Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA
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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.
KeywordsEEG ICA Spatiospectral patterns Multi-subject blind source separation Resting-state Semantic decision Visual oddball
We would like to thank Dr. Milena Košťálová for her help with designing the semantic decision task. This research was supported Grant No. P304/11/1318 of Grant Agency of Czech Republic, by Grants Nos. FEKT-S-14-2210 and FEKT-S-11-2-921 of Brno University of Technology, by Grants Nos. CZ.1.05/1.1.00/02.0068 of Central European Institute of Technology and by Grants Nos. AZV 16-302100A of Palacký University. The funding is highly acknowledged. Computational resources were provided by the MetaCentrum under the program LM2010005 and the CERIT-SC under the program Centre CERIT Scientific Cloud, part of the Operational Program Research and Development for Innovations, Reg. No. CZ.1.05/3.2.00/08.0144.
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