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Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing
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SYSNO ASEP 0547984 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing Author(s) Billings, Jacob (UIVT-O) SAI, ORCID, RID
Tivadar, R. (CH)
Murray, M.M. (CH)
Franceschiello, B. (CH)
Petri, G. (IT)Source Title Brain Topography. - : Springer - ISSN 0896-0267
Roč. 35, č. 1 (2022), s. 79-95Number of pages 17 s. Language eng - English Country NL - Netherlands Keywords Resting-state Electroencephalography ; Topography ; Topology ; Network ; Computational Modelling ; Reference Electrode Subject RIV FH - Neurology OECD category Neurosciences (including psychophysiology Method of publishing Limited access Institutional support UIVT-O - RVO:67985807 UT WOS 000740622900001 EID SCOPUS 85122669521 DOI 10.1007/s10548-021-00882-w Annotation Electroencephalography (EEG) is among the most widely diffused, inexpensive, and adopted neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analyses. It is therefore valuable to obtain quantities that are minimally affected by reference and pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, was neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individual's brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects' space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional-topological-level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2023 Electronic address http://dx.doi.org/10.1007/s10548-021-00882-w
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