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Exploring the distribution of connectivity weights in resting-state EEG networks

T0 review · 1 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that functional connectivity weights in resting-state EEG networks are right-skewed, platykurtic, and relatively uniform regardless of channel density or coupling measure, with volume conduction explaining which measures…

desk verdict A solid descriptive study with a real new observation—right-skewed EEG connectivity weights across montages and measures—but the Shannon entropy numbers for signed measures rest on an undocumented transform, and the simulation needs a repetition count. read the letter →

arxiv 2501.07394 v2 pith:XPVZIXWY submitted 2025-01-13 cs.HC

Shiang Hu , Xiao Gong , Xiaolong Huang , Jie Ruan , Pedro Antonio Valdes-Sosa This is my paper · ORCID
classification cs.HC
keywords resting-stateEEGfunctionalconnectivityweightdistributionskewnesskurtosisShannonentropyvolumeconductionchanneldensity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the raw weights of fully connected resting-state EEG networks follow a stable statistical shape before the usual thresholding step that discards weak links. Using forward simulations of scalp EEG at 19, 32, 64, and 128 channels and five coupling measures, it reports that connection weights are consistently right-skewed, flatter than a normal distribution, and relatively uniform, and that channel density and coupling choice barely change that shape. It then validates the main pattern on a large normative EEG database using coherence and imaginary coherence across frequency bands. If the shape is genuinely stable, descriptors of the weight distribution become a principled baseline for network thresholding, data quality control, and cross-study or clinical comparisons.

What carries the argument

The argument is carried by three shape descriptors computed on the upper-triangle entries of each symmetric functional-connectivity matrix: skewness (third standardized moment), kurtosis (fourth standardized moment), and Shannon entropy of a 100-bin histogram over the weight range [0,1], normalized so 0 means clustered and 1 means uniform. These descriptors compress an entire network into a few numbers that can be compared across montages and coupling measures. The volume-conduction effect is identified by contrasting measures known to be sensitive to it (coherence, phase-locking value, amplitude envelope correlation) with measures designed to suppress it (imaginary part of coherency, phase-lag index); the stability claim rests on the descriptors staying in the same qualitative range across all combinations.

What would settle it

Recompute skewness, kurtosis, and Shannon entropy for the simulated imaginary-coherence and amplitude-envelope-correlation networks after an explicit nonnegative transformation such as taking absolute values or shifting weights to [0,1]; if the right skew, kurtosis below 3, or entropy ranges change materially, the claimed independence from coupling measure is an artifact of the missing handling step.

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Extended reading notes

Core claim

The central claim is that the distribution of functional connectivity weights in resting-state scalp EEG is a stable feature: it is right-skewed (positive skewness), platykurtic (kurtosis below 3), and relatively uniform as measured by Shannon entropy, and this shape does not depend on whether the network is built from 19, 32, 64, or 128 channels or from any of five coupling measures (coherence, imaginary part of coherency, phase-locking value, phase-lag index, amplitude envelope correlation). The paper further claims that volume conduction, the passive spread of one brain source across many electrodes, separates the measures: for coherence, phase-locking value, and amplitude envelope correlation the mean connection weight correlates strongly with skewness, kurtosis, and entropy, whereas for the volume-conduction-robust imaginary coherence and phase-lag index these correlations weaken and the entropy is lower. The normative-data validation with coherence and imaginary coherence reproduces the right skew, the negative mean-kurtosis correlation, and the frequency-band pattern, with alpha-band networks showing larger weights and a more uniform distribution.

Load-bearing premise

The load-bearing assumption is that every connection weight can be treated as a number in [0,1] when its distribution is binned for Shannon entropy; two of the coupling measures, the imaginary part of coherency and amplitude envelope correlation, can produce negative values, and the paper never states how those negatives were folded into the analysis.

