Beauducel André, Debener Stefan
Department of Psychology II, University of Mannheim, Schloss, Ehrenhof Ost, Germany.
J Neurosci Methods. 2003 Mar 30;124(1):103-12. doi: 10.1016/s0165-0270(02)00381-3.
Since Wood and McCarthy's simulation study (Electroenceph Clin Neurophysiol 1984;59:249-260), the use of principal component analysis (PCA) as a tool for the identification and quantification of event-related potentials (ERP) has been considered a challenge. Three relevant aspects have not been fully acknowledged in previous studies, however, and were therefore investigated in the present simulation study. Firstly, the impact of test power on the amount of variance misallocation was studied. Secondly, the impact of ERP component topography on variance misallocation was investigated. Thirdly, a systematic evaluation of variance misallocation in baseline-to-peak derived ERP measures was performed. Results based on an overall set of 2700 simulations indicate that: (a) variance misallocation is reduced to an almost acceptable level when an appropriate test power is simulated; (b) the overall amount of variance misallocation remains at an almost acceptable level when systematic topographic effects are simulated in combination with an appropriate test power; and (c) variance misallocation is in fact also a problem in baseline-to-peak measures. These findings confirm that, when used appropriately, PCA is a helpful and efficient tool for the identification and quantification of ERPs.
自伍德和麦卡锡的模拟研究（《脑电图与临床神经生理学》，1984年；59:249 - 260）以来，使用主成分分析（PCA）作为识别和量化事件相关电位（ERP）的工具一直被视为一项挑战。然而，之前的研究尚未充分认识到三个相关方面，因此在本模拟研究中对其进行了调查。首先，研究了检验效能对方差错配量的影响。其次，研究了ERP成分地形图对方差错配的影响。第三，对从基线到峰值的ERP测量中的方差错配进行了系统评估。基于总共2700次模拟的结果表明：（a）当模拟适当的检验效能时，方差错配会降低到几乎可接受的水平；（b）当结合适当的检验效能模拟系统地形效应时，方差错配的总量仍处于几乎可接受的水平；（c）实际上，方差错配在从基线到峰值的测量中也是一个问题。这些发现证实，当合理使用时，PCA是识别和量化ERP的一种有用且有效的工具。
