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.

提出了一种动态集成贝叶斯滤波器（DyEnsemble），以应对脑机接口（BMI）在线控制中的神经变异，并通过一个人类参与者的 BMI 实验表明，相比于速度卡尔曼滤波器，DyEnsemble 显著提高了控制精度（在随机目标追踪任务中增加了 13.9％的成功率并减少了 13.5％的到达时间），并表现出更强的鲁棒性。

人脑 - 机器接口稳健控制的动态集成贝叶斯滤波器

Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface

Randomly connected networks of excitatory and inhibitory spiking neurons
provide a parsimonious model of neural variability, but are notoriously
unreliable for performing computations. We show that this difficulty is
overcome by incorporating the well-documented dependence of connection
probability on distance. Spatially extended spiking networks exhibit
symmetry-breaking bifurcations and generate spatiotemporal patterns that can be
trained to perform dynamical computations.

本研究发现通过距离依赖性连接概率，可以克服兴奋性和抑制性尖峰神经元随机连接网络在进行计算时的不可靠性。在此基础上建立的空间扩展尖峰神经元网络可以展现出对称性破缺分岔现象，并能生成可进行动力学计算的时空模式。