Editorial extensions

If this is right

  • Thresholding a resting-state network by keeping only strong links removes the long right tail of a right-skewed weight distribution, so the surviving network is a systematically selected tail rather than a random subsample of connections.
  • Because the right skew and the mean-entropy correlation persist from 19 to 128 channels, distribution shape can serve as a montage-invariant reference when comparing studies that used different electrode densities.
  • For volume-conduction-sensitive measures, the mean connection weight carries information about skewness and kurtosis, while for volume-conduction-robust measures it does not, so mean-based shortcuts must be interpreted separately for each measure.
  • The lower Shannon entropy of imaginary-coherence and phase-lag-index networks implies that volume-conduction-robust measures expose a less uniform weight structure that may reflect true neural coupling more directly.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the right-skewed shape is genuinely invariant, it supplies a null model for anomaly detection: a patient resting-state network whose skewness-entropy relationship departs from the expected curve could be flagged for clinical scrutiny without needing a matched control group.
  • The paper's own remark that source-level connectivity may be more right-skewed and leptokurtic suggests the scalp-level uniformity is partly a volume-conduction smoothing effect; recomputing the same descriptors on source-reconstructed EEG would separate smoothing from genuine coupling structure.
  • A direct audit of how negative imaginary-coherence and envelope-correlation values were handled before binning would settle whether the claimed invariance across coupling measures survives an explicit nonnegative transformation.
  • Applying the same simulation-plus-validation workflow to MEG or fNIRS data would show whether the stable shape is a general electrophysiological property or specific to scalp EEG.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 4 minor

Summary. The paper investigates the statistical distribution of connectivity weights in fully connected resting-state EEG networks. Using a forward-model simulation with realistic intracranial EEG sources and four electrode montages (19, 32, 64, 128 channels), the authors construct networks with five coupling measures (COH, iCOH, PLV, PLI, AEC) and quantify the weight distributions via mean, skewness, kurtosis, and Shannon entropy. They report that the weights are right-skewed regardless of channel density or coupling measure, that the distributions are relatively uniform, and that volume conduction affects the degree of uniformity and the correlation between mean connection weight and distribution shape. The simulation findings are then validated in a large normative EEG database using COH and iCOH.

Significance. If the main claims hold, the paper offers a potentially useful empirical regularity: the shape of the functional connectivity weight distribution in resting-state scalp EEG is stable across electrode density and coupling metrics, with a documented role for volume conduction. The strengths of the study include the use of realistic iEEG-derived sources, a forward-modeling pipeline, multiple channel densities and coupling measures, and external validation on a large multicenter normative database. However, the central uniformity comparison currently rests on a questionable computation of Shannon entropy for signed connectivity measures, and the simulation's reproducibility is undermined by an unstated number of realizations. These issues need to be resolved before the conclusions can be accepted.

major comments (1)
  1. [Section 2.1.1] The simulation description reports a single random selection of 200 iEEG sources with 10,000 samples each, but no repetition count. Figures 3-5 show scatter plots with many points per condition, implying either multiple random source selections, multiple noise draws, or both. As written, the statistical results (Pearson correlations in Figs. 3-5) are not reproducible and the scatter density cannot be assessed. Please state the number of independent realizations and, if only one was used, explain how the scatter points in each subplot were generated.
minor comments (4)
  1. [Section 3.1.2] The heading states that the network connection weights 'display a leptokurtic distribution,' but the text and Fig. 4 indicate kurtosis consistently less than 3, which is platykurtic. Please correct the heading to match the reported results.
  2. [Section 3.1.2] In the final sentence of this section, the text says there is no correlation between kurtosis and mean connectivity weights for 'iCOH and PLV,' but the preceding sentence refers to COH, PLV, and AEC; the intended contrast is presumably with iCOH and PLI, as shown in Fig. 4. Please correct this inconsistency.
  3. [Section 2.4.2] Equation (8) appears garbled in the manuscript ('2 12 1SE log ( )log N i i i P P N = −= ∑'). Please reformat the equation so that the summation and normalization are clearly displayed.
  4. [Discussion] The phrase 'volumetric conduction' appears in the paragraph discussing Fig. 6 and Fig. 7; this should read 'volume conduction' for consistency with the rest of the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the distributional findings are direct empirical computations on simulation data and an external normative database, with no fitted parameter renamed as a prediction.

