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Stability of the Default Mode Network Estimated from Electroencephalogram
Mälardalen University, Faculty of Engineering and Health Sciences, Department of Computer Science & Engineering.
Mälardalen University, Faculty of Engineering and Health Sciences, Department of Computer Science & Engineering.
Mälardalen University, Faculty of Engineering and Health Sciences, Department of Computer Science & Engineering.ORCID iD: 000-0002-3869-279X
2025 (English)In: International IEEE/EMBS Conference on Neural Engineering, NER, Institute of Electrical and Electronics Engineers (IEEE) , 2025, p. 573-578Conference paper, Published paper (Refereed)
Abstract [en]

The Default Mode Network (DMN) is associated with an internal self-referential view of the world, and its intrinsic properties have been linked to different cognitive abilities. While its function and structure has been well characterized through functional magnetic resonance imaging (fMRI), much less is known about its behavior using electroencephalography (EEG). This study examines the stability of EEG-based DMN functional connectivity. We focus on eyes-open resting-state across multiple sessions. Using the debiased weighted Phase Lag Index (dwPLI), we analyzed connectivity patterns in the alpha band across four sessions involving twenty participants. Our results show consistent DMN connectivity patterns both within and across individuals and sessions. This indicates that EEG-derived DMN connectivity is relatively stable over time and across people. Such stability could be relevant for the development of brain-computer interfaces (BCI) for cognitive training that adapt to individual connectivity patterns.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2025. p. 573-578
Keywords [en]
BCI, Default mode network (DMN), EEG, functional connectivity, resting-state, Brain computer interface, Electrophysiology, Interfaces (computer), Magnetic resonance imaging, Cognitive ability, Connectivity pattern, Default mode network, Functional magnetic resonance imaging, Intrinsic property, Multiple sessions, Network connectivity, Phase lags, Resting state, Electroencephalography
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:mdh:diva-78674DOI: 10.1109/NER61569.2025.11588924Scopus ID: 2-s2.0-105045298493ISBN: 9798331596262 (print)OAI: oai:DiVA.org:mdh-78674DiVA, id: diva2:2088803
Conference
12th Annual International IEEE/EMBS Conference on Neural Engineering, NER 2025, San Diego, 11 November 2025 - 14 November 2025
Available from: 2026-07-29 Created: 2026-07-29 Last updated: 2026-07-29Bibliographically approved

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Silva, JoanaSyrjänen, ElmeriÅstrand, Elaine
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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