Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces.
Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces.
复制标题
使用贝叶斯自适应核平滑器 (BAKS) 的尖峰率估计及其在脑机接口中的应用。
DOI：
10.1109/embc.2018.8512830
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ahmadi N
中科院分区：
文献类型：
--
作者：
Ahmadi N
脑机接口（Brain Machine Interface，BMI）主要利用尖峰速率作为输入特征来解码期望的运动输出，因为它传达了对潜在神经元活动的有用测量。尖峰速率通常通过使用产生粗略估计的非重叠分箱方法来估计。存在几种可以产生平滑估计的方法，其可以潜在地提高解码性能。然而，这些方法对于实时BMI来说计算量相对较大。为了解决这个问题，我们提出了一种新的方法来估计尖峰率，能够产生一个平滑的估计，也适合于实时BMI。所提出的方法，称为贝叶斯自适应核平滑器（BAKS），采用核平滑技术，认为带宽作为一个随机变量与先验分布是自适应更新，通过贝叶斯框架。通过适当选择先验分布和核函数，可以得到核带宽的解析表达式。我们应用BAKS和评估其影响离线BMI解码性能，使用卡尔曼滤波器。结果表明，BAKS可以提高解码性能相比，合并方法。这表明了BAKS用于实时BMI的可行性和潜在用途。
Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs. The proposed method, referred to as Bayesian adaptive kernel smoother (BAKS), employs kernel smoothing technique that considers the bandwidth as a random variable with prior distribution which is adaptively updated through a Bayesian framework. With appropriate selection of prior distribution and kernel function, an analytical expression can be achieved for the kernel bandwidth. We apply BAKS and evaluate its impact on offline BMI decoding performance using Kalman filter. The results reveal that BAKS can improve the decoding performance compared to the binning method. This suggests the feasibility and the potential use of BAKS for real-time BMIs.