full rationale

The paper does not derive distributional claims from fitted constants. The simulation chain (Section 2.1) generates scalp EEG from iEEG sources and a forward model; Section 2.3 computes five coupling measures; Section 2.4 computes MCW, skewness, kurtosis, and Shannon entropy directly from the upper triangle of each connectivity matrix. Nothing in these computations is fitted to the normative data or to the target conclusions. The normative validation (Section 3.2) uses the publicly available HarMNqEEG database [22]; although some authors of this paper are co-authors of the database paper, the database itself is an external, published, multicenter dataset, and the present paper fits no parameters to it. The only possible self-citation concern is therefore not load-bearing. A methodological caveat exists in Section 2.4.2, where the histogram support is fixed to [0,1] while iCOH and AEC can in principle take negative values; however, this is a question of internal consistency and correctness, not circularity, because the entropy values are not defined in terms of the conclusions they are used to support.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a simulation pipeline whose parameters (noise amplitude, number of sources, window sizes, bin count) are chosen but not systematically varied. The most load-bearing assumption is that all connectivity weights lie in [0,1], which is not true for iCOH and AEC without an unstated transformation. The paper introduces no new theoretical entities.

free parameters (4)
  • Number of histogram bins for Shannon entropy = 100
    Section 2.4.2 sets the bin count to 100 over [0,1]. This choice affects entropy values but is not tested for sensitivity.
  • Noise amplitude for simulated dipole activity = not reported
    Section 2.1.1 describes 'low-intensity Gaussian noise' on 2802 dipoles without specifying the intensity, which could influence connectivity distributions.
  • Number of active signal sources = 200
    Section 2.1.1 randomly selects 200 iEEG sources; the results may depend on this number, which is not varied.
  • Sliding window size for PLV/PLI/AEC = 6 seconds, overlap 0.5 seconds for PLI/AEC, no overlap for PLV
    Section 2.3.4 specifies window parameters without sensitivity analysis; these affect connectivity estimates.
assumptions (5)
  • domain assumption The forward solution (OpenMEEG BEM) accurately maps cortical sources to scalp EEG for all electrode montages.
    Section 2.1.2 relies on this to generate realistic simulated scalp EEG from iEEG sources.
  • domain assumption The random selection of 200 iEEG sources plus low-intensity Gaussian noise on remaining dipoles produces resting-state-like cortical activity.
    Section 2.1.1; this source model underlies all simulated connectivity distributions.
  • domain assumption COH, PLV, and AEC are influenced by volume conduction while iCOH and PLI are not, as classified by prior literature.
    Section 4 uses this classification to interpret why correlations differ across measures.
  • ad hoc to paper Shannon entropy with 100 bins over [0,1] adequately quantifies uniformity of connection weights.
    Section 2.4.2; the bin count is chosen without sensitivity analysis, and assumes all weights are in [0,1].
  • domain assumption Normative database after excluding the Cz-referenced site is a valid representation of resting-state EEG.
    Section 2.2; exclusion is justified by reference distortion but not empirically validated.

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Pith. "Pith review of Exploring the distribution of connectivity weights in resting-state EEG networks." pith.science (2026). https://pith.science/paper/XPVZIXWY

@misc{pith2026250107394,
  author       = {Pith},
  title        = {Pith review of: Exploring the distribution of connectivity weights in resting-state EEG networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XPVZIXWY}},
  note         = {Machine review of arXiv:2501.07394}
}
read the original abstract

The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brain modules, providing essential foundations for decoding intrinsic neural information within the brain. It serves as one of the primary tools for describing the spatial dynamics of the brain using various neuroimaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). However, the distribution rules or potential modes of functional connectivity weights in the resting state remain unclear. In this context, we first start from simulation, using forward solving model to generate scalp EEG with four channel densities (19, 32, 64, 128). Subsequently, we construct scalp brain networks using five coupling measures, aiming to explore whether different channel density or coupling measures affect the distribution pattern of functional connectivity weights. Next, we quantify the distribution pattern by calculating the skewness, kurtosis, and Shannon entropy of the functional connectivity network weights. Finally, the results of the simulation were validated in a normative database. We observed that: 1) The functional connection weights exhibit a right-skewed distribution, and are not influenced by channel density or coupling measures; 2) The functional connection weights exhibit a relatively uniform distribution, with the potential for volume conduction to affect the degree of uniformity in the distribution; 3) Networks constructed using coupling measures influenced by volume conduction exhibit significant correlations between the average connection weight and measures of skewness, kurtosis, and Shannon entropy. This study contributes to a deeper understanding of RSNs, providing valuable insights for research in the field of neuroscience, and holds promise for being associated with brain cognition and disease diagnosis.

