Počet záznamů: 1
Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing
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SYSNO ASEP 0547984 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing Tvůrce(i) Billings, Jacob (UIVT-O) SAI, ORCID, RID
Tivadar, R. (CH)
Murray, M.M. (CH)
Franceschiello, B. (CH)
Petri, G. (IT)Zdroj.dok. Brain Topography. - : Springer - ISSN 0896-0267
Roč. 35, č. 1 (2022), s. 79-95Poč.str. 17 s. Jazyk dok. eng - angličtina Země vyd. NL - Nizozemsko Klíč. slova Resting-state Electroencephalography ; Topography ; Topology ; Network ; Computational Modelling ; Reference Electrode Vědní obor RIV FH - Neurologie, neurochirurgie, neurovědy Obor OECD Neurosciences (including psychophysiology Způsob publikování Omezený přístup Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000740622900001 EID SCOPUS 85122669521 DOI 10.1007/s10548-021-00882-w Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2023 Elektronická adresa http://dx.doi.org/10.1007/s10548-021-00882-w
Počet záznamů: 1