Misallocation of variance in event-related potentials: simulation studies on the effects of test power, topography, and baseline-to-peak versus principal component quantifications
Misallocation of variance in event-related potentials: simulation studies on the effects of test power, topography, and baseline-to-peak versus principal component quantifications
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DOI：
10.1016/s0165-0270(02)00381-3
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发表时间：
2003-03-30
影响因子：
3
通讯作者：
Debener, S
中科院分区：
文献类型：
--
作者：
Beauducel, A;Debener, S
自从Wood和McCarthy的模拟研究（Electroencephh Clin Neurophysiol 1984;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）的工具一直被认为是一个挑战。三个相关的方面还没有得到充分承认，在以前的研究中，但是，因此，在本模拟研究进行了调查。首先，研究了检验功效对方差错配量的影响。其次，研究了ERP组件拓扑结构对方差错配的影响。第三，系统地评估了基线到峰值的ERP测量中的方差误分配。基于一组2700次模拟的结果表明：（a）当模拟适当的检验功效时，方差误分配减少到几乎可接受的水平;（B）当模拟系统地形效应并结合适当的检验功效时，方差误分配的总量保持在几乎可接受的水平;以及（c）方差误分配实际上也是基线到峰值测量中的问题。这些研究结果证实，如果使用得当，PCA是一个有用的和有效的工具，用于识别和量化的ERPs。(C)2003 Elsevier Science B. V.保留所有权利。
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. (C) 2003 Elsevier Science B.V. All rights reserved.