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Works this paper leans on

52 extracted references · 37 canonical work pages

  1. [1]

    Introduction The network is a collection of nodes and links between nodes, serving as a mathematical representation of relational information among populations[1], characterized by vast and intricate multidimensional spatial systems. Brain networks typically utilize electrodes (on the scalp) or brain regions (on the cortex) as vertices, with the coupling ...

    work page

  2. [2]

    The Montreal Neurological Institute and Hospital (MNI) has released iEEG data extracted from normal brain regions of 106 epilepsy patients, accumulating a total of 1785 channels

    Materials and methods 2.1 Simulation 2.1.1 Intracranial EEG To ensure that the simulated data exhibit realistic spectral characteristics of EEG, cortical activity was modeled using real intracranial EEG (iEEG) signals[18]. The Montreal Neurological Institute and Hospital (MNI) has released iEEG data extracted from normal brain regions of 106 epilepsy pati...

    work page

  3. [3]

    Results 3.1 Simulation analysis 3.1.1 The network connection weights exhibit a right-skewed distribution The combined effect of skewness and mean can quantify the symmetry of the distribution of network connection weights[12]. Fig. 3 illustrated the findings of skewness and mean connectivity weights (MCW) for all combinations of electrode montage and conn...

    work page

  4. [4]

    Discussion The present study comprehensively investigates the distribution patterns of functional connectivity weights in the resting- state brain networks, both through simulation and normative data validation. This includes exploring potential factors influencing these distribution patterns, such as the number of electrodes (19, 32, 64, 128) and couplin...

    work page

  5. [5]

    Our findings indicate that resting-state network connection weights exhibit a right-skewed distribution, unaffected by channel density and coupling metrics

    Conclusion In this study, we constructed brain networks using five coupling metrics and quantified the distribution patterns of network connection weights using skewness, kurtosis, and Shannon entropy, validated on normative data. Our findings indicate that resting-state network connection weights exhibit a right-skewed distribution, unaffected by channel...

    work page

  6. [6]

    Cell Reports Medicine

    Müller-Dahlhaus, F., Bergmann, T.O.: Network perturbation-based biomarkers of depression and treatment response. Cell Reports Medicine. 4, 101086 (2023). https://doi.org/10.1016/j.xcrm.2023.101086

    arXiv 2023

  7. [7]

    NeuroImage

    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. NeuroImage. 52, 1059–1069 (2010). https://doi.org/10.1016/j.neuroimage.2009.10.003

    doi:10.1016/j.neuroimage.2009.10.003 2010

  8. [8]

    Nat Rev Neurosci

    Avena-Koenigsberger, A., Misic, B., Sporns, O.: Communication dynamics in complex brain networks. Nat Rev Neurosci. 19, 17–33 (2018). https://doi.org/10.1038/nrn.2017.149

    work page doi:10.1038/nrn.2017.149 2018

Show all 52 references
  1. [9]

    Trends in Cognitive Sciences

    Zuo, X.-N., He, Y ., Betzel, R.F., Colcombe, S., Sporns, O., Milham, M.P .: Human Connectomics across the Life Span. Trends in Cognitive Sciences. 21, 32–45 (2017). https://doi.org/10.1016/j.tics.2016.10.005

    2017 doi

  2. [10]

    IEEE Trans

    Li, P ., Liu, H., Si, Y ., Li, C., Li, F., Zhu, X., Huang, X., Zeng, Y ., Yao, D., Zhang, Y ., Xu, P .: EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations. IEEE Trans. Biomed. Eng. 66, 2869 –2881 (2019). https://doi.org/10.1109/TBM...

    2019

  3. [11]

    NeuroImage: Clinical

    Cecchetti, G., Agosta, F., Basaia, S., Cividini, C., Cursi, M., Santangelo, R., Caso, F., Minicucci, F., Magnani, G., Filippi, M.: Resting-state electroencephalographic biomarkers of Alzheimer’s disease. NeuroImage: Clinical. 31, 102711 (2021). https://doi.org/10.1016/j.nicl.2...

