  Brain-computer interfaces (BCIs) have enabled prosthetic device control by
decoding motor movements from neural activities. Neural signals recorded from
cortex exhibit nonstationary property due to abrupt noises and neuroplastic
changes in brain activities during motor control. Current state-of-the-art
neural signal decoders such as Kalman filter assume fixed relationship between
neural activities and motor movements, thus will fail if this assumption is not
satisfied. We propose a dynamic ensemble modeling (DyEnsemble) approach that is
capable of adapting to changes in neural signals by employing a proper
combination of decoding functions. The DyEnsemble method firstly learns a set
of diverse candidate models. Then, it dynamically selects and combines these
models online according to Bayesian updating mechanism. Our method can mitigate
the effect of noises and cope with different task behaviors by automatic model
switching, thus gives more accurate predictions. Experiments with neural data
demonstrate that the DyEnsemble method outperforms Kalman filters remarkably,
and its advantage is more obvious with noisy signals.
