Tracking functional changes in nonstationary signals with evolutionary ensemble bayesian model for robust neural decoding
NeurIPS | A novel evolutionary ensemble framework, EvoEnsemble, dynamically adapts to changes in neural signals by evolving decoders over time, significantly improving the accuracy and robustness of neural decoding in nonstationary conditions.
Dec 6, 2022
Dynamic ensemble bayesian filter for robust control of a human brain-machine interface
IEEE Trans on Biomedical Engineering | A dynamic ensemble Bayesian filter (DyEnsemble) is proposed to improve the robustness of online brain-machine interface (BMI) control by adaptively combining multiple neural decoding models to handle neural signal variability, showing significantly better accuracy and stability than conventional methods.
Jun 13, 2022