Advanced EEG analysis using threshold-free cluster-enhancement and non-parametric statistics
Advanced EEG analysis using threshold-free cluster-enhancement and non-parametric statistics
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DOI：
10.1016/j.neuroimage.2012.10.027
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发表时间：
2013-02-15
期刊：
影响因子：
5.7
通讯作者：
Khatami, Ramin
中科院分区：
文献类型：
--
作者：
Mensen, Armand;Khatami, Ramin
EEG信号分析的进展及其与其他调查技术的结合使得大型EEG数据集的适当统计分析成为一个关键问题。随着可用通道和样本数量的增加，以及更具探索性的实验设计，有必要开发一种具有高水平统计完整性和信号灵敏度的统计过程，尽管如此，这种统计过程仍会产生对普通用户可解释的结果。最近提出的无障碍集群增强作为一个有用的分析工具的功能磁共振成像数据集。这种方法基本上考虑了数据点的统计强度和邻域，以将原始信号转换为对组或条件之间的“真实的”差异的更直观理解。在这里，我们采用这种方法来优化处理EEG数据集，并使用基于排列的统计来建立一个有效的统计分析。此外，我们比较的结果与其他几个非参数和参数的方法，目前可使用现实的模拟EEG信号。所提出的方法被证明是一般更敏感的各种常见的EEG数据集的信号类型，而不需要任何任意调整的参数。此外，为每个通道样本对生成唯一的p值，以便仍然可以对数据集提出特定问题，同时提供有关大规模实验效应的一般信息。(C)2012 Elsevier Inc. All rights reserved.
Advances in EEG signal analysis and its combination with other investigative techniques make appropriate statistical analysis of large EEG datasets a crucial issue. With an increasing number of available channels and samples, as well as more exploratory experimental designs, it has become necessary to develop a statistical process with a high level of statistical integrity, signal sensitivity which nonetheless produces results which are interpretable to the common user. Threshold-free cluster-enhancement has recently been proposed as a useful analysis tool for fMRI datasets. This approach essentially takes into account both a data point's statistical intensity and neighbourhood to transform the original signal into a more intuitive understanding of 'real' differences between groups or conditions. Here we adapt this approach to optimally deal with EEG datasets and use permutation-based statistics to build an efficient statistical analysis. Furthermore we compare the results with several other non-parametric and parametric approaches currently available using realistic simulated EEG signals. The proposed method is shown to be generally more sensitive to the variety of signal types common to EEG datasets without the need for any arbitrary adjusting of parameters. Moreover, a unique p-value is produced for each channel-sample pair such that specific questions can still be asked of the dataset while providing general information regarding the large-scale experimental effects. (C) 2012 Elsevier Inc. All rights reserved.