Optimizing PCA methodology for ERP component identification and measurement: theoretical rationale and empirical evaluation
Optimizing PCA methodology for ERP component identification and measurement: theoretical rationale and empirical evaluation
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DOI：
10.1016/s1388-2457(03)00241-4
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发表时间：
2003-12-01
影响因子：
4.7
通讯作者：
Tenke, CE
中科院分区：
文献类型：
--
作者：
Kayser, J;Tenke, CE
目的：确定具体的方法选择如何影响“数据驱动”的简化事件相关电位（ERP）使用主成分分析（PCA）。提取的分量测量的有用性可以通过关于ERP的方差分布的知识来评估，其特征在于基线活动的去除。在刺激开始之前和刺激开始时（跨病例和病例内），方差应该很小，但在记录时期结束时和ERP分量峰值处较大。这些特征在协方差矩阵中得以保留，但在相关性矩阵中丢失，相关性矩阵为每个样本点分配相等的权重，从而产生小但系统性的变化可能形成因子的可能性。在模拟和真实的ERP上进行方差最大旋转PCA，系统地改变提取标准（系数数目）和方法（相关/协方差矩阵，使用旋转前的非标准化/标准化载荷）。保守的提取标准改变了一些组件的形态相当大，这有严重的影响，统计推断。解决办法趋于一致，并以更自由的标准稳定下来。对于非标准化的基于协方差的解决方案，可解释性（具有窄且明确的加载峰的更独特的分量波形）和统计结论（跨提取标准的更大的效应稳定性）最佳。相比之下，所有标准化的协方差和相关性为基础的解决方案，包括“高方差”的因素在基线，确认结果为模拟data.Conclusions：不受限制的，非标准化的协方差为基础的PCA解决方案优化ERP组件识别和测量。(C)2003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: To determine how specific methodological choices affect "data-driven" simplifications of event-related potentials (ERPs) using principal components analysis (PCA). The usefulness of the extracted component measures can be evaluated by knowledge about the variance distribution of ERPs, which are characterized by the removal of baseline activity. The variance should be small before and at stimulus onset (across and within cases), but large near the end of the recording epoch and at ERP component peaks. These characteristics are preserved with a covariance matrix, but lost with a correlation matrix, which assigns equal weights to each sample point, yielding the possibility that small but systematic variations may form a factor.Methods: Varimax-rotated PCAs were performed on simulated and real ERPs, systematically varying extraction criteria (number of factors) and method (correlation/covariance matrix, using unstandardized/standardized loadings before rotation).Results: Conservative extraction criteria changed the morphology of some components considerably, which had severe implications for inferential statistics. Solutions converged and stabilized with more liberal criteria. Interpretability (more distinctive component waveforms with narrow and unambiguous loading peaks) and statistical conclusions (greater effect stability across extraction criteria) were best for unstandardized covariance-based solutions. In contrast, all standardized covariance- and correlation-based solutions included "high-variance" factors during the baseline, confirming findings for simulated data.Conclusions: Unrestricted, unstandardized covariance-based PCA solutions optimize ERP component identification and measurement. (C) 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.