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Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications

T0 review · 0 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read The chapter argues that mapping brain networks with EEG/MEG requires moving from sensor-level signals to source-space estimates via forward and inverse modeling, and then using connectivity metrics that suppress zero-lag volume conduction.

desk verdict A competent, clearly written review chapter on EEG/MEG network analysis; useful as a teaching resource, but it carries promotional self-citation and an unquantified leap from source localization to connectivity accuracy. read the letter →

arxiv 2607.17602 v1 pith:LNT7B43P submitted 2026-07-20 q-bio.NC math-phmath.MP

Richard Leahy , Takfarinas Medani This is my paper · ORCID
classification q-bio.NCmath-phmath.MP
keywords EEGMEGbrainnetworksfunctionalconnectivityeffectivesourcereconstructionvolumeconductionmeasures
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

EEG and MEG record brain activity at millisecond resolution, but their sensor-level signals are a mixture of many underlying sources, so raw sensor correlations do not reveal real brain networks. This chapter argues that trustworthy network analysis must first solve the forward problem—modeling how currents in the head produce the measured fields—and then the inverse problem, reconstructing cortical source activity. Once source time series are obtained, connectivity should be computed with measures that down-weight or ignore zero-lag correlations (such as imaginary coherence, the phase-lag index, and its weighted version), because volume conduction and field spread create spurious zero-phase coupling. The chapter also surveys directed (effective) connectivity methods and presents an integrated, reproducible analysis pipeline for doing all of this. For a sympathetic reader, the value is a defensible methodological default: source space plus leakage-robust metrics, built from subject-specific head models.

What carries the argument

The central machinery is the combined forward-inverse pipeline that converts scalp/magnetometer measurements into cortical source time series. The forward problem uses a volume-conductor model of the head (boundary-element or finite-element) to compute a lead-field matrix linking current dipoles to sensors; the inverse problem estimates the dipole currents, typically with minimum-norm or beamforming methods. This is what makes connectivity interpretable. A second mechanism is the use of zero-lag-discounting connectivity metrics—imaginary coherence, phase-lag index (and weighted PLI), and source orthogonalization before amplitude-envelope correlation—which remove instantaneous coupling that v

What would settle it

A ground-truth test with simultaneous intracranial EEG and scalp EEG/MEG, in which the known implanted-network connectivity is compared with sensor-level and source-level estimates, would settle the claim: if leakage-robust source-space connectivity does not outperform sensor-level connectivity, the chapter's central premise is weakened.

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Extended reading notes

Core claim

The chapter's central claim is that interpretable EEG/MEG brain-network analysis depends on source reconstruction and on connectivity metrics that suppress zero-lag volume conduction. Sensor-space correlations are ambiguous: a single source can appear at many sensors with no time delay, so strong coherence between channels may reflect a common source rather than an interaction. The authors therefore advance a workflow in which the forward problem is solved with a volume-conductor model of the head (ideally subject-specific), the inverse problem is solved to estimate cortical currents, and connectivity is then computed on those reconstructed sources using measures such as imaginary coherence,

Load-bearing premise

The argument rests on the premise that subject-specific, anatomically accurate head models yield source estimates accurate enough that the reconstructed regional time series reflect real brain activity rather than leakage artifacts.

Editorial extensions

If this is right

  • Sensor-level connectivity should not be used as evidence for brain networks, because volume conduction creates spurious zero-lag correlations between channels that do not reflect interactions.
  • The recommended default for EEG/MEG network analysis is source-space connectivity using metrics that suppress zero-lag coupling (imaginary coherence, PLI/wPLI, or orthogonalized envelope correlation).
  • Subject-specific head models built from individual MRIs are preferred over template models, since anatomical mismatches degrade source localization and contaminate downstream connectivity.
  • Directed (effective) connectivity methods such as Granger causality, dynamic causal modeling, and transfer entropy should be applied to reconstructed source signals, not to sensor recordings, and interpreted conservatively.
  • Integrated, reproducible pipelines that combine preprocessing, head modeling, source estimation, and connectivity computation make group-level and clinical network studies feasible.

