Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface.
IEEE Trans Biomed Eng
; 69(12): 3825-3835, 2022 12.
Article
en En
| MEDLINE
| ID: mdl-35700258
ABSTRACT
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 motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs.METHOD:
We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. 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% in the random target pursuit task) and robustness (performs more stably over different experiment days).CONCLUSION:
Experimental results demonstrate the superiority of DyEnsemble in online BMI control.SIGNIFICANCE:
DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Miembros Artificiales
/
Interfaces Cerebro-Computador
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Biomed Eng
Año:
2022
Tipo del documento:
Article