Introduction

Resting-state electroencephalography (EEG) measures intrinsic brain activity in the absence of explicit tasks orstimuli[1](https://www.nature.com/articles/s41598-025-23662-z#ref-CR1 “Engel, A. K., Gerloff, C., Hilgetag, C. C. & Nolte, G. Intrinsic coupling modes: Multiscale interactions in ongoing brain activity. Neuron 80(4), 867–886. https://doi.org/10.1016/j.neuron.2013.09.038

(2013).“). While resting-state EEG can be characterized by various aspects of brain function—from local oscillations to network-level organization2—its utility as a biomarker depends on measurement reliability. Subject heterogeneity, varying cognitive states and differences in measurement instructions can influence resting-state EEG recordings. These influences can lead to changes in amplitude, frequency distribution, oscillatory patterns and other EEG features[3](https://www.nature.com/articles/s41598-025-23662-z#ref-CR3 “van Diessen, E. et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin. Neurophysiol. 126(8), 1468–1481. https://doi.org/10.1016/j.clinph.2014.11.018

(2015).“),[4](https://www.nature.com/articles/s41598-025-23662-z#ref-CR4 “Barry, R. J., Clarke, A. R., Johnstone, S. J., Magee, C. A. & Rushby, J. A. EEG differences between eyes-closed and eyes-open resting conditions. Clin. Neurophysiol. 118(12), 2765. https://doi.org/10.1016/j.clinph.2007.07.028

(2007).“). This variability poses a challenge for biomarker development, where the goal is to reliably identify (alterations in) brain activity regardless of momentary state fluctuations. Resting-state EEG measures should demonstrate reliability across different conditions to serve as clinical biomarkers, indicating relative state independence while capturing the underlying trait[5](https://www.nature.com/articles/s41598-025-23662-z#ref-CR5 “Gemein, L. A. W., Schirrmeister, R. T., Boedecker, J. & Ball, T. Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning. Imaging Neurosci. 2, 1–22. https://doi.org/10.1162/imag_a_00210

(2024).“),[6](https://www.nature.com/articles/s41598-025-23662-z#ref-CR6 “Coan, J. A. & Allen, J. J. B. The state and trait nature of frontal EEG asymmetry in emotion. Asymmetrical Brain https://doi.org/10.7551/mitpress/1463.003.0023

(2018).“).

One way to assess EEG biomarker reliability is through measurement of their stability within subjects over time in repeated measures under similar conditions. Different EEG measures show varying degrees of temporal stability, which is high for power spectral characteristics, but variable for different functional connectivity (FC) measures[7](https://www.nature.com/articles/s41598-025-23662-z#ref-CR7 “Lopez, K. L., Monachino, A. D., Vincent, K. M., Peck, F. C. & Gabard-Durnam, L. J. Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities. Neuroimage 275, 120116. https://doi.org/10.1016/j.neuroimage.2023.120116

(2023).“), where stability decreases with increasing retest intervals from hours to days[8](#ref-CR8 “Kuntzelman, K. & Miskovic, V. Reliability of graph metrics derived from resting-state human EEG. Psychophysiology https://doi.org/10.1111/psyp.12600

(2017).“),[9](#ref-CR9 “Moezzi, B., Hordacre, B., Berryman, C., Ridding, M. C. & Goldsworthy, M. R. Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory. Biorxiv https://doi.org/10.1101/385302

(2018).“),10,[11](https://www.nature.com/articles/s41598-025-23662-z#ref-CR11 “Büchel, D., Lehmann, T., Sandbakk, Ø. & Baumeister, J. EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks. Sci. Rep. 11(1), 20803. https://doi.org/10.1038/s41598-021-00371-x

(2021).“) to approximately 1 month[12](https://www.nature.com/articles/s41598-025-23662-z#ref-CR12 “Cannon, R. L. et al. Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days. Neurosci. Lett. 518(1), 27–31. https://doi.org/10.1016/j.neulet.2012.04.035

(2012).“),[13](https://www.nature.com/articles/s41598-025-23662-z#ref-CR13 “Duan, W. et al. Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG. J. Neurosci. Methods 348, 108985. https://doi.org/10.1016/j.jneumeth.2020.108985

(2021).“) or longer10,[14](https://www.nature.com/articles/s41598-025-23662-z#ref-CR14 “Hardmeier, M. et al. Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PLoS ONE 9(10), e108648. https://doi.org/10.1371/journal.pone.0108648

(2014).“). Among FC measures studied in EEG, phase-based measures such as phase lag index (PLI) show moderate to good reliability, while amplitude-based FC measures remain less thoroughly studied and show inconsistent reliability10,[13](https://www.nature.com/articles/s41598-025-23662-z#ref-CR13 “Duan, W. et al. Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG. J. Neurosci. Methods 348, 108985. https://doi.org/10.1016/j.jneumeth.2020.108985

(2021).“),[15](https://www.nature.com/articles/s41598-025-23662-z#ref-CR15 “Rolle, C. E. et al. Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J. Neurosci. Methods 367, 109424. https://doi.org/10.1016/j.jneumeth.2021.109424

(2022).“). Complexity-based EEG measures have received limited attention, with one study reporting moderate reliability over a two-month period in a limited sample for several entropy measures[16](https://www.nature.com/articles/s41598-025-23662-z#ref-CR16 “Gudmundsson, S., Runarsson, T. P., Sigurdsson, S., Eiriksdottir, G. & Johnsen, K. Reliability of quantitative EEG features. Clin. Neurophysiol. 118(10), 2162–2171. https://doi.org/10.1016/j.clinph.2007.06.018

