Vol.:(0123456789) 1 3 Brain Topogr DOI 10.1007/s10548-017-0585-8 ORIGINAL PAPER Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA René Labounek 1,2,3  · David A. Bridwell 4  · Radek Mareček 2  · Martin Lamoš 1,2  · Michal Mikl 2  · Tomáš Slavíček 1,2  · Petr Bednařík 2,5,6  · Jaromír Baštinec 7  · Petr Hluštík 3,8  · Milan Brázdil 2  · Jiří Jan 1   Received: 4 October 2016 / Accepted: 18 August 2017 © Springer Science+Business Media, LLC 2017 that fourteen diferent independent spatiospectral maps are present across the diferent paradigms/tasks, i.e. they are generally stable. Keywords EEG · ICA · Spatiospectral patterns · Multi- subject blind source separation · Resting-state · Semantic decision · Visual oddball Introduction Scalp electrical fuctuations (measured with electroencepha- lography; EEG) are related to a range of cognitive processes, and diferent processes are often associated with diferent frequencies (Buzsaki 2006). These potentially distinct pro- cesses sum together due to the volume conduction properties of the brain, skull, and scalp resulting in spatial smearing of voltages on the surface of the scalp (Nunez and Srinivasan 2006). Blind source separation (BSS) approaches are useful for decomposing voltage mixtures measured from electrodes placed on the scalp surface, and temporal independent com- ponent analysis (ICA) is one of the most often used BSS algorithms. Temporal ICA decomposes the electrode × time Abstract Electroencephalography (EEG) oscillations refect the superposition of diferent cortical sources with potentially diferent frequencies. Various blind source sepa- ration (BSS) approaches have been developed and imple- mented in order to decompose these oscillations, and a sub- set 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 dif- ferent paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates This is one of several papers published together in Brain Topography on the “Special Issue: Multisubject decomposition of EEG - methods and applications”. Electronic supplementary material The online version of this article (doi:10.1007/s10548-017-0585-8) contains supplementary material, which is available to authorized users. * René Labounek rene.labounek@gmail.com 1 Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic 2 Central European Institute of Technology, Masaryk University, Brno, Czech Republic 3 Department of Neurology, Palacký University, Olomouc, Czech Republic 4 Mind Research Network, Albuquerque, NM 87106, USA 5 Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA 6 Division of Endocrinology and Diabetes, University of Minnesota, Minneapolis, MN, USA 7 Department of Mathematics, Brno University of Technology, Brno, Czech Republic 8 Department of Neurology, University Hospital Olomouc, Olomouc, Czech Republic