Reading between the lines

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

  • If the chapter's argument is correct, a large portion of the existing sensor-level coherence literature may need to be reinterpreted or reanalyzed, since many reported 'connections' could be volume-conduction artifacts.
  • A testable extension would be to run the same connectivity analysis at sensor and source levels on data with concurrent intracranial recordings; if source-space metrics do not track the known network better than sensor-space metrics, the central claim would be challenged.
  • The emphasis on zero-lag artifacts suggests that combined EEG+MEG source reconstruction, which uses complementary sensitivity patterns, may further reduce leakage and produce more accurate network estimates than either modality alone.
  • The chapter's best-practice framework implies that when individual MRIs are unavailable, template models should be used with caution and results should be cross-checked with at least two leakage-resistant metrics.
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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

0 major / 6 minor

Summary. The chapter is a review of EEG/MEG-based brain network analysis. It covers the physical principles of EEG and MEG, forward and inverse modeling, volume conduction and leakage, functional and effective connectivity measures, software pipelines (with emphasis on Brainstorm and BrainSuite), and emerging topics such as time-varying and cross-frequency connectivity. The central methodological claim is that interpretable EEG/MEG network analysis requires solving the inverse problem to obtain source-space time series, and that connectivity measures should suppress zero-lag, volume-conduction-related coupling (e.g., imaginary coherence, wPLI, orthogonalized amplitude envelope correlation). The chapter is framed as a tutorial/reference for advanced students and researchers, and it includes practical recommendations for reproducible workflows and data standards.

Significance. If used as a reference chapter, it provides a broad and mostly accurate synthesis of current EEG/MEG connectivity methodology. Its strengths are the clear explanation of volume conduction, the balanced enumeration of functional and effective connectivity measures, the emphasis on source-space interpretation, and the integration of modern reproducible pipelines and BIDS. The paper does not present new empirical results or a novel methodological derivation, so its value is pedagogical and organizational. The stress-test concern that source-space connectivity is recommended without direct quantitative validation does not fully land: the chapter explicitly acknowledges that source-space leakage is not completely resolved and recommends confirmatory controls, and the key claims are supported by standard review citations rather than presented as new quantitative findings. A few editorial revisions would improve clarity and balance.

minor comments (6)
  1. [Functional Connectivity Measures (Undirected)] The sentence 'Several complementary metrics were used in this study' appears to be a leftover from a data-analysis manuscript and is confusing in a review chapter. Please rephrase, e.g., 'The main categories of metrics discussed below include...'.
  2. [BrainSuite for MRI-Based Head Modeling, Structural Connectivity and beyond] The software name is inconsistently capitalized ('Brainsuite' in one passage, 'BrainSuite' elsewhere). Standardize throughout, including in the reference description.
  3. [Template head models vs. subject-specific models] The recommendation that subject-specific models are preferred 'for best accuracy' is reasonable, but it is stated without a direct supporting citation. Adding a specific reference (e.g., Vorwerk et al. 2014 [15] or Darvas et al. 2006 [23]) would help readers locate the evidence.
  4. [Sensor vs. Source Space, Volume Conduction and Its Impact on Connectivity] To preempt a common objection, consider adding one sentence citing Palva et al. 2018 [29] that residual phase-delayed leakage can still create ghost interactions in source space, and that leakage-robust metrics reduce but do not eliminate this risk. The current text already notes that no method completely resolves the issue, but making the residual-leakage point explicit would strengthen the best-practice recommendation.
  5. [References] Reference [61] is a Zenodo tutorial/repository. If the chapter permits, update to the most recent version with a stable DOI so that the reference is fully permanent.
  6. [Figure 6 caption] The caption states that edges are 'derived from the white matter fiber tracks'. If this is just a visualization overlay rather than tractography-based structural connectivity, the wording should be clarified to avoid implying that EEG/MEG connectivity edges are computed from structural connectivity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the chapter is a literature-review and workflow overview with no fitted predictions or derivations that reduce to their inputs.

