KalmMind: A Configurable Kalman Filter Design Framework for Embedded Brain-Computer Interfaces

Kalman Filter (KF) is one of the most prominent algorithms to predict motion from measurements of brain activity. However, little effort has been made to optimize the KF for deployment in embedded brain-computer interfaces (BCIs). To address this challenge, we propose a new framework for designing KF hardware accelerators specialized for BCI, which facilitates design-space exploration by providing a tunable balance between latency and accuracy. Through FPGA-based experiments with brain data, we demonstrate improvements in both latency and accuracy compared to the state of the art.