Quantification of the benefit from integrating MEG and EEG data in minimum ℓ2-norm estimation

Cortical Surface-Based Analysis

Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910

Cortical Surface-Based Analysis

Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147

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

Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684

Geophysical data analysis : discrete inverse theory

Menke, W. H. (1989) · Cited by 2,843

Dynamic Statistical Parametric Mapping

Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., & Halgren, E. (2000) · Neuron · doi:10.1016/s0896-6273(00)81138-1 · Cited by 1,787

Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex

Fischl, B. R., Liu, A., & Dale, A. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906426 · Cited by 1,755

Geophysical Data Analysis: Discrete Inverse Theory

(1989) · International geophysics/International geophysics series · doi:10.1016/s0074-6142(08)x6008-5 · Cited by 1,291

Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data

Hamalainen, M. S., & Sarvas, J. (1989) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.16463 · Cited by 989

Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern mit Anwendung auf die thierisch‐elektrischen Versuche

Helmholtz, H. (1853) · Annalen der Physik · doi:10.1002/andp.18531650603 · Cited by 989

Signal-space projection method for separating MEG or EEG into components

Uusitalo, M. A., & Ilmoniemi, R. J. (1997) · Medical & Biological Engineering & Computing · doi:10.1007/bf02534144 · Cited by 938

Sparse current source estimation for MEG using loose orientation constraints

Chang, W., Ahlfors, S. P., & Lin, F. (2012) · Human Brain Mapping · doi:10.1002/hbm.22057 · Cited by 11

Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements

Jiang, X.-Y., Ye, S., Sohrabpour, A., Bagić, A., & He, B. (2021) · bioRxiv · doi:10.1016/j.nicl.2021.102903 · Cited by 9

Evaluation of a personalized functional near infra‐red optical tomography workflow using maximum entropy on the mean

Cai, Z., Uji, M., Aydın, Ü., Pellegrino, G., Spilkin, A., Delaire, É., Abdallah, C., Lina, J., & Grova, C. (2021) · Human Brain Mapping · doi:10.1002/hbm.25566 · Cited by 10

Towards the Automatic Localization of the Irritative Zone Through Magnetic Source Imaging

Luria, G., Duran, D., Visani, E., Sebastiano, D. R., Sorrentino, A., Tassi, L., Granvillano, A., Franceschetti, S., & Panzica, F. (2020) · Brain Topography · doi:10.1007/s10548-020-00789-y · Cited by 10

Neuromagnetic correlates of hemispheric specialization for face and word recognition

Inamizu, S., Yamada, E., Ogata, K., Uehara, T., Kira, J., & Tobimatsu, S. (2019) · Neuroscience Research · doi:10.1016/j.neures.2019.11.006 · Cited by 10

Research on spatial resolution in cardiac source imaging for multiple measurement modes using a realistic multi-tissue human model

Wang, Y., Wang, H., Yang, Y., Liu, Z.-Y., Pang, J.-J., Cui, P.-L., & Xiang, M. (2025) · Measurement · doi:10.1016/j.measurement.2024.116353 · Cited by 8

μ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates

Feng, Z., Wang, S., Qian, L.-Z., Xu, M., Wu, K., Kakkos, I., Guan, C., & Sun, Y. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120372 · Cited by 9

Fusion of Magnetometer and Gradiometer Sensors of MEG in the Presence of Multiplicative Error

Mohseni, H., Woolrich, M. W., Kringelbach, M. L., Luckhoo, H., Smith, P. P., & Aziz, T. Z. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2195001 · Cited by 9

Graph fractional-order total variation EEG source reconstruction

Li, Y., Qin, J., Osher, S., & Liu, W. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590650 · Cited by 9

Robust Evaluation and Comparison of EEG Source Localization Algorithms for Accurate Reconstruction of Deep Cortical Activity

Shen, H., & Yu, Y.-G. (2023) · Mathematics · doi:10.3390/math11112450 · Cited by 9

