Dataset for "Brain-computer interface control with artificial intelligence copilots" Abstract Motor brain-computer interfaces (BCIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BCIs face key obstacles to clinical viability: BCI performance should strongly outweigh BCI costs and risks. We use shared autonomy, where artificial intelligence (AI) copilots collaborate with BCI users to achieve task goals, to significantly increase the performance of BCIs. We demonstrate this "AI-BCI" in a non-invasive BCI system decoding electroencephalography (EEG). We first contribute a hybrid adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), enabling healthy users and a paralyzed participant to autonomously and proficiently control computer cursors and robotic arms with EEG. We then demonstrate AI-BCIs that enable a paralyzed participant to (1) achieve 4.3× higher performance in a cursor control task and (2) control a robotic arm to sequentially move random objects to random locations, a task he could not do without an AI copilot. As AI copilots improve, BCIs designed with shared autonomy may achieve higher performance.