(2007).“). Nevertheless, local signal complexity is an emerging type of EEG measure, possibly relating to balance between neuronal excitation and inhibition[17](https://www.nature.com/articles/s41598-025-23662-z#ref-CR17 “Donoghue, T. et al. Evaluating and comparing measures of aperiodic neural activity. BioRXiv https://doi.org/10.1101/2024.09.15.613114

(2024).“). Expanding on this, an EEG marker combining local signal complexity and long-range FC is joint permutation entropy (JPE)[18](https://www.nature.com/articles/s41598-025-23662-z#ref-CR18 “Yin, Y., Shang, P., Ahn, A. C. & Peng, C. K. Multiscale joint permutation entropy for complex time series. Phys. A: Stat. Mech. Appl. 515, 388–402. https://doi.org/10.1016/j.physa.2018.09.179

(2019).“). Although JPE is a promising biomarker[19](https://www.nature.com/articles/s41598-025-23662-z#ref-CR19 “Scheijbeler, E. P. et al. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer’s disease?. Netw. Neurosci. 6(2), 382–400. https://doi.org/10.1162/netn_a_00224

(2022).“), specifically inverted JPE (JPEINV), its temporal reliability remains unexplored in EEG.

Besides the functional interactions itself, the networks formed by interacting brain regions can also be characterized, although this often comes with arbitrary methodological choices[20](https://www.nature.com/articles/s41598-025-23662-z#ref-CR20 “Van Wijk, B. C., Stam, C. J. & Daffertshofer, A. Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE 5(10), e13701. https://doi.org/10.1371/journal.pone.0013701

(2010).“). Analyzing the core of such networks using Minimum Spanning Trees (MST) offers an alternative approach to characterize brain networks that avoids such choices[21](https://www.nature.com/articles/s41598-025-23662-z#ref-CR21 “Tewarie, P., van Dellen, E., Hillebrand, A. & Stam, C. J. The minimum spanning tree: An unbiased method for brain network analysis. Neuroimage https://doi.org/10.1016/j.neuroimage.2014.10.015

(2015).“). This avoidance of arbitrary thresholds potentially leads to more consistent characterizations of core network topology across different studies, potentially reducing the heterogeneity observed in the literature on network alterations in brain disorders[22](https://www.nature.com/articles/s41598-025-23662-z#ref-CR22 “Blomsma, N. et al. Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity. Netw. Neurosci. 6(2), 301. https://doi.org/10.1162/netn_a_00245

(2022).“). However, the temporal reliability of MST characteristics remains unclear. Nevertheless, three studies using a non-MST graph theory approach report heterogeneous outcomes ranging from poor to good reliability[9](https://www.nature.com/articles/s41598-025-23662-z#ref-CR9 “Moezzi, B., Hordacre, B., Berryman, C., Ridding, M. C. & Goldsworthy, M. R. Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory. Biorxiv https://doi.org/10.1101/385302

(2018).“),[14](https://www.nature.com/articles/s41598-025-23662-z#ref-CR14 “Hardmeier, M. et al. Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG. PLoS ONE 9(10), e108648. https://doi.org/10.1371/journal.pone.0108648

(2014).“),[23](https://www.nature.com/articles/s41598-025-23662-z#ref-CR23 “Hatz, F. et al. Microstate connectivity alterations in patients with early Alzheimer’s disease. Alzheimers Res. Ther. 7(1), 78. https://doi.org/10.1186/s13195-015-0163-9

(2015).“).

The reliability of EEG measures may be improved through source reconstruction, which can reduce volume conduction effects while more directly capturing neuronal activity[3](https://www.nature.com/articles/s41598-025-23662-z#ref-CR3 “van Diessen, E. et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin. Neurophysiol. 126(8), 1468–1481. https://doi.org/10.1016/j.clinph.2014.11.018

(2015).“), aid interpretation of results[24](https://www.nature.com/articles/s41598-025-23662-z#ref-CR24 “Antiqueira, L., Rodrigues, F. A., van Wijk, B. C., Costa, L. D. F. & Daffertshofer, A. Estimating complex cortical networks via surface recordings—a critical note. Neuroimage 53(2), 439–449. https://doi.org/10.1016/j.neuroimage.2010.06.018

(2010).“) and increase signal-to-noise ratios[25](https://www.nature.com/articles/s41598-025-23662-z#ref-CR25 “Adjamian, P. et al. Effective electromagnetic noise cancellation with beamformers and synthetic gradiometry in shielded and partly shielded environments. J. Neurosci. Methods 178(1), 120–127. https://doi.org/10.1016/j.jneumeth.2008.12.006

(2009).“). Additionally, source reconstruction can lead to faster stabilization of MST measures[26](https://www.nature.com/articles/s41598-025-23662-z#ref-CR26 “Fraschini, M. et al. The effect of epoch length on estimated EEG functional connectivity and brain network organisation. J. Neural Eng. 13(3), 036015. https://doi.org/10.1088/1741-2560/13/3/036015

