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Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing

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    SYSNO ASEP0547984
    Document TypeJ - Journal Article
    R&D Document TypeJournal Article
    Subsidiary JČlánek ve WOS
    TitleTopological 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 TitleBrain Topography - ISSN 0896-0267
    Roč. 35, č. 1 (2022), s. 79-95
    Number of pages17 s.
    Languageeng - English
    CountryNL - Netherlands
    KeywordsResting-state Electroencephalography ; Topography ; Topology ; Network ; Computational Modelling ; Reference Electrode
    Subject RIVFH - Neurology
    OECD categoryNeurosciences (including psychophysiology
    Method of publishingLimited access
    Institutional supportUIVT-O - RVO:67985807
    UT WOS000740622900001
    EID SCOPUS85122669521
    DOI10.1007/s10548-021-00882-w
    AnnotationElectroencephalography (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.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2023
    Electronic addresshttp://dx.doi.org/10.1007/s10548-021-00882-w
Number of the records: 1