Center for Birth Defects, University of Louisville, Louisville, Kentucky, USA.
Psychophysiology. 2010 Jan 1;47(1):170-83. doi: 10.1111/j.1469-8986.2009.00885.x. Epub 2009 Sep 15.
Principal components analysis (PCA) can facilitate analysis of event-related potential (ERP) components. Geomin, Oblimin, Varimax, Promax, and Infomax (independent components analysis) were compared using a simulated data set. Kappa settings for Oblimin and Promax were also systematically compared. Finally, the rotations were also analyzed in a two-step PCA procedure, including a contrast between spatiotemporal and temporospatial procedures. Promax was found to give the best overall results for temporal PCA, and Infomax was found to give the best overall results for spatial PCA. The current practice of kappa values of 3 or 4 for Promax and 0 for Oblimin was supported. Source analysis was meaningfully improved by temporal Promax PCA over the conventional windowed difference wave approach (from a median 32.9 mm error to 6.7 mm). It was also found that temporospatial PCA produced modestly improved results over spatiotemporal PCA.
主成分分析（PCA）可以促进事件相关电位（ERP）成分的分析。使用模拟数据集比较了 Geomin、Oblimin、Varimax、Promax 和 Infomax（独立成分分析）。还系统地比较了 Oblimin 和 Promax 的 Kappa 设置。最后，还在两步 PCA 过程中分析了旋转，包括时空和时-空程序之间的对比。发现 Promax 对于时间 PCA 的总体结果最好，而 Infomax 对于空间 PCA 的总体结果最好。支持当前 Promax 的 Kappa 值为 3 或 4，Oblimin 的 Kappa 值为 0 的做法。与传统的窗口差波方法（从中位数 32.9mm 误差到 6.7mm）相比，时间 Promax PCA 显著提高了源分析的准确性。还发现时-空 PCA 比时空 PCA 产生了适度的改进结果。
Zotero 插件