(2016).“). Evidence showing reliability of measures based on source-reconstructed EEG is limited, with heterogeneous results ranging from poor to good[9](https://www.nature.com/articles/s41598-025-23662-z#ref-CR9 “Moezzi, B., Hordacre, B., Berryman, C., Ridding, M. C. & Goldsworthy, M. R. Test-retest reliability of functional brain network characteristics using resting-state EEG and graph theory. Biorxiv https://doi.org/10.1101/385302

(2018).“),[13](https://www.nature.com/articles/s41598-025-23662-z#ref-CR13 “Duan, W. et al. Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG. J. Neurosci. Methods 348, 108985. https://doi.org/10.1016/j.jneumeth.2020.108985

(2021).“),[15](https://www.nature.com/articles/s41598-025-23662-z#ref-CR15 “Rolle, C. E. et al. Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J. Neurosci. Methods 367, 109424. https://doi.org/10.1016/j.jneumeth.2021.109424

(2022).“). Altogether, while power spectral measures show robust temporal reliability, evidence for the reliability of FC, complexity-based, and MST measures remains limited, with source reconstruction showing mixed benefits for measurement reliability.

Apart from these technical aspects, a complementary approach to assess biomarker stability is to investigate reliability of EEG measures through comparison of task-related resting periods, or semi-resting-state EEG[27](https://www.nature.com/articles/s41598-025-23662-z#ref-CR27 “Hommelsen, M., Viswanathan, S. & Daun, S. Robustness of individualized inferences from longitudinal resting state EEGdynamics. Eur. J. Neurosci. 56(1), 3613. https://doi.org/10.1111/ejn.15673

(2022).“), with resting-state EEG. The periods surrounding task execution are characterized by heightened attention, anticipation, and cognitive preparation, potentially altering brain activity even in the absence of stimuli[28](#ref-CR28 “Rogala, J., Kublik, E., Krauz, R. & Wróbel, A. Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance. Sci. Rep. 10(1), 5064. https://doi.org/10.1038/s41598-020-61866-7

(2020).“),[29](#ref-CR29 “Meredith Weiss, S. & Marshall, P. J. Anticipation across modalities in children and adults: Relating anticipatory alpha rhythm lateralization, reaction time, and executive function. Dev. Sci. 26(1), e13277. https://doi.org/10.1111/desc.13277

(2023).“),[30](#ref-CR30 “Ülkü, S., Getzmann, S., Wascher, E. & Schneider, D. Be prepared for interruptions: EEG correlates of anticipation when dealing with task interruptions and the role of aging. Sci. Rep. 14(1), 5679. https://doi.org/10.1038/s41598-024-56400-y

(2024).“),[31](https://www.nature.com/articles/s41598-025-23662-z#ref-CR31 “Clements, G. M. et al. Spontaneous alpha and theta oscillations are related to complementary aspects of cognitive control in younger and older adults. Front. Hum. Neurosci. 15, 621620. https://doi.org/10.3389/fnhum.2021.621620

(2021).“). Evidence suggests that oscillatory neural fingerprints remain stable across resting-state EEG and semi-resting-state EEG in the context of a motor task[27](https://www.nature.com/articles/s41598-025-23662-z#ref-CR27 “Hommelsen, M., Viswanathan, S. & Daun, S. Robustness of individualized inferences from longitudinal resting state EEGdynamics. Eur. J. Neurosci. 56(1), 3613. https://doi.org/10.1111/ejn.15673

(2022).“), indicating that resting-state EEG characteristics can maintain reliability despite different resting conditions. The extent to which more high-order measures such as FC, complexity and network characteristics remain stable across different resting conditions is yet to be investigated.

The present study used both repeated resting-state EEG measurements and semi-resting-state EEG to evaluate the reliability of various quantitative EEG measures across both time and different resting-state contexts in healthy subjects. The semi-resting-state data consisted of EEG in the periods of rest in a P50 gating paradigm measurement, which aims to measure sensory gating, referring to the preattentional filtering of irrelevant stimuli[32](https://www.nature.com/articles/s41598-025-23662-z#ref-CR32 “Wan, L., Friedman, B. H., Boutros, N. N. & Crawford, H. J. P50 sensory gating and attentional performance. Int. J. Psychophysiol. 67(2), 91–100. https://doi.org/10.1016/j.ijpsycho.2007.10.008

(2008).“). EEG offers diverse measures such as phase-, amplitude- and complexity-based measures, which fundamentally differ in the characteristics they capture[3](https://www.nature.com/articles/s41598-025-23662-z#ref-CR3 “van Diessen, E. et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin. Neurophysiol. 126(8), 1468–1481. https://doi.org/10.1016/j.clinph.2014.11.018

(2015).“), alongside network topology methods like the widely applied Minimum Spanning Tree (MST) to characterize brain organization[22](https://www.nature.com/articles/s41598-025-23662-z#ref-CR22 “Blomsma, N. et al. Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity. Netw. Neurosci. 6(2), 301. https://doi.org/10.1162/netn_a_00245

(2022).“). To cover these categories, we assessed phase-based (PLI), amplitude-based (corrected amplitude envelope correlation or AECc), and complexity-based (JPEINV) FC measures, alongside local signal complexity (PE) and network topology characteristics (MST), at both sensor and source level. This selection provides a diverse sample of common, relatively interpretable measures, aligning with prior applications in clinical contexts[19](https://www.nature.com/articles/s41598-025-23662-z#ref-CR19 “Scheijbeler, E. P. et al. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer’s disease?. Netw. Neurosci. 6(2), 382–400. https://doi.org/10.1162/netn_a_00224

