{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:08:09Z","timestamp":1789470489829,"version":"build-2803163510"},"reference-count":70,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Electroencephalography (EEG) source localization has evolved from a technical solution to an inverse problem and into a fundamental framework for investigating large-scale brain networks. By reconstructing cortical activity from scalp-recorded signals, source-space EEG improves anatomical interpretability and reduces confounding effects of volume conduction compared with sensor-level analyses. These advances have enabled more reliable estimation of functional and effective connectivity, graph-theoretical characterization of brain networks, and the integration of machine learning for automated analysis and clinical decision support. Nevertheless, the accuracy and reproducibility of source-level analyses remain highly dependent on forward modeling, inverse algorithms, preprocessing strategies, and connectivity estimation methods. Recent methodological developments have further expanded the role of EEG source imaging in computational neuroscience, supporting applications ranging from network-based investigations of brain function to biomarker discovery and disease classification. This review summarizes the computational framework underlying source-level EEG, beginning with the forward and inverse problems and progressing through source reconstruction, connectivity analysis, graph-theoretical network modeling, and emerging machine learning and deep learning techniques. We also highlight current clinical applications and major methodological challenges, including source leakage, model uncertainty, reproducibility, and interpretability, and outline future directions for robust and clinically translatable source-level EEG analysis.<\/jats:p>","DOI":"10.3390\/biology15181622","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T10:32:01Z","timestamp":1789468321000},"page":"1622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From EEG Source Localization to Brain Network Analysis: A Computational Neuroscience Perspective"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7162-4572","authenticated-orcid":false,"given":"Jooyoung","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae-Hoon","family":"Eom","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nunez, P.L., and Srinivasan, R. (2006). Electric Fields of the Brain: The Neurophysics of EEG, Oxford University Press. [2nd ed.].","DOI":"10.1093\/acprof:oso\/9780195050387.001.0001"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Michel, C.M., and Brunet, D. (2019). EEG source imaging: A practical review of the analysis steps. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00325"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/BF02512476","article-title":"Interpreting magnetic fields of the brain: Minimum norm estimates","volume":"32","author":"Ilmoniemi","year":"1994","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/1743-0003-5-25","article-title":"Review on solving the inverse problem in EEG source analysis","volume":"5","author":"Grech","year":"2008","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/79.962275","article-title":"Electromagnetic brain mapping","volume":"18","author":"Baillet","year":"2001","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, B. (2020). Electrophysiological mapping and source imaging. Neural Engineering, Springer. [3rd ed.].","DOI":"10.1007\/978-3-030-43395-6_13"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1089\/brain.2011.0008","article-title":"Functional and effective connectivity: A review","volume":"1","author":"Friston","year":"2011","journal-title":"Brain Connect."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bastos, A.M., and Schoffelen, J.-M. (2016). A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front. Syst. Neurosci., 9.","DOI":"10.3389\/fnsys.2015.00175"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1002\/hbm.20745","article-title":"Source connectivity analysis with MEG and EEG","volume":"30","author":"Schoffelen","year":"2009","journal-title":"Hum. Brain Mapp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: Uses and interpretations","volume":"52","author":"Rubinov","year":"2010","journal-title":"NeuroImage"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"111","DOI":"10.31887\/DCNS.2018.20.2\/osporns","article-title":"Graph theory methods: Applications in brain networks","volume":"20","author":"Sporns","year":"2018","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.neuroimage.2017.02.076","article-title":"Consistency of EEG source localization and connectivity estimates","volume":"152","author":"Mahjoory","year":"2017","journal-title":"NeuroImage"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: A systematic review","volume":"16","author":"Roy","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1007\/s11517-025-03295-0","article-title":"Graph convolution network-based EEG signal analysis: A review","volume":"63","author":"Xiong","year":"2025","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aziz, M.Z., Yu, X., Guo, X., He, X., Huang, B., and Fan, Z. (2025). BCINetV1: Integrating temporal and spectral focus through a novel convolutional attention architecture for MI EEG decoding. Sensors, 25.","DOI":"10.3390\/s25154657"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110765","DOI":"10.1016\/j.bspc.2026.110765","article-title":"A hybrid adaptive EEG signal tokenization and vector embedding framework for interpretable brain\u2013computer interfaces","volume":"126","author":"Huang","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_17","unstructured":"Jiang, W.B., Zhao, L., and Lu, B.L. (2024). Large brain model for learning generic representations with tremendous EEG data in BCI. