Society for Neuroscience, San Diego, Nov 2007
New methods for analyzing trial-to-trial EEG spectral
variability
Scott Makeig* and Julie Onton
Event-related potential (ERP) and spectral perturbation
(ERSP) measures are typically assumed to reveal EEG activities specifically
associated with brain responses to selected task events while canceling out
other concurrent ('random') brain variability. By contrast, we assume that all
changes in EEG brain activity index the actions and interactions of meaningful
brain processes. To reveal these, we first spatially decompose EEG data by independent component
analysis (ICA)
into maximally distinct or temporally independent component (IC) activities.
Under favorable circumstances, many separated ICs can be localized to specific
cortical areas. However, even trial averages of IC activities or activity
spectra still ignore the significant variability in these activities across
single experimental trials or time windows. Here, we propose new methods for
studying trial-to-trial or moment-to-moment variability in the log power
spectra of IC processes. Our aim is to identify characteristic modes of
spectral modulation of single or multiple EEG processes, and the relationship
of these modes to specific cognitive demands. We compare and contrast three new
ICA
decomposition methods: 1) Template-Weight (TW) decompositions into frequency
template and independent trial weight matrices, respectively (see Onton et al., Neuroimage,
2005).; 2) Weight-Template (WT) decompositions
into time weight and independent frequency template matrices; 3) Context ICA
(XICA) decomposition. TW or WT decompositions may be performed on single trials
from one or more components from one or more subjects. XICA, a TW log spectral
decomposition that incorporates behavioral and event information for each trial
and previous trials, identifies modes of spectral activity associated with
specific types of event context, e.g., with specific 'brain challenges'. We believe the new methods may help clarify
the functions of EEG activity.