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.

Center for Magnetic Resonance Research University of Minnesota Minneapolis MN USA

Central European Institute of Technology Masaryk University Brno Czech Republic

Department of Biomedical Engineering Brno University of Technology Brno Czech Republic

Department of Mathematics Brno University of Technology Brno Czech Republic

Department of Neurology Palacký University Olomouc Czech Republic

Department of Neurology University Hospital Olomouc Olomouc Czech Republic

Division of Endocrinology and Diabetes University of Minnesota Minneapolis MN USA

Mind Research Network Albuquerque NM 87106 USA

References provided by Crossref.org

Spatial (mis)match between EEG and fMRI signal patterns revealed by spatio-spectral source-space EEG decomposition

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