full rationale

This paper is a review/methods chapter, not a derivation or prediction paper. Its central claim—that EEG/MEG network analysis benefits from source reconstruction and zero-lag-suppressing connectivity metrics—is supported by physical reasoning (volume conduction, quasistatic fields) and by citations to the broader external literature (e.g., Bastos & Schoffelen 2015, Schoffelen & Gross 2009, Palva et al. 2018, Nolte et al. 2004, Vinck et al. 2011). No equation in the chapter defines a quantity in terms of the quantity it is supposed to predict, and no parameter is fitted to data and then renamed as a prediction. The authors' self-citations to Brainstorm, BrainSuite, and their own methodological papers describe software and earlier methods, but these citations are not load-bearing for the volume-conduction argument; the argument would stand with the external citations alone. The chapter also explicitly acknowledges the limitation that 'no single method completely resolves the issue' of volume conduction, and it recommends combining strategies rather than claiming a forced unique result. The skeptic concern that source-localization accuracy does not automatically guarantee connectivity accuracy is an evidentiary gap or correctness risk, not a circularity, because the chapter does not derive connectivity accuracy from localization accuracy by construction. Accordingly, no circular step can be exhibited, and the appropriate finding is no significant circularity.

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

No new entities, free parameters, or fitting are introduced; the chapter reviews existing methods. Its only assumptions are standard bioelectromagnetic modeling and software-accuracy premises.

assumptions (4)
  • domain assumption Quasistatic approximation of the electromagnetic forward problem: volume conduction effects are instantaneous/zero-lag.
    Invoked when the text says volume-conduction effects are 'essentially zero-lag' because of the 'quasistatic nature of the underlying electromagnetics'; this motivates discarding zero-lag correlations as artifactual.
  • domain assumption Neural activity can be modeled as current dipoles from dendritic currents in pyramidal neurons.
    Used as the source model for the forward problem and all inverse methods reviewed.
  • domain assumption MRI-based tissue segmentation and conductivity assignment are accurate enough for source localization.
    The chapter recommends subject-specific head models and states that template models introduce errors; the entire source-space connectivity workflow assumes these models are approximately correct.
  • domain assumption Connectivity metrics computed on source estimates reflect true interactions after leakage mitigation.
    The chapter admits source-space analysis does not entirely solve the problem, but assumes substantial reduction of leakage; this assumption underlies all practical recommendations.

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Cite this review

Pith. "Pith review of Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications." pith.science (2026). https://pith.science/paper/LNT7B43P

@misc{pith2026260717602,
  author       = {Pith},
  title        = {Pith review of: Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LNT7B43P}},
  note         = {Machine review of arXiv:2607.17602}
}
read the original abstract

Electroencephalography (EEG) and magnetoencephalography (MEG) provide noninvasive measurements of brain activity with millisecond temporal resolution, enabling the investigation of functional and effective interactions within large-scale brain networks. This chapter presents a comprehensive overview of the methodological foundations and practical workflows for EEG/MEG-based brain network analysis. We first review the physical principles underlying EEG and MEG, emphasizing their complementary strengths and limitations. We then describe the forward and inverse problems, including subject-specific head modeling, source reconstruction techniques, and the importance of accurate anatomical modeling for reliable source localization. Strategies for mitigating volume conduction and signal leakage are discussed, together with best practices for source-space connectivity analysis. The chapter reviews widely used functional and effective connectivity measures, including coherence, phase synchronization metrics, amplitude envelope correlation, Granger causality, dynamic causal modeling, and transfer entropy, highlighting their assumptions, advantages, and limitations. Modern end-to-end analysis pipelines are presented, with particular emphasis on Brainstorm and complementary open-source software for reproducible EEG/MEG research. Finally, we discuss emerging approaches, including time-varying connectivity, cross-frequency interactions, and network-based analyses, illustrating how noninvasive electrophysiology contributes to understanding brain organization in health and disease. The chapter provides both conceptual foundations and practical guidance for researchers and advanced students seeking to map and interpret human brain networks using EEG and MEG.

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Works this paper leans on

63 extracted references · 20 canonical work pages

  1. [1]

    Electromagnetic brain mapping

    Baillet S, Mosher JC, Leahy RM. Electromagnetic brain mapping. IEEE Signal Process Mag. 2001;18: 14–30. doi:10.1109/79.962275

    doi:10.1109/79.962275 2001

  2. [2]

    Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain

    Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys. 1993;65: 413–497. doi:10.1103/revmodphys.65.413

    doi:10.1103/revmodphys.65.413 1993

  3. [3]

    Exploration and modulation of brain network interactions with noninvasive brain stimulation in combination with neuroimaging