(2022).“),[33](#ref-CR33 “Dauwan, M. et al. Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease. Alzheimer’s Dementia: Diagn. Assess. Dis. Monit. https://doi.org/10.1016/j.dadm.2016.07.003

(2016).“),[34](#ref-CR34 “Dominicus, L. et al. Advancing treatment response prediction in first-episode psychosis: Integrating clinical and electroencephalography features. Psychiatry Clin. Neurosci. https://doi.org/10.1111/pcn.13791

(2025).“),[35](https://www.nature.com/articles/s41598-025-23662-z#ref-CR35 “Numan, T. et al. Functional connectivity and network analysis during hypoactive delirium and recovery from anesthesia. Clin. Neurophysiol. https://doi.org/10.1016/j.clinph.2017.02.022

(2017).“). Given previous heterogeneous findings[7](https://www.nature.com/articles/s41598-025-23662-z#ref-CR7 “Lopez, K. L., Monachino, A. D., Vincent, K. M., Peck, F. C. & Gabard-Durnam, L. J. Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities. Neuroimage 275, 120116. https://doi.org/10.1016/j.neuroimage.2023.120116

(2023).“), we hypothesized that (1) connectivity and complexity measures would demonstrate moderate reliability across both time and resting conditions and (2) MST measures would show comparable reliability as they are based on methodologically robust approach to network characterization.

Methods

EEG recordings from healthy adults in the age range between 18 and 40 years were acquired as part of the Pan European Collaboration Antipsychotic-Naïve Studies (PECANS), ClinicalTrials.gov Identifier: NCT01154829)[36](https://www.nature.com/articles/s41598-025-23662-z#ref-CR36 “Nielsen, M. O. et al. Improvement of brain reward abnormalities by antipsychotic monotherapy in schizophrenia. Arch. Gen. Psychiatry https://doi.org/10.1001/archgenpsychiatry.2012.847

(2012).“) and the OPTIMISE STUDY (ClinicalTrials.gov Identifier: NCT01555814)[37](https://www.nature.com/articles/s41598-025-23662-z#ref-CR37 “Bojesen, K. B. et al. Treatment response after 6 and 26 weeks is related to baseline glutamate and GABA levels in antipsychotic-naïve patients with psychosis. Psychol. Med. https://doi.org/10.1017/S0033291719002277

(2020).“),[38](https://www.nature.com/articles/s41598-025-23662-z#ref-CR38 “Kahn, R. S. et al. Amisulpride and olanzapine followed by open-label treatment with clozapine in first-episode schizophrenia and schizophreniform disorder (OPTiMiSE): A three-phase switching study. Lancet Psychiatry https://doi.org/10.1016/S2215-0366(18)30252-9

(2018).“). Matching inclusion criteria and site allowed for the combination of these two datasets. Exclusion criteria included a presence or history of head injury or mental health conditions, physical illness and/or first-degree relatives with mental health conditions. Data were collected according to the declaration of Helsinki and both studies were approved by the Danish Ethical Committee. Participants signed informed consent before participating.

EEG recordings

EEG recordings were acquired using BioSemi hardware (BioSemi, Amsterdam, The Netherlands). A cap with 64active electrodes with an extended 10–20 layout was used. Sampling rate was 2048 Hz. Participants were seated in a comfortable chair in a sound-insulated room with a sound level of < 40 dB. Participants were instructed not to drink caffeine-containing beverages and/or smoke cigarettes 1 h before the recording. Participants were instructed to stay awake. EEG recordings took place between 9 AM and noon. An event-related potential (ERP) battery was recorded followed by ten minutes of eyes-closed resting-state EEG, ensuring a constant setting[39](#ref-CR39 “Düring, S., Glenthøj, B. Y., Andersen, G. S. & Oranje, B. Effects of dopamine D2/D3 blockade on human sensory and sensorimotor gating in initially antipsychotic-naive, first-episode schizophrenia patients. Neuropsychopharmacology 39(13), 3000–3008. https://doi.org/10.1038/npp.2014.152

(2014).“),[40](#ref-CR40 “Düring, S., Glenthøj, B. Y. & Oranje, B. Effects of blocking D2/D3 receptors on mismatch negativity and P3a amplitude of initially antipsychotic naïve, first episode schizophrenia patients. Int. J. Neuropsychopharmacol. 19(3), 109. https://doi.org/10.1093/ijnp/pyv109

(2016).“),[41](#ref-CR41 “Jensen, K. S., Oranje, B., Wienberg, M. & Glenthøj, B. Y. The effects of increased serotonergic activity on human sensory gating and its neural generators. Psychopharmacology 196(4), 631–641. https://doi.org/10.1007/s00213-007-1001-y

(2008).“),[42](#ref-CR42 “Oranje, B. & Glenthøj, B. Y. Clonidine normalizes sensorimotor gating deficits in patients with schizophrenia on stable medication. Schizophr. Bull. 39(3), 684–691. https://doi.org/10.1093/schbul/sbs071

(2013).“),[43](https://www.nature.com/articles/s41598-025-23662-z#ref-CR43 “Wienberg, M., Glenthoj, B. Y., Jensen, K. S. & Oranje, B. A single high dose of escitalopram increases mismatch negativity without affecting processing negativity or P300 amplitude in healthy volunteers. J. Psychopharmacol. 24(8), 1183–1192. https://doi.org/10.1177/0269881109102606

