Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA

Abstract

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

“…In comparison to resting theta oscillations, the alpha rhythm dominates the resting EEG, with most individuals showing a distinct alpha peak at about 10 Hz having a robust posterior topography (Aurlien et al, 2004;Chiang, Rennie, Robinson, van Albada, & Kerr, 2011). Moreover, the alpha rhythm is prominent (e.g., visible in raw EEG traces) and reliably quantified by different research groups using different methodologies (e.g., Barry & De Blasio, 2018;Labounek et al, 2018;Schmidt et al, 2017;Shackman et al, 2010;Sockeel, Schwartz, Pélégrini-issac, & Benali, 2016;Tenke et al, 2017). Importantly, greater posterior alpha oscillations at rest predicted a favorable clinical outcome for individuals diagnosed with MDD (Baskaran et al, 2017;Bruder et al, 2008;Jaworska, de la Salle, Ibrahim, Blier, & Knott, 2019;Kandilarova et al, 2017;Knott et al, 1996;Tenke et al, 2011;Ulrich, Renfordt, Zeller, & Frick, 1984;Ulrich, Renfordt, & Frick, 1986; although see Arns et al, 2016, andKnott, Mahoney, Kennedy, &Evans, 2000, for unsuccessful attempts to replicate these findings).…”

Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?

“…The identical simultaneous EEG-fMRI dataset of visual oddball paradigm was used as previously described (18,19,48,49). The event-related designed visual oddball task was performed by 21 healthy subjects (13 right-handed men, one left-handed man, seven right-handed women; age 23 ± 2 years).…”

“…The EEG absolute/relative power spectra consist of a linear mixture of stable spatiospectral patterns [i.e., different stable g(c,ω) functions in Equation ( 2)] with temporal fluctuations that were more task-related for the relative power rather than the absolute power (48,49). Absolute EEG power identified 14 stable patterns with highly significant EEG-fMRI associations at visual oddball dataset (19, 48).…”

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

“…Various extensions to BSS have been developed to address the issue of determining which BSS sources correspond across subjects, including approaches which decompose the aggregate group EEG data (Kovacevic and McIntosh, 2007 ; Congedo et al, 2010 ; Eichele et al, 2011 ; Cong et al, 2013 ; Lio and Boulinguez, 2013 ; Ponomarev et al, 2014 ; Ramkumar et al, 2014 ; Huster et al, 2015 ; Huster and Raud, 2018 ; Labounek et al, 2018 ). Within temporal Group ICA implemented in EEGIFT 1 , sources are estimated at the aggregate group level and individual subject sources are recovered by back-reconstruction (Calhoun et al, 2001 ; Calhoun and Adali, 2012 ).…”