单变量
计算机科学
随机排列
排列(音乐)
随机效应模型
脑电图
人工智能
广义线性混合模型
线性模型
混合模型
统计
机器学习
多元统计
数学
心理学
神经科学
医学
荟萃分析
物理
几何学
声学
内科学
块(置换群论)
作者
Antonino Visalli,Maria Montefinese,Giada Viviani,Livio Finos,Antonino Vallesi,Ettore Ambrosini
标识
DOI:10.1016/j.jneumeth.2023.109991
摘要
Mixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. Here, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. lmeEEG showed excellent performance properties in terms of power and false positive rate. lmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). lmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.
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