(2010).“). The ERP battery, also known as Copenhagen Psychophysiology Test Battery (CPTB), includes the startle reflex paradigm (PPI), P50 suppression, mismatch negativity (MMN), and selective attention paradigms. EEG recordings were made twice with a 6 week time interval, allowing for quantification of temporal reliability of the resting-state EEG characteristics described below. We analyzed epochs obtained during the rest periods of a P50 gating task in addition to true resting-state EEG. In this paper, we refer to the former as semi-resting-state EEG. We chose the P50 task for this semi-resting-state analysis, as there are periods of 10 s between sets of stimuli. Participants listened to three identical blocks of 40 auditory stimuli and were instructed to focus their gaze on a fixed point. These stimuli consisted of 1.5 ms-long bursts of white noise with an interstimulus interval of 500 ms. The analyses of the P50 paradigm have previously been published[39](https://www.nature.com/articles/s41598-025-23662-z#ref-CR39 “Düring, S., Glenthøj, B. Y., Andersen, G. S. & Oranje, B. Effects of dopamine D2/D3 blockade on human sensory and sensorimotor gating in initially antipsychotic-naive, first-episode schizophrenia patients. Neuropsychopharmacology 39(13), 3000–3008. https://doi.org/10.1038/npp.2014.152

(2014).“),[40](https://www.nature.com/articles/s41598-025-23662-z#ref-CR40 “Düring, S., Glenthøj, B. Y. & Oranje, B. Effects of blocking D2/D3 receptors on mismatch negativity and P3a amplitude of initially antipsychotic naïve, first episode schizophrenia patients. Int. J. Neuropsychopharmacol. 19(3), 109. https://doi.org/10.1093/ijnp/pyv109

(2016).“); in the current project we made use of the EEG data recorded in the time between the trials. Using this paradigm in addition to resting-state EEG allowed us to study the effect of different brain states affected by measurement instruction and attention level. We compared these EEG epochs with the resting-state EEG recordings, both of which were acquired on the same day. In Fig. 1, an overview of the study design is provided.

Fig. 1

Overview of study design. Resting-state and repeated resting-state EEG measurements were made with a 6 week interval. Resting-state and semi-resting-state EEG measurements were made on the same day with an approximately 20 min interval.

EEG preprocessing

The EEG data were preprocessed using functions from version 1.8.0 of the open-source MNE Python package[44](https://www.nature.com/articles/s41598-025-23662-z#ref-CR44 “Gramfort, A. et al. MEG and EEG data analysis with MNE-Python. Front. Neuroinf. 26(7), 267. https://doi.org/10.3389/fnins.2013.00267

(2013).“). This implementation is openly accessible via: https://github.com/yorbenlodema/EEG-Pype. In short, we manually selected bad channels for interpolation and selected 15 artifact-free 4-s-long epochs for further quantitative analysis. See supplements S1 for a full explanation of the approach used.

EEG measures

Data were filtered in four frequency bands: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–20 Hz)2,[45](https://www.nature.com/articles/s41598-025-23662-z#ref-CR45 “Whitham, E. M. et al. Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG. Clin. Neurophysiol. https://doi.org/10.1016/j.clinph.2007.04.027

(2007).“). We excluded frequency content above 20 Hz to reduce muscle activity[45](https://www.nature.com/articles/s41598-025-23662-z#ref-CR45 “Whitham, E. M. et al. Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG. Clin. Neurophysiol. https://doi.org/10.1016/j.clinph.2007.04.027

(2007).“). EEG features were calculated using in-house developed software in Python, using functions from the Scipy package (available via https://github.com/yorbenlodema/EEG-Pype). The reliability of FC measures (PLI and AECc)[46](#ref-CR46 “Fraschini, M., Pani, S. M., Didaci, L. & Marcialis, G. L. Robustness of functional connectivity metrics for EEG-based personal identification over task-induced intra-class and inter-class variations. Pattern Recognit. Lett. https://doi.org/10.1016/j.patrec.2019.03.025

(2019).“),[47](#ref-CR47 “Hipp, J. F., Hawellek, D. J., Corbetta, M., Siegel, M. & Engel, A. K. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. https://doi.org/10.1038/nn.3101

(2012).“),[48](https://www.nature.com/articles/s41598-025-23662-z#ref-CR48 “Stam, C. J., Nolte, G. & Daffertshofer, A. Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp. https://doi.org/10.1002/hbm.20346

(2007).“), MST characteristics based on these FC estimates using a maximum spanning tree approach[21](https://www.nature.com/articles/s41598-025-23662-z#ref-CR21 “Tewarie, P., van Dellen, E., Hillebrand, A. & Stam, C. J. The minimum spanning tree: An unbiased method for brain network analysis. Neuroimage https://doi.org/10.1016/j.neuroimage.2014.10.015

(2015).“),[49](https://www.nature.com/articles/s41598-025-23662-z#ref-CR49 “Kruskal, J. B. On the shortest spanning subtree of a graph and the traveling salesman problem (1956). Ideas that Created the Future https://doi.org/10.7551/mitpress/12274.003.0019

