Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity
Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity
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DOI：
10.1152/jn.00684.2017
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发表时间：
2018-04-01
影响因子：
2.5
通讯作者：
Chen, Zhe
中科院分区：
文献类型：
--
作者：
Hu, Sile;Zhang, Qiaosheng;Chen, Zhe
从时间序列数据中进行顺序变化点检测是许多神经科学应用中的常见问题，例如癫痫检测、异常检测和疼痛检测。在我们之前的工作中（Chen Z，Zhang Q，Tong AP，Manders TR，Wang J. J Neural Eng 14：036023，2017），我们开发了一种潜在状态空间模型，称为泊松线性动力系统，用于检测神经元集合尖峰活动的突然变化。在在线脑机接口（BMI）应用中，使用递归过滤算法来跟踪潜在变量的变化。然而，以前的方法仅限于高斯动态噪声，并且使用高斯近似来计算泊松似然。为了提高检测速度，我们引入非高斯动态噪声来建模潜在状态空间中的随机跳跃过程。为了有效地估计适应非高斯噪声和非高斯似然的状态后验，我们提出了针对变点检测问题的粒子滤波和平滑算法。为了加快计算速度，我们使用先进的图形处理单元计算技术来实现所提出的粒子滤波算法。我们使用计算机模拟和急性疼痛检测的实验数据来验证我们的算法。最后，我们讨论了实时闭环 BMI 应用中的几个重要的实际问题。新的和值得注意的顺序变化点检测是闭环神经科学实验中的一个重要问题。这项研究提出了新颖的顺序蒙特卡罗方法，可以快速检测驱动种群峰值活动的随机跳跃过程的开始和偏移。这种新方法对于尖峰排序噪声和不同水平的信噪比具有鲁棒性。计算算法的 GPU 实现允许实时并行处理。
Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications.NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.