Chen Yonghong, Bressler Steven L, Knuth Kevin H, Truccolo Wilson A, Ding Mingzhou
The J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32611, USA.
Chaos. 2006 Jun;16(2):026113. doi: 10.1063/1.2208455.
In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.
在本文中，我们考虑来自认知实验的神经生物学时间序列的随机建模。我们的出发点是可变信号加持续活动模型。基于该模型，从贝叶斯角度开发了一种差分可变分量分析策略，以在单个试验基础上估计事件相关信号。从记录的单个试验时间序列中减去事件相关信号后，将剩余的持续活动视为分段平稳随机过程，并通过自适应多元自回归建模策略进行分析，该策略可产生功率、相干性和格兰杰因果谱。本文展示了将这些方法应用于执行认知任务的猴子的局部场电位记录的结果。
