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

0547984 - ÚI 2023 RIV NL eng J - Journal Article
Billings, Jacob - Tivadar, R. - Murray, M.M. - Franceschiello, B. - Petri, G.
Topological Features of Electroencephalography are Robust to Re-referencing and Preprocessing.
Brain Topography. Roč. 35, č. 1 (2022), s. 79-95. ISSN 0896-0267. E-ISSN 1573-6792
Institutional support: RVO:67985807
Keywords : Resting-state Electroencephalography * Topography * Topology * Network * Computational Modelling * Reference Electrode
OECD category: Neurosciences (including psychophysiology
Impact factor: 2.7, year: 2022
Method of publishing: Limited access
http://dx.doi.org/10.1007/s10548-021-00882-w

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.
Permanent Link: http://hdl.handle.net/11104/0324118