(2021).“), and entropy or complexity-based measures (PE and JPEINV)[18](https://www.nature.com/articles/s41598-025-23662-z#ref-CR18 “Yin, Y., Shang, P., Ahn, A. C. & Peng, C. K. Multiscale joint permutation entropy for complex time series. Phys. A: Stat. Mech. Appl. 515, 388–402. https://doi.org/10.1016/j.physa.2018.09.179

(2019).“),[50](https://www.nature.com/articles/s41598-025-23662-z#ref-CR50 “Bandt, C. & Pompe, B. Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett. 88(17), 174102. https://doi.org/10.1103/PhysRevLett.88.174102

(2002).“) were studied. As a supplementary analysis, the AECc was calculated both on separate epochs and one-minute concatenated epochs, which could result in higher reliability[15](https://www.nature.com/articles/s41598-025-23662-z#ref-CR15 “Rolle, C. E. et al. Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J. Neurosci. Methods 367, 109424. https://doi.org/10.1016/j.jneumeth.2021.109424

(2022).“). See supplement S2 for a full explanation of this analysis. MST was calculated with a maximum spanning tree approach (see supplement S1 for a full explanation). For the MST measures, we included both conventional quantitative measures and MST overlap. The MST overlap provides an additional way of quantifying MST reliability by looking at the overlap of the network backbone during different recordings and conditions and is similar to the approach by Govaarts et al.[51](https://www.nature.com/articles/s41598-025-23662-z#ref-CR51 “Govaarts, R. et al. Longitudinal changes in MEG-based brain network topology of ALS patients with cognitive/behavioural impairment, an exploratory study. Netw. Neurosci. 9, 824–841. https://doi.org/10.1162/netn_a_00450

(2025).“). See Table 1 for an overview of the included EEG measures and their description. For more details concerning the EEG preprocessing and analysis see supplement S1.

Quantification of reliability

In this paper, the term “reliability” refers to test–retest reliability. A method that is commonly used to assess reliability is the Intraclass Correlation Coefficient (ICC). This test was first introduced by Fisher52 and modern versions are often used for clinical purposes, including assessing the reliability of clinical measurements like EEG recordings[53](https://www.nature.com/articles/s41598-025-23662-z#ref-CR53 “Brunner, J. F. et al. Long-term test-retest reliability of the P3 NoGo wave and two independent components decomposed from the P3 NoGo wave in a visual Go/NoGo task. Int. J. Psychophysiol. 89(1), 106–114. https://doi.org/10.1016/j.ijpsycho.2013.06.005

(2013).“),[54](https://www.nature.com/articles/s41598-025-23662-z#ref-CR54 “Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

(2016).“). Compared to Pearson’s correlation, ICC is less sensitive to bias[54](https://www.nature.com/articles/s41598-025-23662-z#ref-CR54 “Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

(2016).“). We used a 2-way mixed-effects ICC model[54](https://www.nature.com/articles/s41598-025-23662-z#ref-CR54 “Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

(2016).“). The level of reliability was based on the 95% confidence interval of the ICC: reliability scores lower than 0.50 were considered ‘poor’, scores from 0.50 to 0.75 ‘moderate’, scores from 0.76 to 0.90 ‘good’, and scores above 0.90 ‘excellent’[54](https://www.nature.com/articles/s41598-025-23662-z#ref-CR54 “Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

(2016).“). ICC scores were calculated for all EEG measures except MST overlap. This comparison was made over time for the repeated resting-state EEG, using the resting-state and follow-up after six weeks. We also calculated the ICC scores across resting-state conditions, comparing resting-state and semi-resting-state data. To assess whether resting-state EEG is stable and relatively state-independent, we interpreted the four different possible combinations of high and low reliability together for the two comparisons. The following combinations are possible:

High state reliability and high temporal reliability: possible biomarker.

Low state reliability and high temporal reliability: possible biomarker, sensitive to resting-state instructions.

High state reliability and low temporal reliability: possible cross-sectional marker, sensitive to time-variable factors.

Low state reliability and low temporal reliability: poor reliability.

Results

The reliability analysis over time included 42 participants, while 24 participants were included for the reliability analysis across resting-states. The mean age of participants was 24.2 years (SD 5.4) for the group that completed the repeated resting-state measurement and 26.0 years (SD 6.5) for the group that additionally completed the semi-resting-state condition. In the resting-state group 61.9% (N = 26) were male compared to 70.8% (N = 17) in the semi-resting-state group. See supplement S3 for 95% confidence intervals of all measures based on non-parametric bootstrapping with 1000 resamples. Note that these intervals vary across the different measures and tend to be wider for the across states comparison due to the smaller sample size.

Phase lag index

At sensor level, the PLI showed moderate reliability in the theta and alpha bands across time. The PLI showed poor delta and beta band reliability. When compared between resting-state and semi-resting-state conditions, the PLI showed excellent reliability in the theta band, good reliability in the alpha band and moderate reliability in the delta and beta band.

At source level, the PLI showed moderate reliability in the alpha and beta band when compared across time. In the delta and theta band the PLI showed poor reliability for this comparison. The PLI showed moderate to good reliability in the theta, alpha and beta bands and poor reliability in the delta band when compared across resting-states at source level. See Figs. 2, 3 and 4 for an overview of the results.

