The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl‐fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series.

中文翻译：

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神经科学数据的贝叶斯功能主成分分析中的参数聚类

在过去的几十年中，用于脑部记录的神经科学技术的非凡进步导致了时空数据集的日益复杂。为了减少过度简化，已经开发了新模型，以便能够在高要求的数据环境中识别有意义的模式和新见解。在此程度上，我们提出了一种新的模型，称为参数聚类功能主成分分析（PC1-fPCA），该模型将功能数据分析和贝叶斯非参数方法的思想融合在一起，以获得灵活且在计算上可行的信号重建和时空神经科学数据探索。特别是，我们使用Dirichlet过程高斯混合模型在标准贝叶斯功能PCA框架内对功能主成分评分进行聚类。这种方法捕获了平滑时间序列（曲线）之间的空间相关性结构及其与时域的交互，而无需在数据上施加先验的空间结构。此外，通过将混合数据从数据移动到功能性主成分评分，我们获得了更通用的聚类程序，从而可以对数据进行更高级别的复杂洞察和理解。我们提供了一项仿真研究的结果，与不同的贝叶斯和频频fPCA模型相比，曲线和相关性重构得到了改善，并将我们的方法应用于功能磁共振成像和脑电图数据分析，从而提供了对脑时间序列时空依赖性的丰富探索。