    2021

  4. [12]

    Hu, S., Ruan, J., Langer, N., Bosch-Bayard, J., Lv, Z., Yao, D., Valdes-Sosa, P .A.: Spectral homogeneity cross frequencies 17 can be a quality metric for the large-scale resting EEG preprocessing, http://arxiv.org/abs/2310.11994, (2023)

    2023 arXiv

  5. [13]

    NeuroImage

    Van Den Heuvel, M.P ., De Lange, S.C., Zalesky, A., Seguin, C., Yeo, B.T .T., Schmidt, R.: Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations. NeuroImage. 152, 437...

    2017 doi

  6. [14]

    Boschi, A., Brofiga, M., Massobrio, P .: Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large -Scale Neuronal Networks. Front. Neurosci. 15, 705103 (2021). https://doi.org/10.3389/fnins.2021.705103

    2021

  7. [15]

    -M.: A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

    Bastos, A.M., Schoffelen, J. -M.: A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls. Front. Syst. Neurosci. 9, (2016). https://doi.org/10.3389/fnsys.2015.00175

    2016

  8. [16]

    NeuroImage

    Pellegrino, G., Schuler, A.-L., Arcara, G., Di Pino, G., Piccione, F., Kobayashi, E.: Resting state network connectivity is attenuated by fMRI acoustic noise. NeuroImage. 247, 118791 (2022). https://doi.org/10.1016/j.neuroimage.2021.118791

    2022

  9. [17]

    Hu, S., Ruan, J., Hou, J., Valdes-Sosa, P .A., Lv, Z.: How do the resting EEG preprocessing states affect the outcomes of postprocessing? arXiv preprint arXiv:2310.15194. (2023)

    2023 arXiv

  10. [18]

    Anzolin, A., Toppi, J., Petti, M., Cincotti, F., Astolfi, L.: SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms. Sensors. 21, 3632 (2021). https://doi.org/10.3390/s21113632

    2021 doi

  11. [19]

    NeuroImage

    Allouch, S., Kabbara, A., Duprez, J., Khalil, M., Modolo, J., Hassan, M.: Effect of channel density, inverse solutions and connectivity measures on EEG resting -state networks reconstruction: A simulation study. NeuroImage. 271, 120006 (2023). https://doi.org/10.1016/j.neuroim...

    2023

  12. [20]

    150, 1–16 (2023)

    Hatlestad-Hall, C., Bruña, R., Liljeström, M., Renvall, H., Heuser, K., Taubøll, E., Maestú, F., Haraldsen, I.H.: Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clinical Neurophysiology. 150, 1–16 (...

    2023 doi

  13. [21]

    Chaos: An Interdisciplinary Journal of Nonlinear Science

    Yu, M.: Benchmarking metrics for inferring functional connectivity from multi -channel EEG and MEG: A simulation study. Chaos: An Interdisciplinary Journal of Nonlinear Science. 30, 123124 (2020). https://doi.org/10.1063/5.0018826

    2020 doi

  14. [22]

    Adamovich, T., Zakharov, I., Tabueva, A., Malykh, S.: The thresholding problem and variability in the EEG graph network parameters. Sci Rep. 12, 18659 (2022). https://doi.org/10.1038/s41598-022-22079-2

    2022 doi

  15. [23]

    Revilla-Vallejo, M., Poza, J., Gomez -Pilar, J., Hornero, R., Tola -Arribas, M.Á., Cano, M., Gómez, C.: Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer’s Disease. Entropy. 23, 500 (2021). https://doi.org/10.3390/e23050500

    2021 doi

  16. [24]

    Brain Topogr

    Hu, S., Yao, D., Bringas-Vega, M.L., Qin, Y ., Valdes-Sosa, P .A.: The Statistics of EEG Unipolar References: Derivations and Properties. Brain Topogr. 32, 696–703 (2019). https://doi.org/10.1007/s10548-019-00706-y

    2019 doi

  17. [25]

    Frauscher, B., Von Ellenrieder, N., Zelmann, R., Doležalová, I., Minotti, L., Olivier, A., Hall, J., Hoffmann, D., Nguyen, D.K., Kahane, P ., Dubeau, F., Gotman, J.: Atlas of the normal intracranial electroencephalogram: neurophysiological awake activity in different cortical ...

