Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA
- 702 Downloads
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.
- Bridwell DA, Calhoun V (2014) Fusing Concurrent EEG and fMRI intrinsic networks. In: Magnetoencephalography. Springer, Berlin Heidelberg, pp 213–235Google Scholar
- Himberg J, Hyvärinen A (2003) Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. In: 13th Workshop on Neural Networks for Signal Processing. IEEE, pp 259–268Google Scholar
- Labounek R, Janeček D, Mareček R et al (2016) Generalized EEG-fMRI spectral and spatiospectral heuristic models. In: IEEE 13th international symposium on biomedical imaging: From nano to macro. IEEE, Prague, pp 767–770. doi: 10.1109/ISBI.2016.7493379
- Lio G, Boulinguez P (2013) Greater robustness of second order statistics than higher order statistics algorithms to distortions of the mixing matrix in blind source separation of human EEG: implications for single-subject and group analyses. Neuroimage 67:137–152. doi: 10.1016/j.neuroimage.2012.11.015 CrossRefPubMedGoogle Scholar
- Niedermeyer E, da Silva FL (2011) Electroencephalography: basic principles, clinical applications, and related fields, 6th edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
- Stone JV (2004) Independent component analysis: a tutorial introduction. MIT press, CambridgeGoogle Scholar
- Tang A (2010) Applications of second order blind identification to high-density EEG-based brain imaging: a review. In: International Symposium on Neural Networks. Springer Berlin, Heidelberg, pp 368–377Google Scholar
- Tichavský P, Koldovský Z, Doron E et al (2006) Blind signal separation by combining two ICA algorithms: HOS-based EFICA and time structure-based WASOBI. In: 14th European Signal Processing Conference. IEEE, Florence, pp 1–5Google Scholar
- Tomioka R, Dornhege G, Nolte G et al (2006) Spectrally weighted Common Spatial Pattern algorithm for single trial EEG classification. Dept Math Eng Univ Tokyo Tokyo Japan Tech Rep 40:1–23Google Scholar
- Yuan H, Liu T, Szarkowski R et al (2010) Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements. Neuroimage 49:2596–2606. doi: 10.1016/j.neuroimage.2009.10.028 CrossRefPubMedGoogle Scholar