Building blocks of functional connectivity measures for aperiodic electrophysiological brain signals.

Rikkert Hindriks, Thomas O Rot, Michel J A M van Putten, Prejaas Tewarie

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Article in Neuroimage. Reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Enhancing EEG-Based Brain Pattern Recognition Through Functional-Network-Level Volume Conduction Mitigation: Spatially Informed Decay Modeling-Residual Correction.Brain sciences · 2026
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5 · Who and what money

Authors and funding

4 authors.

Rikkert HindriksDepartment of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands.

Thomas O RotDepartment of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands.

Michel J A M van PuttenClinical Neurophysiology Group, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands.

Prejaas TewarieSir Peter Mansfield Imaging Center, School of Physics, University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A challenge in interpreting functional connectivity results in electroencephalography (EEG) data is volume conduction. A common way to mitigate spurious connectivity due to volume conduction is to use connectivity measures that are insensitive to volume conduction. Examples of such measures are the imaginary coherence, the lagged coherence, and the (weighted) phase-lag index. Their insensitivity to volume conduction stems from an invariant property and it is of both practical and theoretical interest to identify all measures with this property. In this study we derive a set of invariant connectivity measures that are fundamental in the sense that all others can be constructed from them by combination. These "building blocks" of connectivity measures quantify the lack of invariance of multivariate EEG signals under permutation of the time-points. We use this result to construct a new connectivity measure for stationary aperiodic EEG signals, referred to as the

Indexed as

Aperiodic signalElectroencephalographyFunctional connectivityGrassmann manifoldLocal field potentialPlücker coordinateUtah arrayVolume conduction

Identifiers

PMID41472720

DOI10.1016/j.ynirp.2025.100284

PMCPMC12746282

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