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首页> 外文期刊>Biological Cybernetics: Communication and Control in Organisms and Automata: = Nachrichtenubertragung, Nachrichtenverarbeitung, Steuerung und Regelung in Organismen und in Automaten >Application of modern tests for stationarity to single-trial MEG: Data transferring powerful statistical tools from econometrics to neuroscience
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Application of modern tests for stationarity to single-trial MEG: Data transferring powerful statistical tools from econometrics to neuroscience

机译：平稳性的现代测试在单次试验中的应用：数据将强大的统计工具从计量经济学转移到神经科学

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Stationarity is a crucial yet rarely questioned assumption in the analysis of time series ofmagneto- (MEG) or electroencephalography (EEG). One key drawback of the commonly used tests for stationarity of encephalographic time series is the fact that conclusions on stationarity are only indirectly inferred either from the Gaussianity (e.g. the Shapiro-Wilk test or Kolmogorov-Smirnov test) or the randomness of the time series and the absence of trend using very simple time-series models (e.g. the sign and trend tests by Bendat and Piersol). We present a novel approach to the analysis of the stationarity of MEG and EEG time series by applying modern statistical methods which were specifically developed in econometrics to verify the hypothesis that a time series is stationary. We report our findings of the application of three different tests of stationarity - the Kwiatkowski- Phillips-Schmidt-Schin (KPSS) test for trend or mean stationarity, the Phillips-Perron (PP) test for the presence of a unit root and the White test for homoscedasticity - on an illustrative set of MEG data. For five stimulation sessions, we found already for short epochs of duration of 250 and 500ms that, although the majority of the studied epochs of single MEG trials were usually mean-stationary (KPSS test and PP test), they were classified as nonstationary due to their heteroscedasticity (White test). We also observed that the presence of external auditory stimulation did not significantly affect the findings regarding the stationarity of the data.We conclude that the combination of these tests allows a refined analysis of the stationarity of MEG and EEG time series.
机译：平稳性是在磁磁（MEG）或脑电图（EEG）的时间序列分析中一个至关重要但很少有人质疑的假设。常用的脑电图时间序列平稳性测试的一个关键缺点是，仅从高斯性（例如Shapiro-Wilk检验或Kolmogorov-Smirnov检验）或时间序列的随机性间接推断出平稳性结论使用非常简单的时间序列模型（例如Bendat和Piersol的符号和趋势测试）就没有趋势。我们提出了一种新颖的方法，通过应用计量经济学中专门开发的现代统计方法来验证MEG和EEG时间序列的平稳性，以验证时间序列是平稳的假设。我们报告了应用三种不同的平稳性测试的结果：Kwiatkowski-Phillips-Schmidt-Schin（KPSS）趋势或平均平稳性测试，Phillips-Perron（PP）测试是否存在单位根和白色测试均一性-在一组说明性的MEG数据上。在五个刺激阶段中，我们已经发现持续250毫秒和500毫秒的较短时间段，尽管单个MEG试验的大多数研究时间段通常是平稳的（KPSS测试和PP测试），但由于以下原因，它们被分类为非平稳的他们的异方差（White测试）。我们还观察到外部听觉刺激的存在并没有显着影响有关数据平稳性的发现。我们得出结论，这些测试的组合可以对MEG和EEG时间序列的平稳性进行精细分析。

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