Bernstein Center for Computational Neuroscience Göttingen, Germany.
Int J Neural Syst. 2011 Feb;21(1):65-78. doi: 10.1142/S0129065711002651.
We introduce an approach to compensate for temporal distortions of repeated measurements in event-related potential research. The algorithm uses a combination of methods from nonlinear time-series analysis and is based on the construction of pairwise registration functions from cross-recurrence plots of the phase-space representations of ERP signals. The globally optimal multiple-alignment path is approximated by hierarchical cluster analysis, i.e. by iteratively combining pairs of trials according to similarity. By the inclusion of context information in form of externally acquired time markers (e.g. reaction time) into a regularization scheme, the extracted warping functions can be guided near paths that are implied by the experimental procedure. All parameters occurring in the algorithm can be optimized based on the properties of the data and there is a broad regime of parameter configurations where the algorithm produces good results. Simulations on artificial data and the analysis of ERPs from a psychophysical study demonstrate the robustness and applicability of the algorithm.
我们介绍了一种方法来补偿事件相关电位研究中重复测量的时间扭曲。该算法结合了非线性时间序列分析的方法，基于从 ERP 信号的相空间表示的交叉递归图构建成对的配准函数。全局最优的多对齐路径通过层次聚类分析来近似，即根据相似性迭代地组合对试验。通过将外部获取的时间标记（例如反应时间）的上下文信息纳入正则化方案中，可以引导提取的变形函数接近实验过程所暗示的路径。算法中出现的所有参数都可以根据数据的特性进行优化，并且在算法产生良好结果的参数配置中有一个广泛的范围。人工数据的模拟和心理物理研究中 ERPs 的分析证明了该算法的稳健性和适用性。
