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解决事件相关电位主成分分析中方差的错误分配问题。

Addressing misallocation of variance in principal components analysis of event-related potentials.

作者信息

Dien J

机构信息

Center for Neuroscience, University of California, Davis 95616, USA.

出版信息

Brain Topogr. 1998 Fall;11(1):43-55. doi: 10.1023/a:1022218503558.

Abstract

Interpretation of evoked response potentials is complicated by the extensive superposition of multiple electrical events. The most common approach to disentangling these features is principal components analysis (PCA). Critics have demonstrated a number of caveats that complicate interpretation, notably misallocation of variance and latency jitter. This paper describes some further caveats to PCA as well as using simulations to evaluate three potential methods for addressing them: parallel analysis, oblique rotations, and spatial PCA. An improved simulation model is introduced for examining these issues. It is concluded that PCA is an essential statistical tool for event-related potential analysis, but only if applied appropriately.

摘要

诱发反应电位的解释因多种电事件的广泛叠加而变得复杂。解开这些特征的最常见方法是主成分分析(PCA)。批评者已经证明了一些使解释复杂化的注意事项,特别是方差的错误分配和潜伏期抖动。本文描述了PCA的一些进一步注意事项,并使用模拟来评估三种潜在的解决方法:平行分析、斜交旋转和空间PCA。引入了一个改进的模拟模型来研究这些问题。得出的结论是,PCA是事件相关电位分析的重要统计工具,但前提是要适当应用。

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