Order patterns recurrence plots in the analysis of ERP data
Order patterns recurrence plots in the analysis of ERP data
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DOI：
10.1007/s11571-007-9023-z
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发表时间：
2007-12-01
影响因子：
3.7
通讯作者：
Kurths, Juergen
中科院分区：
文献类型：
--
作者：
Schinkel, Stefan;Marwan, Norbert;Kurths, Juergen
递归量化分析（RQA）是一种成熟的行为科学数据分析工具。在本文中，我们提出了基于订单模式的RQA的改进概念。顺序模式的使用在时间序列分析中很常见。将这一概念与递归图（RP）及其量化（RQA）相结合，可以促进当代脑电图研究的进步，特别是在事件相关电位（ERP）的分析方面，因为该方法已知对非平稳数据具有鲁棒性。在语言处理实验中记录的脑电数据中使用顺序模式递归图（oprp）证明了该方法的潜力。我们可以证明，RQA对ERP数据的应用可以大大减少ERP研究所需的试验数量，同时仍然保持统计效度。
Recurrence quantification analysis (RQA) is an established tool for data analysis in various behavioural sciences. In this article we present a refined notion of RQA based on order patterns. The use of order patterns is commonplace in time series analysis. Exploiting this concept in combination with recurrence plots (RP) and their quantification (RQA) allows for advances in contemporary EEG research, specifically in the analysis of event related potentials (ERP), as the method is known to be robust against non-stationary data. The use of order patterns recurrence plots (OPRPs) on EEG data recorded during a language processing experiment exemplifies the potentials of the method. We could show that the application of RQA to ERP data allows for a considerable reduction of the number of trials required in ERP research while still maintaining statistical validity.