    Shafi MM, Westover MB, Fox MD, Pascual-Leone A. Exploration and modulation of brain network interactions with noninvasive brain stimulation in combination with neuroimaging. Eur J Neurosci. 2012;35: 805–825. doi:10.1111/j.1460-9568.2012.08035.x

    arXiv 2012

  4. [4]

    Electrophysiological imaging of brain activity and connectivity-challenges and opportunities

    He B, Yang L, Wilke C, Yuan H. Electrophysiological imaging of brain activity and connectivity-challenges and opportunities. IEEE Trans Biomed Eng. 2011;58: 1918–1931. doi:10.1109/TBME.2011.2139210

    arXiv 2011

  5. [5]

    Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging

    Sohrabpour A, He B. Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging. Curr Opin Biomed Eng. 2021;18: 100277. doi:10.1016/j.cobme.2021.100277

    arXiv 2021

  6. [6]

    The role of the salience network in cognitive and affective deficits

    Schimmelpfennig J, Topczewski J, Zajkowski W, Jankowiak-Siuda K. The role of the salience network in cognitive and affective deficits. Front Hum Neurosci. 2023;17: 1133367. doi:10.3389/fnhum.2023.1133367

    arXiv 2023

  7. [7]

    EEG and MEG: forward solutions for inverse methods

    Mosher J, Leahy R, Lewis P. EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng. 1999;46: 245–259. doi:10.1109/10.748978

    work page doi:10.1109/10.748978 1999

  8. [8]

    Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity

    Medani T, Garcia-Prieto J, Tadel F, Antonakakis M, Erdbrügger T, Höltershinken M, et al. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. Neuroimage. 2023;267: 119851. doi:10.1016/j.neuroimage.2022.119851

    arXiv 2023

Show all 63 references
  1. [9]

    Boundary element fast multipole method for enhanced modeling of neurophysiological recordings

    Makarov SN, Hamalainen M, Okada Y, Noetscher GM, Ahveninen J, Nummenmaa A. Boundary element fast multipole method for enhanced modeling of neurophysiological recordings. IEEE Trans Biomed Eng. 2021;68: 308–318. doi:10.1109/TBME.2020.2999271

    2021

  2. [10]

    Numerical mathematics of the subtraction method for the modeling of a current dipole in EEG source reconstruction using finite element head models

    Wolters CH, Köstler H, Möller C, Härdtlein J, Grasedyck L, Hackbusch W. Numerical mathematics of the subtraction method for the modeling of a current dipole in EEG source reconstruction using finite element head models. SIAM J Sci Comput. 2008;30: 24–45. doi:10.1137/060659053

    2008 doi

  3. [11]

    Modelling of brain sources using the modified saint Venant’s method in FEM resolution of EEG forward problem

    Medani T, Lautru D, Schwartz D, Ren Z, Sou G. Modelling of brain sources using the modified saint Venant’s method in FEM resolution of EEG forward problem. 2015; 1080–1083. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=yHgUV8MAAAAJ&cstart=20&...

    2015

  4. [12]

    OpenMEEG: opensource software for quasistatic bioelectromagnetics

    Gramfort A, Papadopoulo T, Olivi E, Clerc M. OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed Eng Online. 2010;9: 45. doi:10.1186/1475-925X-9-45

    2010 doi

  5. [13]

    Bioelectromagnetism in human brain research: New applications, new questions

    Gross J, Junghöfer M, Wolters C. Bioelectromagnetism in human brain research: New applications, new questions. Neuroscientist. 2023;29: 62–77. doi:10.1177/10738584211054742

    2023 doi

  6. [14]

    The influence of brain tissue anisotropy on human EEG and MEG

    Haueisen J, Tuch DS, Ramon C, Schimpf PH, Wedeen VJ, George JS, et al. The influence of brain tissue anisotropy on human EEG and MEG. Neuroimage. 2002;15: 159–166. doi:10.1006/nimg.2001.0962

    2002

  7. [15]

    A guideline for head volume conductor modeling in EEG and MEG

    Vorwerk J, Cho J-H, Rampp S, Hamer H, Knösche TR, Wolters CH. A guideline for head volume conductor modeling in EEG and MEG. Neuroimage. 2014;100: 590–607. doi:10.1016/j.neuroimage.2014.06.040

    2014 doi

  8. [16]

    The University of Münster DUNEuro-based pipeline to create personalized head models with calibrated skull conductivity for EEG/MEG source analysis and optimized multi-channel tES

    Erdbrügger CH, T., Antonakakis, M., Aydin, Ü., et al. The University of Münster DUNEuro-based pipeline to create personalized head models with calibrated skull conductivity for EEG/MEG source analysis and optimized multi-channel tES. 2025. Available: https://scholar.google.com...