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, G., Liu, W., He, Y., Xu, C., Ma, L., and Li, H. (2024, January 10\u201315). EEGPT: Pretrained transformer for universal and reliable representation of EEG signals. Proceedings of the NeurIPS 2024 Conference, Vancouver, BC, Canada.","DOI":"10.52202\/079017-1239"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: A review","volume":"16","author":"Craik","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.neuroimage.2014.06.040","article-title":"A guideline for head volume conductor modeling in EEG and MEG","volume":"100","author":"Vorwerk","year":"2014","journal-title":"NeuroImage"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.neuroimage.2005.10.014","article-title":"Influence of tissue conductivity anisotropy on EEG\/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling","volume":"30","author":"Wolters","year":"2006","journal-title":"NeuroImage"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/S1388-2457(02)00030-5","article-title":"A standardized boundary element method volume conductor model","volume":"113","author":"Fuchs","year":"2002","journal-title":"Clin. Neurophysiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0013-4694(98)00121-7","article-title":"Spatial mislocalization of EEG electrodes\u2014Effects on accuracy of dipole estimation","volume":"110","author":"Khosla","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"ref_24","first-page":"75","article-title":"Review of methods for solving the EEG inverse problem","volume":"1","year":"1999","journal-title":"Int. J. Bioelectromagn."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0168-5597(85)90033-4","article-title":"Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model","volume":"62","author":"Scherg","year":"1985","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/10.748978","article-title":"EEG and MEG: Forward solutions for inverse methods","volume":"46","author":"Mosher","year":"1999","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.neuroimage.2013.09.002","article-title":"Algorithmic procedures for Bayesian MEG\/EEG source reconstruction in SPM","volume":"84","author":"Litvak","year":"2014","journal-title":"NeuroImage"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gramfort, A., Luessi, M., Larson, E., Engemann, D.A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., and Parkkonen, L. (2013). MEG and EEG data analysis with MNE-Python. Front. Neurosci., 7.","DOI":"10.3389\/fnins.2013.00267"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"879716","DOI":"10.1155\/2011\/879716","article-title":"Brainstorm: A user-friendly application for MEG\/EEG analysis","volume":"2011","author":"Tadel","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_30","first-page":"5","article-title":"Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details","volume":"24","year":"2002","journal-title":"Methods Find. Exp. Clin. Pharmacol."},{"key":"ref_31","unstructured":"Pascual-Marqui, R.D. (2007). Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: Exact, zero error localization. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0896-6273(00)81138-1","article-title":"Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity","volume":"26","author":"Dale","year":"2000","journal-title":"Neuron"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.neuroimage.2007.09.048","article-title":"Multiple sparse priors for the M\/EEG inverse problem","volume":"39","author":"Friston","year":"2008","journal-title":"NeuroImage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/10.623056","article-title":"Localization of brain electrical activity via linearly constrained minimum variance spatial filtering","volume":"44","author":"Yuchtman","year":"1997","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","unstructured":"Yoshimoto, T., Kotani, M., Kuriki, S., Karibe, H., and Nakasato, N. (1999). Functional neuroimaging by synthetic aperture magnetometry (SAM). Recent Advances in Biomagnetism, Tohoku University Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1073\/pnas.98.2.694","article-title":"Dynamic imaging of coherent sources: Studying neural interactions in the human brain","volume":"98","author":"Gross","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.tics.2010.04.004","article-title":"Large-scale brain networks in cognition: Emerging methods and principles","volume":"14","author":"Bressler","year":"2010","journal-title":"Trends Cogn. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.clinph.2012.01.011","article-title":"The organization of physiological brain networks","volume":"123","author":"Stam","year":"2012","journal-title":"Clin. Neurophysiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2292","DOI":"10.1016\/j.clinph.2004.04.029","article-title":"Identifying true brain interaction from EEG data using the imaginary part of coherency","volume":"115","author":"Nolte","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1002\/hbm.20346","article-title":"Phase lag index: Assessment of functional connectivity from multichannel EEG and MEG with diminished bias from common sources","volume":"28","author":"Stam","year":"2007","journal-title":"Hum. Brain Mapp."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.neuroimage.2011.01.055","article-title":"An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias","volume":"55","author":"Vinck","year":"2011","journal-title":"NeuroImage"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1038\/nn.3101","article-title":"Large-scale cortical correlation structure of spontaneous oscillatory activity","volume":"15","author":"Hipp","year":"2012","journal-title":"Nat. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.neuroimage.2015.03.071","article-title":"A symmetric multivariate leakage correction for MEG connectomes","volume":"117","author":"Colclough","year":"2015","journal-title":"NeuroImage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1016\/S1053-8119(03)00202-7","article-title":"Dynamic causal modelling","volume":"19","author":"Friston","year":"2003","journal-title":"NeuroImage"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating causal relations by econometric models and cross-spectral methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/BF00198091","article-title":"A new method of the description of the information flow in the brain structures","volume":"65","author":"Blinowska","year":"1991","journal-title":"Biol. Cybern."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/PL00007990","article-title":"Partial directed coherence: A new concept in neural structure determination","volume":"84","author":"Sameshima","year":"2001","journal-title":"Biol. Cybern."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nrn2575","article-title":"Complex brain networks: Graph theoretical analysis of structural and functional systems","volume":"10","author":"Bullmore","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1146\/annurev-clinpsy-040510-143934","article-title":"Brain graphs: Graphical models of the human brain connectome","volume":"7","author":"Bullmore","year":"2011","journal-title":"Annu. Rev. Clin. Psychol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8577","DOI":"10.1073\/pnas.0601602103","article-title":"Modularity and community structure in networks","volume":"103","author":"Newman","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1177\/1073858406293182","article-title":"Small-world brain networks","volume":"12","author":"Bassett","year":"2006","journal-title":"Neuroscientist"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u201csmall-world\u201d networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"15775","DOI":"10.1523\/JNEUROSCI.3539-11.2011","article-title":"Rich-club organization of the human connectome","volume":"31","author":"Sporns","year":"2011","journal-title":"J. Neurosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: Going beyond Euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106043","DOI":"10.1016\/j.compbiomed.2022.106043","article-title":"Explainable, trustworthy, and ethical machine learning for healthcare: A survey","volume":"149","author":"Rasheed","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1111\/j.1528-1167.2007.01381.x","article-title":"EEG source localization in focal epilepsy: Where are we now?","volume":"49","author":"Plummer","year":"2008","journal-title":"Epilepsia"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1093\/brain\/awr243","article-title":"Electroencephalographic source imaging: A prospective study of 152 operated epileptic patients","volume":"134","author":"Brodbeck","year":"2011","journal-title":"Brain"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Paitel, E.R., Otteman, C.B.D., Polking, M.C., Licht, H.J., and Nielson, K.A. (2025). Functional and effective EEG connectivity patterns in Alzheimer\u2019s disease and mild cognitive impairment: A systematic review. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1496235"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4427","DOI":"10.1002\/hbm.25554","article-title":"Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study","volume":"42","author":"Alivar","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Iglesias-Parro, S., Soriano, M.F., Ib\u00e1\u00f1ez-Molina, A.J., P\u00e9rez-Matres, A.V., and Ruiz de Miras, J. (2023). Examining neural connectivity in schizophrenia using task-based EEG: A graph theory approach. Sensors, 23.","DOI":"10.3390\/s23218722"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/nrn3801","article-title":"Modern network science of neurological disorders","volume":"15","author":"Stam","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.tins.2017.06.003","article-title":"Developmental connectomics from infancy through early childhood","volume":"40","author":"Cao","year":"2017","journal-title":"Trends Neurosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1146\/annurev-bioeng-062117-120853","article-title":"Electrophysiological source imaging: A noninvasive window to brain dynamics","volume":"20","author":"He","year":"2018","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn Human Connectome Project: An overview","volume":"80","author":"Smith","year":"2013","journal-title":"NeuroImage"},{"key":"ref_66","first-page":"230","article-title":"Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness","volume":"1","author":"Calhoun","year":"2016","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"ref_67","first-page":"1","article-title":"Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?","volume":"150","author":"Renvall","year":"2023","journal-title":"Clin. Neurophysiol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"156869","DOI":"10.1155\/2011\/156869","article-title":"FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data","volume":"2011","author":"Oostenveld","year":"2011","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41597-019-0104-8","article-title":"EEG-BIDS, an extension to the brain imaging data structure for electroencephalography","volume":"6","author":"Pernet","year":"2019","journal-title":"Sci. Data"}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/18\/1622\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T10:51:05Z","timestamp":1789469465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/18\/1622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":70,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["biology15181622"],"URL":"https:\/\/doi.org\/10.3390\/biology15181622","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}