Multiway array decomposition methods have been shown to be promising statistical tools for identifying neural activity in the EEG spectrum. They blindly decompose the EEG spectrum into spatial-temporal-spectral patterns by taking into account inherent relationships among signals acquired at different frequencies and sensors. Our study evaluates the stability of spatial-temporal-spectral patterns derived by one particular method, parallel factor analysis (PARAFAC). We focused on patterns' stability over time and in population and divided the complete data set containing data from 50 healthy subjects into several subsets. Our results suggest that the patterns are highly stable in time, as well as among different subgroups of subjects. Further, we show with simultaneously acquired fMRI data that power fluctuations of some patterns have stable correspondence to hemodynamic fluctuations in large-scale brain networks. We did not find such correspondence for power fluctuations in standard frequency bands, the common way of dealing with EEG data. Altogether, our results suggest that PARAFAC is a suitable method for research in the field of large-scale brain networks and their manifestation in EEG signal.

Brno University of Technology 60190 Brno Czech Republic and Department of Neurology Palacky University 77515 Olomouc Czech Republic

Central European Institute of Technology Masaryk University 62500 Brno Czech Republic

Central European Institute of Technology Masaryk University 62500 Brno Czech Republic and Brno University of Technology 60190 Brno Czech Republic

Central European Institute of Technology Masaryk University 62500 Brno Czech Republic and Brno University of Technology 60190 Brno Czech Repulbic

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?