IEEE Trans Biomed Eng. 2022 Dec;69(12):3825-3835. doi: 10.1109/TBME.2022.3182588. Epub 2022 Nov 21.
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.
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.
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).
Experimental results demonstrate the superiority of DyEnsemble in online BMI control.
DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.
脑机接口（BMI）旨在提供对假肢和计算机光标等设备的直接大脑控制，这些设备在运动恢复方面具有巨大的潜力。当前 BMI 的一个主要局限性在于由于神经信号的可变性，尤其是在在线控制中，性能不稳定，这严重阻碍了 BMI 的临床可用性。
我们提出了一种动态集成贝叶斯滤波器（DyEnsemble）来处理在线 BMI 控制中的神经可变性。与大多数使用固定模型的现有方法不同，DyEnsemble 学习了一组模型，这些模型具有描述神经功能的不同能力。在每个时间槽中，它根据神经信号在贝叶斯框架中动态加权和组合模型。通过这种方式，DyEnsemble 应对信号的可变性并提高在线控制的鲁棒性。
对人类参与者的在线 BMI 实验表明，与速度卡尔曼滤波器相比，DyEnsemble 显著提高了控制精度（在随机目标跟踪任务中成功率提高了 13.9%）和鲁棒性（在不同的实验日表现更稳定）。
实验结果证明了 DyEnsemble 在在线 BMI 控制中的优越性。
DyEnsemble 为稳健的 BMI 构建了一种新颖而灵活的动态解码框架，有利于各种神经解码应用。
