Mining event-related brain dynamics

Abstract

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.

Updating P300: An integrative theory of P3a and P3b

“…The distinct time-scales for brain stem, middle latency, and endogenous potentials indicate that frequency is an important parameter for ERP interpretation (Hillyard and Picton, 1987;Makeig, 2002;Regan, 1989). Procedures such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are based on mathematical transformations of ERP data and can be employed to separate functionally distinct events that occur simultaneously in time Tenke, 2003, 2005;Makeig et al, 2004). Additional analytical methods are also available (cf.…”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…Microstates are determined post hoc by fitting the cluster maps back to the data (see below). Several alternative methods for cluster or factor analysis can be used to determine the most dominant spatial components in map series, such as agglomerative hierarchical clustering (Murray et al, 2008), principal component analysis (Pourtois et al, 2008;Skrandies, 1989;Spencer et al, 2001), independent component analysis (Makeig et al, 2004;Makeig et al, 1999), a mixture of Gaussian algorithms (De Lucia et al, 2007), or decomposition based on Markov processes (Hadriche et al, 2013). These methods all aim to identify subcomponents of the data that are considered to be unrelated, but they differ with regard to the mathematical definitions of "unrelated."…”

Unmixing concurrent EEG-fMRI with parallel independent component analysis

“…We see a major utility for parallel ICA in this context as it provides the means to disentangle and visualize these networks both in their spatial and temporal form (Calhoun, et al, 2006a;Debener, et al, 2006;Makeig, et al, 2004a;McKeown, et al, 2003;Onton, et al, 2006). However, some limitations apply: Infomax assumes sources to have non-normal, either superor subgaussian distributions (Bell, et al, 1995;Lee, et al, 1999), and this seems to hold for a great variety of physiological signals as well as technical artefacts.…”

“…The reason for this is that a salient event can induce multiple, simultaneously active, regionally overlapping, and functionally separable responses which add to existing neuronal background activity, in other words, event-related processes are spatially and temporally mixed across the brain. The scalp EEG samples a volume-conducted, spatially degraded version of the responses, where the potential at any location and latency can be considered a mixture of multiple independent timecourses that stem from large-scale synchronous field potentials (Makeig, et al, 2004a;Onton, et al, 2006). Similarly, the neurovascular transformation of the distributed neuronal activity into hemodynamic signals (Lauritzen, et al, 2003;Logothetis, 2003) affords detection of blood oxygenation level dependent responses (BOLD, Ogawa, et al, 1990) that are temporally degraded and spatially mixed across the fMRI volume (Calhoun, et al, 2006a;McKeown, et al, 2003).…”