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

Details

Serval ID
serval:BIB_ED30F69DB1B4
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing.
Journal
Brain topography
Author(s)
Billings J., Tivadar R., Murray M.M., Franceschiello B., Petri G.
ISSN
1573-6792 (Electronic)
ISSN-L
0896-0267
Publication state
Published
Issued date
01/2022
Peer-reviewed
Oui
Volume
35
Number
1
Pages
79-95
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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.
Keywords
Computational modelling, Network, Reference electrode, Resting-state electroencephalography, Topography, Topology
Pubmed
Web of science
Create date
17/01/2022 11:24
Last modification date
20/07/2022 5:37
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