    2025

  9. [17]

    DUNEuro-A software toolbox for forward modeling in bioelectromagnetism

    Schrader S, Westhoff A, Piastra MC, Miinalainen T, Pursiainen S, Vorwerk J, et al. DUNEuro-A software toolbox for forward modeling in bioelectromagnetism. PLoS One. 2021;16: e0252431. doi:10.1371/journal.pone.0252431

    2021 doi

  10. [18]

    Brainstorm: a user-friendly application for MEG/EEG analysis

    Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci. 2011;2011: 879716. doi:10.1155/2011/879716

    2011 doi

  11. [19]

    Global sensitivity of EEG source analysis to tissue conductivity uncertainties

    Vorwerk J, Wolters CH, Baumgarten D. Global sensitivity of EEG source analysis to tissue conductivity uncertainties. Front Hum Neurosci. 2024;18: 1335212. doi:10.3389/fnhum.2024.1335212

    2024

  12. [20]

    Validating EEG, MEG and combined MEG and EEG beamforming for an estimation of the epileptogenic zone in focal cortical dysplasia

    Neugebauer F, Antonakakis M, Unnwongse K, Parpaley Y, Wellmer J, Rampp S, et al. Validating EEG, MEG and combined MEG and EEG beamforming for an estimation of the epileptogenic zone in focal cortical dysplasia. Brain Sci. 2022;12: 114. doi:10.3390/brainsci12010114

    2022 doi

  13. [21]

    A probabilistic atlas of the human brain: Theory and rationale for its development

    Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J. A probabilistic atlas of the human brain: Theory and rationale for its development. Neuroimage. 1995;2: 89–101. doi:10.1006/nimg.1995.1012

    1995

  14. [22]

    A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI

    Joshi AA, Choi S, Liu Y, Chong M, Sonkar G, Gonzalez-Martinez J, et al. A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI. J Neurosci Methods. 2022;374: 109566. doi:10.1016/j.jneumeth.2022.109566

    2022

  15. [23]

    Generic head models for atlas-based EEG source analysis

    Darvas F, Ermer JJ, Mosher JC, Leahy RM. Generic head models for atlas-based EEG source analysis. Hum Brain Mapp. 2006;27: 129–143. doi:10.1002/hbm.20171

    2006 doi

  16. [24]

    BrainSuite: an automated cortical surface identification tool

    Shattuck DW, Leahy RM. BrainSuite: an automated cortical surface identification tool. Med Image Anal. 2002;6: 129–142. doi:10.1016/s1361-8415(02)00054-3

    2002 doi

  17. [25]

    Statistical parametric maps in functional imaging: A general linear approach

    Friston KJ, Holmes AP, Worsley KJ, Poline J-P, Frith CD, Frackowiak RSJ. Statistical parametric maps in functional imaging: A general linear approach. Hum Brain Mapp. 1994;2: 189–210. doi:10.1002/hbm.460020402

    1994 doi

  18. [26]

    Tetrahedral mesh generation from volumetric binary and grayscale images

    Fang Q, Boas DA. Tetrahedral mesh generation from volumetric binary and grayscale images. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE; 2009

    2009

  19. [27]

    Volume conduction effects in EEG and MEG

    van den Broek SP, Reinders F, Donderwinkel M, Peters MJ. Volume conduction effects in EEG and MEG. Electroencephalogr Clin Neurophysiol. 1998;106: 522–534. doi:10.1016/s0013-4694(97)00147-8

    1998 doi

  20. [28]

    A tutorial review of functional connectivity analysis methods and their interpretational pitfalls

    Bastos AM, Schoffelen J-M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci. 2015;9: 175. doi:10.3389/fnsys.2015.00175

    2015

  21. [29]

    Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures

    Palva JM, Wang SH, Palva S, Zhigalov A, Monto S, Brookes MJ, et al. Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures. Neuroimage. 2018;173: 632–643. doi:10.1016/j.neuroimage.2018.02.032