Secondary somatosensory area is involved in vibrotactile temporal-structure processing: MEG analysis of slow cortical potential shifts in humans

Hagiwara, K., Ogata, K., Hironaga, N., & Tobimatsu, S. (2020) · Somatosensory & Motor Research · doi:10.1080/08990220.2020.1784127 · Cited by 8

Sparse algorithms for EEG source localization

Mannepalli, T., & Routray, A. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02444-5 · Cited by 9

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

Wingfield, C., Su, L., Liu, X., Zhang, C., Woodland, P. C., Thwaites, A., Fonteneau, E., & Marslen‐Wilson, W. D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005617 · Cited by 8

Neuromagnetic evidence that the right fusiform face area is essential for human face awareness: An intermittent binocular rivalry study

Kume, Y., Maekawa, T., Urakawa, T., Hironaga, N., Ogata, K., Shigyo, M., & Tobimatsu, S. (2016) · Neuroscience Research · doi:10.1016/j.neures.2016.02.004 · Cited by 9

Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

Seeland, A., Krell, M., Straube, S., & Kirchner, E. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00340 · Cited by 6

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

Rodríguez-González, V., Nùñez, P., Gómez, C., Shigihara, Y., Hoshi, H., Tola-Arribas, M. Á., Cano, M., Peral, Á. L. G., García‐Azorín, D., Hornero, R., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120332 · Cited by 7

Investigating the impact of the regularization parameter on EEG resting-state source reconstruction and functional connectivity using real and simulated data

Leone, F., Caporali, A., Pascarella, A., Perciballi, C., Maddaluno, O., Basti, A., Belardinelli, P., Marzetti, L., Lorenzo, G. D., & Betti, V. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120896 · Cited by 7

A role for retro-splenial cortex in the task-related P3 network

Das, D., Shaw, M. E., Hämäläinen, M., Dykstra, A. R., Doll, L., & Gutschalk, A. (2023) · Clinical Neurophysiology · doi:10.1016/j.clinph.2023.11.014 · Cited by 6

DYNAMO: Concurrent dynamic multi-model source localization method for EEG and/or MEG

Antelis, J. M., & Mínguez, J. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.09.017 · Cited by 7

Spatiotemporal Accuracy of Gradient Magnetic-Field Topography (GMFT) Confirmed by Simultaneous Magnetoencephalography and Intracranial Electroencephalography Recordings in Patients with Intractable Epilepsy

Shirozu, H., Hashizume, A., Masuda, H., Fukuda, M., Ito, Y., Nakayama, Y., Higashijima, T., & Kameyama, S. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00065 · Cited by 7

Variation-based sparse source imaging in localizing uterine activity

Zahran, S., Yochum, M., Diab, A., Zahran, S., & Marque, C. K. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037475 · Cited by 7

Neural Switch Asymmetry in Feature-Based Auditory Attention Tasks

McLaughlin, S. A., Larson, E., & Lee, A. K. C. (2019) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-018-00713-z · Cited by 7

Linking offline learning mechanisms to anxiety traitsPreprint

Yu, Q., Luo, Y., Dolan, R. J., Ou, J., Huang, C., Wang, H., Xiao, Z., Nour, M. M., & Liu, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.21.546031 · Cited by 7

Word-selective EEG/MEG responses in the English language obtained with fast periodic visual stimulation (FPVS)

Hauk, O., Marchive, M., Volfart, A., Schiltz, C., Rossion, B., Ralph, M. A. L., & Lochy, A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00414 · Cited by 6

Synthetic Epileptic Brain Activities with TripleGAN

Xu, M., Jiao, J., Zhou, W., Zhou, H., & Jin, S. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/2841228 · Cited by 6

Decoding Semantics from Dynamic Brain Activation Patterns: From Trials to Task in EEG/MEG Source Space

Magnabosco, F., & Hauk, O. (2024) · eNeuro · doi:10.1523/eneuro.0277-23.2023 · Cited by 6

Source imaging method based on diagonal covariance bases and its applications to OPM-MEG

