title: Non-parametric group-level statistics for source-resolved ERP analysis.
abstract: We have developed a new statistical framework for group-level event-related potential (ERP) analysis in EEGLAB. The framework calculates the variance of scalp channel signals accounted for by the activity of homogeneous clusters of sources found by independent component analysis (ICA). When ICA data decomposition is performed on each subject's data separately, functionally equivalent ICs can be grouped into EEGLAB clusters. Here, we report a new addition (statPvaf) to the EEGLAB plug-in std_envtopo to enable inferential statistics on main effects and interactions in event related potentials (ERPs) of independent component (IC) processes at the group level. We demonstrate the use of the updated plug-in on simulated and actual EEG data.
authors.forename: Clement
authors.initials: C
authors.lastname: Lee
authors.forename: Makoto
authors.initials: M
authors.lastname: Miyakoshi
authors.forename: Arnaud
authors.initials: A
authors.lastname: Delorme
authors.forename: Gert
authors.initials: G
authors.lastname: Cauwenberghs
authors.forename: Scott
authors.initials: S
authors.lastname: Makeig
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D012535:Scalp; D018709:Statistics, Nonparametric