Department of Statistics, University of California Davis, MSB 4118 One Shields Avenue, Davis, CA 95616, USA.
Comput Intell Neurosci. 2010;2010. doi: 10.1155/2010/946089. Epub 2010 Jul 29.
An important goal in neuroscience is to identify instances when EEG signals are coupled. We employ a method to measure the coupling strength between gamma signals (40-100 Hz) on a short time scale as the maximum cross-correlation over a range of time lags within a sliding variable-width window. Instances of coupling states among several signals are also identified, using a mixed multivariate beta distribution to model coupling strength across multiple gamma signals with reference to a common base signal. We first apply our variable-window method to simulated signals and compare its performance to a fixed-window approach. We then focus on gamma signals recorded in two regions of the rat hippocampus. Our results indicate that this may be a useful method for mapping coupling patterns among signals in EEG datasets.
神经科学的一个重要目标是确定 EEG 信号何时耦合。我们采用一种方法来测量短时间尺度上伽马信号（40-100 Hz）之间的耦合强度，方法是在滑动可变宽度窗口内的时间滞后范围内对最大互相关进行测量。使用混合多元贝塔分布来识别多个信号之间的耦合状态，该分布参考共同的基准信号来对多个伽马信号的耦合强度进行建模。我们首先将我们的变窗方法应用于模拟信号，并将其性能与固定窗方法进行比较。然后，我们专注于在大鼠海马体的两个区域记录的伽马信号。我们的结果表明，这可能是一种在 EEG 数据集的信号中绘制耦合模式的有用方法。
