Optimizing principal components analysis of event-related potentials: Matrix type, factor loading weighting, extraction, and rotations
Optimizing principal components analysis of event-related potentials: Matrix type, factor loading weighting, extraction, and rotations
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DOI：
10.1016/j.clinph.2004.11.025
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发表时间：
2005-08-01
影响因子：
4.7
通讯作者：
Berg, P
中科院分区：
文献类型：
--
作者：
Dien, J;Beal, DJ;Berg, P
目的：鉴于在文献中相互矛盾的建议，本报告旨在提出一个标准的协议，应用主成分分析（PCA）事件相关电位（ERP）datasets.Methods：协方差与相关矩阵的影响，凯撒归一化与协方差加载，截断与无限制的解决方案，方差与Promax旋转进行了测试100模拟数据集。此外，这些参数的影响是否介导的组件size.Results：参数进行了评估，根据时间过程重建，源定位的结果，和分配不当的方差分析的影响。相关矩阵导致显著的方差分配错误。Promax旋转比Varimax旋转产生更准确的结果。协方差加载是劣于Kaiser Normalization和unweighted loadings.Conclusions：基于目前的模拟两个组件，证据支持使用协方差矩阵，Kaiser归一化，和Promax旋转。当使用这些参数时，无限制的解决方案并没有实质性地改善结果。我们反对使用它们。结果还表明，优化的PCA程序可以显着提高源localization results.Significance：PCA程序的持续发展可以提高结果时，PCA应用于ERP数据集。(c)2005年国际临床神经生理学联合会。由Elsevier爱尔兰有限公司出版。保留所有权利。
Objective: Given conflicting recommendations in the literature, this report seeks to present a standard protocol for applying principal components analysis (PCA) to event-related potential (ERP) datasets.Methods: The effects of a covariance versus a correlation matrix, Kaiser normalization vs. covariance loadings, truncated versus unrestricted solutions, and Varimax versus Promax rotations were tested on 100 simulation datasets. Also, whether the effects of these parameters are mediated by component size was examined.Results: Parameters were evaluated according to time course reconstruction, source localization results, and misallocation of ANOVA effects. Correlation matrices resulted in dramatic misallocation of variance. The Promax rotation yielded much more accurate results than Varimax rotation. Covariance loadings were inferior to Kaiser Normalization and unweighted loadings.Conclusions: Based on the current simulation of two components, the evidence supports the use of a covariance matrix, Kaiser normalization, and Promax rotation. When these parameters are used, unrestricted solutions did not materially improve the results. We argue against their use. Results also suggest that optimized PCA procedures can measurably improve source localization results.Significance: Continued development of PCA procedures can improve the results when PCA is applied to ERP datasets. (c) 2005 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.