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StaR: An EEGLAB Framework for the Measure Projection Toolbox (MPT) Statistical Analyses to be Performed in R

  • Yannick RoyEmail author
  • Jean-Claude Piponnier
  • Jocelyn Faubert
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 478)

Abstract

EEGLAB, a widely used toolbox in MATLAB (The Mathworks, Inc.), uses Independent Component Analysis (ICA) to decompose the EEG signal into sub-signals, and localizes brain sources of those sub-signals prior to independent component (IC) clustering for group study. In 2013, the Measure Projection Toolbox (MPT) was introduced as a new data-driven IC clustering toolbox for EEGLAB. Despite the numerous features and advantages offered by EEGLAB and the MPT, they both have limitations for statistical analyses with more than two independent variables. In order to work around those limitations, this paper introduces StaR, an EEGLAB framework for the MPT statistical analyses to be performed in R. StaR initially exports the data from different clusters generated by the MPT for different measures of interest (e.g., Event-Related Potentials (ERPs) and Event-Related Spectral Perturbations (ERSPs)) and formats the data such that further statistical analyses can be performed in R. Once in R, StaR uses linear mixed models as its default method to better handle missing values and intra-subject variability. Finally, StaR brings the results back into MATLAB to plot the results with the well-known and easy to interpret EEGLAB graphics. To make the whole process easy, StaR also offers an intuitive user interface that integrates into EEGLAB’s menu.

Keywords

EEG EEGLAB Measure projection toolbox Mixed models R 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yannick Roy
    • 1
    Email author
  • Jean-Claude Piponnier
    • 1
  • Jocelyn Faubert
    • 1
  1. 1.Visual Psychophysics and Perception Laboratory, École d’OptométrieUniversité de MontréalMontréalCanada

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