EEG (Electroencephalography) resting state was studied by means of group blind source separation (gBSS), employing a test-retest strategy in two large-sample normative databases (N=57 and N=84). Using a BSS method in the complex Fourier domain and a model-driven distributed inverse solution we closely replicate both the spatial distribution and spectral pattern of seven source components. Norms were then constructed for their spectral power so as to allow testing patients against the norms. As compared to existing normative databases based on scalp spectral measures, the resulting tool defines a smaller number of features with very little inter-correlation. Furthermore, these features are physiologically meaningful as they relate the activity of several brain regions, forming a total of seven patterns, each with a peculiar spatial distribution and spectral profile. This new tool, that we name normative independent component analysis (NICA), may serve as an adjunct to diagnosis and assessment of abnormal brain functioning and aid in research on normal resting state networks.
Abstract. The aim of this work is to study the coherence profile (dependence) of robust eyes-closed resting EEG sources isolated by group blind source separation (gBSS). We employ a test-retest strategy using two large sample normative databases (N=57 and N=84). Using a BSS method in the complex Fourier domain, we show that we can rigourously study the out-of-phase dependence of the exctracted components, albeit they are extracted so as to be in-phase independent (by BSS definition). Our focus on lagged communication between components effectively yields dependence measures unbiased by volume conduction effects, which is a major concern about the validity of any dependence measures issued by EEG measurements. We are able to show the organization of the extracted components in two networks. Within each network components oscillate coherently with multiple-frequency dynamics, whereas between networks they exchange information at non-random multiple time-lag rates.
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