    2018 doi

  18. [26]

    BioMed Eng OnLine

    Gramfort, A., Papadopoulo, T., Olivi, E., Clerc, M.: OpenMEEG: opensource software for quasistatic bioelectromagnetics. BioMed Eng OnLine. 9, 45 (2010). https://doi.org/10.1186/1475-925X-9-45

    2010 doi

  19. [27]

    Computational Intelligence and Neuroscience

    Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: A User -Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience. 2011, 1–13 (2011). https://doi.org/10.1155/2011/879716

    2011 doi

  20. [28]

    NeuroImage

    Li, M., Wang, Y ., Lopez-Naranjo, C., Hu, S., Reyes, R.C.G., Paz-Linares, D., Areces-Gonzalez, A., Hamid, A.I.A., Evans, A.C., Savostyanov, A.N., Calzada-Reyes, A., Villringer, A., Tobon-Quintero, C.A., Garcia-Agustin, D., Yao, D., Dong, L., Aubert- Vazquez, E., Reza, F., Razz...

    2022

  21. [29]

    Chella, F., Pizzella, V., Zappasodi, F., Marzetti, L.: Impact of the reference choice on scalp EEG connectivity estimation. J. Neural Eng. 13, 036016 (2016). https://doi.org/10.1088/1741-2560/13/3/036016

    2016 doi

  22. [30]

    Human Brain Mapping

    Samogin, J., Marino, M., Porcaro, C., Wenderoth, N., Dupont, P ., Swinnen, S.P ., Mantini, D.: Frequency-dependent functional connectivity in resting state networks. Human Brain Mapping. 41, 5187 –5198 (2020). https://doi.org/10.1002/hbm.25184

    2020 doi

  23. [31]

    Clinical Neurophysiology

    Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., Hallett, M.: Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical Neurophysiology. 115, 2292 –2307 (2004). https://doi.org/10.1016/j.clinph.2004.04.029

    2004 doi

  24. [32]

    Lachaux, J.-P ., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208 (1999). https://doi.org/10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C

    1999 doi

  25. [33]

    Nolte, G., Galindo-Leon, E., Li, Z., Liu, X., Engel, A.K.: Mathematical Relations Between Measures of Brain Connectivity Estimated From Electrophysiological Recordings for Gaussian Distributed Data. Front. Neurosci. 14, 577574 (2020). https://doi.org/10.3389/fnins.2020.577574

    2020

  26. [34]

    Human Brain Mapping

    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. Human Brain Mapping. 28, 1178 –1193 (2007). https://doi.org/10.1002/hbm.20346

    2007 doi

  27. [35]

    11, 1509–1514 (2000)

    Bruns, A., Eckhorn, R., Jokeit, H., Ebner, A.: Amplitude envelope correlation detects coupling among incoherent brain signals: NeuroReport. 11, 1509–1514 (2000). https://doi.org/10.1097/00001756-200005150-00029 18

    2000 doi

  28. [36]

    M, J.M.J., Sudha, G.F., R, N.: Towards non-invasive PTSD diagnosis: Utilising EEG based Emotion Recognition with the DEAP Database, https://www.researchsquare.com/article/rs-4292055/v1, (2024)

    2024

  29. [37]

    Daly, I., Pichiorri, F., Faller, J., Kaiser, V., Kreilinger, A., Scherer, R., Muller-Putz, G.: What does clean EEG look like? In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 3963–3966. IEEE, San Diego, CA (2012)

    2012

  30. [38]

    Biomedical Signal Processing and Control

    Shen, M., Wen, P ., Song, B., Li, Y .: Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. Biomedical Signal Processing and Control. 82, 104566 (2023). https://doi.org/10.1016/j.bspc.2022.104566

    2023

  31. [39]

    In: 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP)

    Sahraei-Ardakani, M., Soltanian-Zadeh, H.: A graph theoretical analysis of brain functional network in temporal lobe epilepsy patients. In: 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP). pp. 099 –104. IEEE, Isfahan, Iran (2017)

    2017

  32. [40]

    Bioengineering

    Shen, M., Zhang, L., Gong, Y ., Li, L., Liu, X.: Epileptic Tissue Localization through Skewness -Based Functional Connectivity in the High -Frequency Band of Intracranial EEG. Bioengineering. 10, 461 (2023). https://doi.org/10.3390/bioengineering10040461

    2023 doi

  33. [41]

    Revilla-Vallejo, M., Gómez, C., Gomez-Pilar, J., Hornero, R., Ángel Tola-Arribas, M., Cano, M., Shigihara, Y ., Hoshi, H., Poza, J.: Quantification of the robustness of functional neural networks: application to the characterization of Alzheimer’s disease continuum. J. Neural ...

