during natural movement. To explore whether this signal can causally control a prosthesis, we developed a novel BCI training paradigm centered on the reproduction of hand posture transitions. Macaque monkeys trained with the protocol were able to control a multi-dimensional hand prosthesis at high-accuracy, including execution of the very intricate precision grip. Subsequent analysis revealed that the posture signal in the target grasping areas was a major contributor to control. Population activity exhibited pattern separation and dimensionality increases driven by posture kinematics, and simulations with a grasping circuit model demonstrated the generalizability of our approach. We present for the first time neural posture control of a multi-dimensional hand prosthesis, opening the door for future devices to leverage this additional information channel.