Dien Joseph
Center for Advanced Study of Language, University of Maryland, College Park, Maryland 20742-0025, USA.
Dev Neuropsychol. 2012;37(6):497-517. doi: 10.1080/87565641.2012.697503.
Principal components analysis (PCA) has attracted increasing interest as a tool for facilitating analysis of high-density event-related potential (ERP) data. While every researcher is exposed to this statistical procedure in graduate school, its complexities are rarely covered in depth and hence researchers are often not conversant with its subtleties. Furthermore, application to ERP datasets involves unique aspects that would not be covered in a general statistics course. This tutorial seeks to provide guidance on the decisions involved in applying PCA to ERPs and their consequences, using the ERP PCA Toolkit to illustrate the analysis process on a novelty oddball dataset.
主成分分析（PCA）作为一种有助于分析高密度事件相关电位（ERP）数据的工具，已引起越来越多的关注。虽然每位研究人员在研究生阶段都会接触到这种统计方法，但其复杂性很少被深入探讨，因此研究人员往往并不熟悉其细微之处。此外，将其应用于ERP数据集涉及一些一般统计学课程中不会涵盖的独特方面。本教程旨在就将PCA应用于ERP时所涉及的决策及其后果提供指导，并使用ERP PCA工具包来说明对一个新奇Oddball数据集的分析过程。