    2018 doi

  22. [30]

    Functional and effective connectivity: a review

    Friston KJ. Functional and effective connectivity: a review. Brain Connect. 2011;1: 13–36. doi:10.1089/brain.2011.0008

    2011

  23. [31]

    Volume conduction influences scalp-based connectivity estimates

    Brunner C, Billinger M, Seeber M, Mullen TR, Makeig S. Volume conduction influences scalp-based connectivity estimates. Front Comput Neurosci. 2016;10: 121. doi:10.3389/fncom.2016.00121

    2016

  24. [32]

    Source connectivity analysis with MEG and EEG

    Schoffelen J-M, Gross J. Source connectivity analysis with MEG and EEG. Hum Brain Mapp. 2009;30: 1857–1865. doi:10.1002/hbm.20745

    2009 doi

  25. [33]

    EEG functional connectivity analysis in the source space

    Xie W, Toll RT, Nelson CA. EEG functional connectivity analysis in the source space. Dev Cogn Neurosci. 2022;56: 101119. doi:10.1016/j.dcn.2022.101119

    2022

  26. [34]

    Review on solving the inverse problem in EEG source analysis

    Grech R, Cassar T, Muscat J, Camilleri KP, Fabri SG, Zervakis M, et al. Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil. 2008;5: 25. doi:10.1186/1743-0003-5-25

    2008 doi

  27. [35]

    Review of methods for solving the EEG inverse problem

    Pascual-Marqui R. Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism. 1999;1: 75. Available: https://cir.nii.ac.jp/crid/1370584341815444248

    1999

  28. [36]

    EEG source imaging: A practical review of the analysis steps

    Michel CM, Brunet D. EEG source imaging: A practical review of the analysis steps. Front Neurol. 2019;10. doi:10.3389/fneur.2019.00325

    2019

  29. [37]

    Multiple dipole modeling and localization from spatio-temporal MEG data

    Mosher JC, Lewis PS, Leahy RM. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng. 1992;39: 541–557. doi:10.1109/10.141192

    1992 doi

  30. [38]

    On MEG forward modelling using multipolar expansions

    Jerbi K, Mosher JC, Baillet S, Leahy RM. On MEG forward modelling using multipolar expansions. Phys Med Biol. 2002;47: 523–555. doi:10.1088/0031-9155/47/4/301

    2002 doi

  31. [39]

    Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity

    Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, et al. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron. 2000;26: 55–67. doi:10.1016/s0896-6273(00)81138-1

    doi

  32. [40]

    Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details

    Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24 Suppl D: 5–12. Available: https://pubmed.ncbi.nlm.nih.gov/12575463/

    2002

  33. [41]

    EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy

    Afnan J, Cai Z, Lina J-M, Abdallah C, Delaire E, Avigdor T, et al. EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy. Hum Brain Mapp. 2024;45: e26720. doi:10.1002/hbm.26720

    2024 doi

  34. [42]

    FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Oostenveld R, Fries P, Maris E, Schoffelen J-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. 2011;2011: 156869. doi:10.1155/2011/156869

    2011 doi

  35. [43]

    EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134: 9–21. doi:10.1016/j.jneumeth.2003.10.009

    2004 doi

  36. [44]

    MEG and EEG data analysis with MNE-Python

    Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. MEG and EEG data analysis with MNE-Python. Front Neurosci. 2013;7: 267. doi:10.3389/fnins.2013.00267

    2013

  37. [45]

    Identifying true brain interaction from EEG data using the imaginary part of coherency

    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. Clin Neurophysiol. 2004;115: 2292–2307. doi:10.1016/j.clinph.2004.04.029

    2004 doi

  38. [46]

    Measuring phase synchrony in brain signals

    Lachaux JP, Rodriguez E, Martinerie J, Varela FJ. Measuring phase synchrony in brain signals. Hum Brain Mapp. 1999;8: 194–208. doi:10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c

    1999 doi

  39. [47]

    An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias

    Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CMA. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011;55: 1548–1565. doi:10.1016/j.neuroimage.2011.01.055

    2011 doi

  40. [48]

    Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources

    Stam CJ, 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. 2007;28: 1178–1193. doi:10.1002/hbm.20346