Fig. 2

Temporal and state reliability of EEG measures at sensor and source level. Intraclass correlation coefficients (ICC) for EEG measures across time (6 week interval) and states (resting state vs. semi-resting-state) at both sensor level (A) and source level (B). Reliability scores lower than 0.50 were considered ‘poor’, scores from 0.50 to 0.75 as ‘moderate’, scores from 0.76 to 0.90 as ‘good’, and scores above 0.90 were considered ‘excellent’ (Koo & Li, 2016). Measures include phase lag index (PLI), corrected amplitude envelope correlation (AECc), permutation entropy (PE), and joint permutation entropy (JPEINV) across different frequency bands (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–20 Hz). MST minimum spanning tree.

Fig. 3

Scatterplot of measures across state and across time at sensor level. The ICCs for all MST measures, except for those for the PLI and AECc MST were averaged (‘overall’), as they all showed poor reliability. Delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–20 Hz). PLI phase lag index, AECc corrected amplitude envelope correlation, MST minimum spanning tree, PE permutation entropy, JPE joint permutation entropy, ICC intraclass correlation coefficients, inv inverted.

Fig. 4

Scatterplot of measures across state and across time at source level. The ICCs for all MST measures, except for those for the PLI and AECc MST, were averaged (‘overall’), as they all, except for MST leaf fraction (based on alpha AECc), showed poor reliability. Delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–20 Hz). PLI phase lag index, AECc corrected amplitude envelope correlation, MST minimum spanning tree, PE permutation entropy, JPE joint permutation entropy, ICC intraclass correlation coefficients, inv inverted.

Fig. 5

Temporal and state reliability of minimum spanning tree measures at sensor and source level. Intraclass correlation coefficients (ICC) for minimum spanning tree (MST) measures across time (6 week interval) and states (resting state vs. semi-resting-state) at both sensor level (A) and source level (B). Reliability scores lower than 0.50 were considered ‘poor’, scores from 0.50 to 0.75 as ‘moderate’, scores from 0.76 to 0.90 as ‘good’, and scores above 0.90 were considered ‘excellent’ (Koo & Li, 2016). MST measures were derived from both PLI and AECc connectivity matrices and include leaf fraction (LF), maximum degree (k), diameter (D), maximum betweenness centrality (BC), eccentricity (ECC), and tree hierarchy (Th) across different frequency bands (delta: 0.5–4 Hz, theta: 4–8 Hz, alpha: 8–13 Hz, beta: 13–20 Hz). PLI phase lag index, AECc corrected amplitude envelope correlation.

Corrected amplitude envelope correlation

The AECc showed moderate reliability in the beta band compared across time at sensor level, while all other AECc frequency bands demonstrated poor reliability. Across-states comparisons at sensor level showed moderate AECc reliability in the alpha band, and poor reliability in the delta, theta, and beta bands.

At source level, the AECc demonstrated moderate reliability over time in the theta, alpha and beta band, and poor reliability in the delta band. The reliability across states was moderate-to-good in the alpha and beta band, but poor in the delta and theta band. See Figs. 2, 3 and 4 for an overview of the results. Concatenating epochs before calculating AECc yields mixed results and does not provide a substantial overall improvement. While concatenation may slightly increase reliability for state-level analyses in sensor space, it often performs comparably or significantly worse in other contexts, particularly at the source level. See Supplements S2 for full results.

Permutation entropy and joint permutation entropy (inversed)

At sensor level, the PE showed good reliability over time in the theta and alpha bands, moderate in the beta band, and poor in the delta band. For JPEINV, reliability over time was moderate in the alpha band, but poor in the theta, beta, and delta bands. Across states, the reliability of PE was good in the theta and alpha bands, moderate in the beta band, and poor in the delta band. In contrast, JPEINV showed poor reliability across states in all four frequency bands.

At source level, the PE demonstrated good reliability in the theta and alpha bands and moderate reliability in the beta band when compared across time; PE also showed poor reliability in the delta band across time. The JPEINV showed moderate reliability when compared across time in the theta, alpha, and beta bands, and poor reliability in the delta band. When compared across resting-states at source level, the PE showed moderate reliability in the delta and beta bands and good reliability in the theta and alpha bands. The JPEINV showed poor reliability across states for all frequency bands at the source level for this comparison. See Figs. 2, 3 and4 for an overview of the results.

Minimum spanning tree

Several MST measures showed moderate reliability at sensor level. When compared across time, this included the AECc degree (k) in the alpha band and PLI Leaf fraction (LF) in the theta band. All other variables showed poor reliability for this comparison. When comparing across resting-states, some measures, mostly in the theta and alpha band, showed moderate to good reliability. This included the following measures: Leaf fraction (based on theta PLI, delta AECc, theta AECc, alpha PLI, alpha AECc, beta AECc), the maximum degree (based on theta PLI and alpha PLI), the diameter (based on delta AECc, theta PLI and alpha PLI) and eccentricity (based on delta AECc, theta PLI and alpha PLI) and tree hierarchy (based on beta AECc). See Figs. 3, 4 and 5 for a complete overview of these results.

At source level, the MST leaf fraction (based on alpha AECc) showed moderate reliability when compared across time. All other features showed poor reliability for this analysis.

MST overlap

The MST overlap analysis generally showed low percentages of overlap between the MST matrix elements of AECc and PLI MST matrices for both comparisons. The overlap ranged from 2.7 to 10.8%, with the highest overlap in the alpha band, indicating that on average, no more than 6.8 edges were consistently present in this backbone network in repeated EEG registrations. Network reconstruction in source space led to less overlap between networks with a maximum overlap of 4.7% or 3.0 edges. See Table 2 for a complete overview of these results.