Li, W., Cao, F., An, N., Wang, W., Wang, C., Xu, W., Yu, D., Xiang, M., & Ning, X. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120851 · Cited by 6

Electroencephalographic source imaging of spikes with concurrent high‐frequency oscillation s is concordant with the clinical ground truth

Gonsisko, C. B., Cai, Z., Jiang, X., Duque-Lopez, A., Worrell, G. A., & He, B. (2024) · Epilepsia · doi:10.1111/epi.18141 · Cited by 6

Temporal attention amplifies stimulus information in fronto-cingulate cortex at an intermediate processing stage

Zhu, J., Tian, K. J., Carrasco, M., & Denison, R. N. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae535 · Cited by 6

On the similarities of representations in artificial and brain neural networks for speech recognition

Wingfield, C., Zhang, C., Devereux, B. J., Fonteneau, E., Thwaites, A., Liu, X., Woodland, P. C., Marslen‐Wilson, W. D., & Su, L. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.1057439 · Cited by 6

Investigating brain mechanisms underlying natural reading by co-registering eye tracking with combined EEG and MEGPreprint

Weiss, B., Dreyer, F. R., Fonteneau, E., van Casteren, M., & Hauk, O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.09.451139 · Cited by 6

Source Extent Estimation in OPM-MEG: A Two-Stage Champagne Approach

Li, W., Cao, F., An, N., Wang, W., Wang, C., Xu, W., Gao, Y., & Ning, X. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3462415 · Cited by 4

Deep learning-based EEG source imaging is robust under varying electrode configurations

Rong, J., Sun, R., Joseph, B., Worrell, G. A., & He, B. (2025) · Clinical Neurophysiology · doi:10.1016/j.clinph.2025.04.009 · Cited by 7

Transforming the neuroscience of language: estimating pattern-to-pattern transformations of brain activity

Hauk, O., Jackson, R. L., & Rahimi, S. V. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2023.2226268 · Cited by 5

Effects of Spatial Pattern Scale of Brain Activity on the Sensitivity of DOT, fMRI, EEG and MEG

Perdue, K. L., & Diamond, S. G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083299 · Cited by 5

EEG/MEG Source Estimation and Spatial Filtering: The Linear Toolkit

Hauk, O., Stenroos, M., & Treder, M. S. (2019) · Springer eBooks · doi:10.1007/978-3-319-62657-4_85-1 · Cited by 5

A new multimodal cortical source imaging algorithm for integrating simultaneously recorded EEG and MEG

Choi, J.-H., Jung, Y., Jung, H., & Im, C. (2012) · Inverse Problems in Science and Engineering · doi:10.1080/17415977.2012.731596 · Cited by 5

Identifying nonlinear Functional Connectivity with EEG/MEG using Nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC)Preprint

Rahimi, S., Jackson, R. L., & Hauk, O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.19.524690 · Cited by 6

Source Imaging Method Based on Spatial Smoothing and Edge Sparsity (SISSES) and Its Application to OPM-MEG

Li, W., An, N., Cao, F.-Z., Wang, W., Wang, C., Xu, W., Gao, Y., & Ning, X.-L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3467377 · Cited by 1

Evaluating the effect of OPM array errors on the multiple dipole localization accuracy of OPM-MEG

Qi, S., Ma, Y., Liu, Z. A., Jia, L., Wu, H., Song, X., & Ning, X. (2025) · Measurement · doi:10.1016/j.measurement.2025.117764 · Cited by 5

Sparse wavelet-based solutions for the M/EEG inverse problem

Mokhtari, S., Badier, J., Bénar, C., & Torrésani, B. (2023) · International Conference on Sampling Theory and Applications · doi:10.1109/sampta59647.2023.10301414 · Cited by 4

Mapping Brain Networks Using Multimodal Data

Shou, G., Yuan, H., & Ding, L. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_83 · Cited by 4

Higher Resolution sLORETA (HR-sLORETA) in EEG Source Imaging

Sadat-Nejad, Y., & Beheshti, S. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2019.8856905 · Cited by 5