    2007 doi

  41. [49]

    Differential classification of states of consciousness using envelope- and phase-based functional connectivity

    Duclos C, Maschke C, Mahdid Y, Berkun K, Castanheira J da S, Tarnal V, et al. Differential classification of states of consciousness using envelope- and phase-based functional connectivity. Neuroimage. 2021;237: 118171. doi:10.1016/j.neuroimage.2021.118171

    2021

  42. [50]

    Large-scale cortical correlation structure of spontaneous oscillatory activity

    Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat Neurosci. 2012;15: 884–890. doi:10.1038/nn.3101

    2012 doi

  43. [51]

    A systematic framework for functional connectivity measures

    Wang HE, Bénar CG, Quilichini PP, Friston KJ, Jirsa VK, Bernard C. A systematic framework for functional connectivity measures. Front Neurosci. 2014;8: 405. doi:10.3389/fnins.2014.00405

    2014

  44. [52]

    Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG

    Sakkalis V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med. 2011;41: 1110–1117. doi:10.1016/j.compbiomed.2011.06.020

    2011 doi

  45. [53]

    Structure and function of complex brain networks

    Sporns O. Structure and function of complex brain networks. Dialogues Clin Neurosci. 2013;15: 247–262. doi:10.31887/dcns.2013.15.3/osporns

    2013 doi

  46. [54]

    Effective connectivity: influence, causality and biophysical modeling

    Valdes-Sosa PA, Roebroeck A, Daunizeau J, Friston K. Effective connectivity: influence, causality and biophysical modeling. Neuroimage. 2011;58: 339–361. doi:10.1016/j.neuroimage.2011.03.058

    2011 doi

  47. [55]

    Connectivity analysis in EEG data: A tutorial review of the state of the art and emerging trends

    Chiarion G, Sparacino L, Antonacci Y, Faes L, Mesin L. Connectivity analysis in EEG data: A tutorial review of the state of the art and emerging trends. Bioengineering (Basel). 2023;10: 372. doi:10.3390/bioengineering10030372

    2023 doi

  48. [56]

    Investigating causal relations by econometric models and cross-spectral methods

    Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969;37: 424. doi:10.2307/1912791

    1969 doi

  49. [57]

    Dynamic causal modelling

    Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19: 1273–1302. doi:10.1016/s1053-8119(03)00202-7

    2003 doi

  50. [58]

    Dynamic causal modeling for EEG and MEG

    Kiebel SJ, Garrido MI, Moran R, Chen C-C, Friston KJ. Dynamic causal modeling for EEG and MEG. Hum Brain Mapp. 2009;30: 1866–1876. doi:10.1002/hbm.20775

    2009 doi

  51. [59]

    Transfer entropy--a model-free measure of effective connectivity for the neurosciences

    Vicente R, Wibral M, Lindner M, Pipa G. Transfer entropy--a model-free measure of effective connectivity for the neurosciences. J Comput Neurosci. 2011;30: 45–67. doi:10.1007/s10827-010-0262-3

    2011 doi

  52. [60]

    Robustly estimating the flow direction of information in complex physical systems

    Nolte G, Ziehe A, Nikulin VV, Schlögl A, Krämer N, Brismar T, et al. Robustly estimating the flow direction of information in complex physical systems. Phys Rev Lett. 2008;100: 234101. doi:10.1103/PhysRevLett.100.234101

    2008 doi

  53. [61]

    PracticalMeeg2025: Brainstorm hands-on tutorial

    Medani T, Niso G, Cassani R, Dubarry A-S, Mosher J, Baillet S, et al. PracticalMeeg2025: Brainstorm hands-on tutorial. Zenodo; 2025. doi:10.5281/ZENODO.17644377

    2025 doi

  54. [62]

    Good scientific practice in EEG and MEG research: Progress and perspectives

    Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, et al. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage. 2022;257: 119056. doi:10.1016/j.neuroimage.2022.119056

    2022

  55. [63]

    BrainSuite BIDS App: Containerized workflows for MRI analysis

    Kim Y, Joshi AA, Choi S, Joshi SH, Bhushan C, Varadarajan D, et al. BrainSuite BIDS App: Containerized workflows for MRI analysis. bioRxiv. 2023. doi:10.1101/2023.03.14.532686

    2023 doi

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.