Ahmadi Nur, Constandinou Timothy G, Bouganis Christos-Savvas
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2547-2550. doi: 10.1109/EMBC.2018.8512830.
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.
脑机接口（BMI）大多将脉冲发放率用作输入特征来解码期望的运动输出，因为它传达了关于潜在神经元活动的有用度量。脉冲发放率通常通过使用不重叠分箱方法来估计，该方法会产生一个粗略的估计值。存在几种能够产生平滑估计值的方法，这可能会潜在地提高解码性能。然而，对于实时BMI而言，这些方法的计算量相对较大。为了解决这个问题，我们提出了一种新的估计脉冲发放率的方法，该方法能够产生平滑估计值，并且适用于实时BMI。所提出的方法称为贝叶斯自适应核平滑器（BAKS），它采用核平滑技术，将带宽视为具有先验分布的随机变量，并通过贝叶斯框架进行自适应更新。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，并使用卡尔曼滤波器评估其对离线BMI解码性能的影响。结果表明，与分箱方法相比，BAKS可以提高解码性能。这表明BAKS用于实时BMI的可行性和潜在用途。