    2023 doi

  34. [42]

    IEEE Trans

    He, B., Astolfi, L., Valdes-Sosa, P .A., Marinazzo, D., Palva, S.O., Benar, C.-G., Michel, C.M., Koenig, T.: Electrophysiological Brain Connectivity: Theory and Implementation. IEEE Trans. Biomed. Eng. 66, 2115 –2137 (2019). https://doi.org/10.1109/TBME.2019.2913928

    2019

  35. [43]

    Soft Comput

    Nagabushanam, P ., Thomas George, S., Radha, S.: EEG signal classification using LSTM and improved neural network algorithms. Soft Comput. 24, 9981–10003 (2020). https://doi.org/10.1007/s00500-019-04515-0

    2020 doi

  36. [44]

    In: 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS)

    Islam, Md.K., Rastegarnia, A.: Probability Mapping Based Artifact Detection and Wavelet Denoising based Artifact Removal from Scalp EEG for BCI Applications. In: 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS). pp. 243–247. IEEE, Singapore (2019)

    2019

  37. [45]

    Human Brain Mapping

    Miyakoshi, M., Kim, H., Nakanishi, M., Palmer, J., Kanayama, N.: One out of ten independent components shows flipped polarity with poorer data quality: EEG database study. Human Brain Mapping. hbm.26540 (2023). https://doi.org/10.1002/hbm.26540

    2023 doi

  38. [46]

    Biomedical Signal Processing and Control

    Alharbi, N.: A novel approach for noise removal and distinction of EEG recordings. Biomedical Signal Processing and Control. 39, 23–33 (2018). https://doi.org/10.1016/j.bspc.2017.07.011

    2018 doi

  39. [47]

    Nat Neurosci

    Reid, A.T., Headley, D.B., Mill, R.D., Sanchez-Romero, R., Uddin, L.Q., Marinazzo, D., Lurie, D.J., Valdés-Sosa, P .A., Hanson, S.J., Biswal, B.B., Calhoun, V., Poldrack, R.A., Cole, M.W.: Advancing functional connectivity research from association to causation. Nat Neurosci. ...

    2019 doi

  40. [49]

    IEEE Access

    Ismail, L.E., Karwowski, W.: A Graph Theory -Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics. IEEE Access. 8, 155103 –155135 (2020). https://doi.org/10.1109/ACCESS.2020.3018995

    2020

  41. [50]

    Brunner, C., Billinger, M., Seeber, M., Mullen, T.R., Makeig, S.: Volume Conduction Influences Scalp-Based Connectivity Estimates. Front. Comput. Neurosci. 10, (2016). https://doi.org/10.3389/fncom.2016.00121

    2016

  42. [51]

    Olejarczyk, E., Marzetti, L., Pizzella, V., Zappasodi, F.: Comparison of connectivity analyses for resting state EEG data. J. Neural Eng. 14, 036017 (2017). https://doi.org/10.1088/1741-2552/aa6401

    2017 doi

  43. [52]

    NeuroImage

    Hillebrand, A., Barnes, G.R., Bosboom, J.L., Berendse, H.W., Stam, C.J.: Frequency-dependent functional connectivity within resting -state networks: An atlas -based MEG beamformer solution. NeuroImage. 59, 3909 –3921 (2012). https://doi.org/10.1016/j.neuroimage.2011.11.005

    2012 doi

  44. [3002]

    + 19/32/64/128 e lectrodes setup iEEG Head model + A

    The estimated scalp EEG representation through forward solution is as follows: 11( ) ( ( ) ( )) ( ( ) ( )) ( ) cs TT NNV t v t v t G x t x t GX t= =  = (1) where tV represents scalp EEG, cN denotes the number of electrodes, t stands for the time sample points, G signifies the...

    1966

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.