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• W2031491966 abstract "Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task." @default.
• W2031491966 created "2016-06-24" @default.
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• W2031491966 date "2011-01-01" @default.
• W2031491966 modified "2023-09-27" @default.
• W2031491966 title "Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies" @default.
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• W2031491966 doi "https://doi.org/10.1002/hbm.21462" @default.
• W2031491966 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6869945" @default.
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