Discussion

The present study investigated the reliability of quantitative FC, complexity and MST EEG measures. Frequency-dependent reliability was assessed between repeated resting-state EEG measurements over time and between resting-state and semi-resting-state EEG recordings at the same time point, both for sensor-level data and after source reconstruction. The reliability of FC measures varied depending on the measure, frequency band, and application of source reconstruction. Overall, we found moderate to good reliability for FC and complexity measures, where the PE showed highest reliability, but low reliability for MST measures. The results for FC measures were in line with our hypothesis, while PE outperformed our expectation and MST measures performed worse than hypothesized.

In sensor‐level analyses, PLI exhibited somewhat higher ICC values than AECc in the theta and alpha bands, though this pattern varied in other frequency bands and across different states. Additionally, measures in the theta and alpha bands showed higher reliability than in the delta and beta bands. Overall, when comparing ICCs across time and across resting-states, neither was clearly higher than the other. ICCs were slightly higher at sensor level than at source level.

Combining findings of reliability across time and across states, we can categorize measures in terms of their relative state-dependence. The theta and alpha PE showed high reliability across time and states, at both sensor and source level. This indicates PE exhibits high stability across different resting-state conditions, which include both cognitive factors and eye state, essential properties for a potential biomarker. Notably, JPEINV showed lower reliability than PE in all comparisons. It is conceivable that JPEINV is sensitive to non-stationary, metastable brain dynamics[18](https://www.nature.com/articles/s41598-025-23662-z#ref-CR18 “Yin, Y., Shang, P., Ahn, A. C. & Peng, C. K. Multiscale joint permutation entropy for complex time series. Phys. A: Stat. Mech. Appl. 515, 388–402. https://doi.org/10.1016/j.physa.2018.09.179

(2019).“),[55](https://www.nature.com/articles/s41598-025-23662-z#ref-CR55 “Tognoli, E. & Kelso, J. S. The metastable brain. Neuron 81(1), 35–48. https://doi.org/10.1016/j.neuron.2013.12.022

(2014).“) which could manifest as lower test-retest reliability. Additionally, it is worth noting that JPEINV is a relatively new FC measure, which means little is known about the influence of factors such as epoch length, signal-to-noise ratio and different parameter settings on reliability. Current evidence does not support the use of JPEINV as a reliable disease marker, though this result can be considered preliminary. Sensor-level theta and alpha PLI showed moderate temporal reliability combined with high reliability across states, implicating PLI as relatively state-independent. All remaining MST measures at source-level showed poor reliability, with low reliability across time and state. It is noteworthy that the task paradigm included eyes open EEG compared to the eyes closed resting-state recordings. Relating to this difference, it has been found that spectral measures in eyes closed resting-state EEG are more stable than in eyes open resting-state EEG[56](https://www.nature.com/articles/s41598-025-23662-z#ref-CR56 “Corsi-Cabrera, M., Galindo-Vilchis, L., Del-Río-Portilla, Y., Arce, C. & Ramos-Loyo, J. Within-subject reliability and inter-session stability of EEG power and coherent activity in women evaluated monthly over nine months. Clin. Neurophysiol. 118(1), 9–21. https://doi.org/10.1016/j.clinph.2006.08.013

(2007).“),[57](https://www.nature.com/articles/s41598-025-23662-z#ref-CR57 “Rogers, J. M., Johnstone, S. J., Aminov, A., Donnelly, J. & Wilson, P. H. Test-retest reliability of a single-channel, wireless EEG system. Int. J. Psychophysiol. 106, 87–96. https://doi.org/10.1016/j.ijpsycho.2016.06.006

(2016).“) and the spatial pattern of oscillatory activity is different between these conditions[4](https://www.nature.com/articles/s41598-025-23662-z#ref-CR4 “Barry, R. J., Clarke, A. R., Johnstone, S. J., Magee, C. A. & Rushby, J. A. EEG differences between eyes-closed and eyes-open resting conditions. Clin. Neurophysiol. 118(12), 2765. https://doi.org/10.1016/j.clinph.2007.07.028

(2007).“). Nevertheless, eyes open and eyes closed resting-state EEG differ only marginally in terms of reliability when looking at functional connectivity[15](https://www.nature.com/articles/s41598-025-23662-z#ref-CR15 “Rolle, C. E. et al. Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J. Neurosci. Methods 367, 109424. https://doi.org/10.1016/j.jneumeth.2021.109424

(2022).“). Despite this, there is a possibility that some of the EEG measures included in the current study are more sensitive to changes related to eyes open resting-state EEG than others.

There is limited literature to relate our current findings regarding reliability of these EEG measures, but Hommelsen et al. were able to identify an individual neural finger print with high accuracies using a machine learning model based on power spectral density[27](https://www.nature.com/articles/s41598-025-23662-z#ref-CR27 “Hommelsen, M., Viswanathan, S. & Daun, S. Robustness of individualized inferences from longitudinal resting state EEGdynamics. Eur. J. Neurosci. 56(1), 3613. https://doi.org/10.1111/ejn.15673

(2022).“). Interestingly, this was possible both for longitudinal resting-state data and for resting-state versus semi-resting-state data, indicating relative state-independence of the power spectral density when using