  Objective: Brain-machine interfaces (BMIs) aim to provide direct brain
control of devices such as prostheses and computer cursors, which have
demonstrated great potential for mobility restoration. One major limitation of
current BMIs lies in the unstable performance in online control due to the
variability of neural signals, which seriously hinders the clinical
availability of BMIs. Method: To deal with the neural variability in online BMI
control, we propose a dynamic ensemble Bayesian filter (DyEnsemble). DyEnsemble
extends Bayesian filters with a dynamic measurement model, which adjusts its
parameters in time adaptively with neural changes. This is achieved by learning
a pool of candidate functions and dynamically weighting and assembling them
according to neural signals. In this way, DyEnsemble copes with variability in
signals and improves the robustness of online control. Results: Online BMI
experiments with a human participant demonstrate that, compared with the
velocity Kalman filter, DyEnsemble significantly improves the control accuracy
(increases the success rate by 13.9% and reduces the reach time by 13.5% in the
random target pursuit task) and robustness (performs more stably over different
experiment days). Conclusion: Our results demonstrate the superiority of
DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and
flexible framework for robust neural decoding, which is beneficial to different
neural decoding applications.
