Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies

Jeffrey S. Spence, Matthew R. Brier, John Hart, Thomas C. Ferree

Research output: Contribution to journalArticle

6 Scopus citations

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. Hum Brain Mapp, 2013.

Original languageEnglish (US)
Pages (from-to)651-664
Number of pages14
JournalHuman Brain Mapping
Volume34
Issue number3
DOIs
StatePublished - Mar 1 2013

Keywords

  • Principal components
  • Statistical power
  • Time-frequency analysis

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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