Multivariate tests for the evaluation of high-dimensional EEG data
Multivariate tests for the evaluation of high-dimensional EEG data
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DOI：
10.1016/j.jneumeth.2004.04.013
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发表时间：
2004-10-15
影响因子：
3
通讯作者：
Weiss, S
中科院分区：
文献类型：
--
作者：
Hemmelmann, C;Horn, M;Weiss, S
本文提出了几种多元检验，特别是排列检验，它们可用于多终点问题，例如比较脑电数据的高维向量。我们已经研究了这些测试的力量，使用模拟和真实的EEG数据中的人工数据。很明显，没有一个多元检验是一致最有效的。不同方法的功效以不同方式取决于端点之间的相关性、存在差异的端点数量以及其他因素。根据我们的研究结果，我们已经得出了关于在哪些配置下应该使用特定测试的经验法则。为了证明不同的多元检验的属性，我们将它们应用于EEG相干数据。作为配对样本情况的一个例子，我们比较了在处理具体或抽象名词时观察到的171维相干向量，并在某些时间段获得了显着的全局差异。作为非配对样本的例子，我们比较了语言学生和非语言学生处理英语文本时观察到的相干向量，发现了显著的全局差异。(C)2004 Elsevier B.V.保留所有权利。
In this paper several multivariate tests are presented, in particular permutation tests, which can be used in multiple endpoint problems as for example in comparisons of high-dimensional vectors of EEG data. We have investigated the power of these tests using artificial data in simulations and real EEG data. It is obvious that no one multivariate test is uniformly most powerful. The power of the different methods depends in different ways on the correlation between the endpoints, on the number of endpoints for which differences exist and on other factors. Based on our findings, we have derived rules of thumb regarding under which configurations a particular test should be used. In order to demonstrate the properties of different multivariate tests we applied them to EEG coherence data. As an example for the paired samples case, we compared the 171-dimensional coherence vectors observed for the alphal-band while processing either concrete or abstract nouns and obtained significant global differences for some sections of time. As an example for the unpaired samples case, we compared the coherence vectors observed for language students and non-language students who processed an English text and found a significant global difference. (C) 2004 Elsevier B.V. All rights reserved.