Robust Sparse Weighted Classification For Crowdsourcing

Yu, H., Zhang, C., Li, J., & Zhang, S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3201955 · Cited by 4

Auditory Neuroscience: Sounding Out the Brain Basis of Speech Perception

Sohoglu, E. (2019) · Current Biology · doi:10.1016/j.cub.2019.05.008 · Cited by 4

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task LassoPreprint

Chevalier, J.-A., Gramfort, A., Salmon, J., & Thirion, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.14310 · Cited by 4

The effects of peeling on finite element method -based EEG source reconstruction

Söderholm, S., Lahtinen, J., Wolters, C. H., & Pursiainen, S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105695 · Cited by 3

Source localization of uterine activity using maximum entropy on the mean approach

Zahran, S., Diab, A., Khalil, M. A., & Marque, C. K. (2018) · Middle East Conference on Biomedical Engineering · doi:10.1109/mecbme.2018.8402442 · Cited by 3

A Context-Aware Collaborative Filtering Recommender System Based on GCNs

Chen, B., & Xie, H. (2020) · International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) · doi:10.1109/icitbs49701.2020.00155 · Cited by 3

MEG and Multimodal Integration

Ahlfors, S. P. (2019) · Springer eBooks · doi:10.1007/978-3-030-00087-5_7 · Cited by 3

New horizons in clinical electric source imaging

Mégevand, P., Hamid, L., Dümpelmann, M., & Heers, M. (2019) · Zeitschrift für Epileptologie · doi:10.1007/s10309-019-0258-6 · Cited by 4

A common framework for semantic memory and semantic composition

Law, R. M. C., Ralph, M. A. L., & Hauk, O. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1131 · Cited by 6

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints

Kouti, M., Ansari-Asl, K., & Namjoo, E. (2024) · Medical and Biological Engineering and Computing · doi:10.1007/s11517-024-03125-9 · Cited by 3

Cortical parcellation optimized for magnetoencephalography with a clustering technique

Sommariva, S., Subramaniyam, N. P., & Parkkonen, L. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-90166-1 · Cited by 2

Quellenanalyse epileptischer Aktivität

Bast, T. (2011) · Clinical Epileptology · doi:10.1007/s10309-011-0226-2 · Cited by 2

MEG and Multimodal Integration

Ahlfors, S. P. (2014) · Springer eBooks · doi:10.1007/978-3-642-33045-2_7 · Cited by 2

Investigation of EEG and MEG source imaging accuracy in reconstructing extended cortical sources

Ding, L., & Yuan, H. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091773 · Cited by 2

Semantic categories and contexts of written words affect the early ERP component

Hata, M., Homae, F., & Hagiwara, H. (2013) · Neuroreport · doi:10.1097/wnr.0b013e32835f679d · Cited by 2

Bayesian multi–dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings

Rossi, F., Luria, G., Sommariva, S., & Sorrentino, A. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-5122-7_211 · Cited by 2

Effects of EEG Electrode Positional Deviations for Classification Accuracy on Different Days

Gamano, Y., Saito, Y., Takamori, K., & Morishige, K.-I. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00391 · Cited by 2

Evaluating spatial normalization for SVM-based EEG decoding: A within- and between-subjects perspective

Qin, Y., Xu, Q., Kujala, T., Wang, X., & Cong, F. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109535 · Cited by 1

Magnetic Source Localization: A Review of Methods and Applications

Zhang, X., Zhang, Y., & Xiang, M. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3635829 · Cited by 2

A new approach to the fusion of EEG and MEG signals using the LCMV beamformer

Mohseni, H., Kringelbach, M. L., Woolrich, M. W., Aziz, T. Z., & Smith, P. P. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6637841 · Cited by 1

Evaluations of sparse source imaging and minimum norm estimate methods in both simulation and clinical MEG data

Zhu, M., Zhang, W., Dickens, D. L., & Ding, L. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347542 · Cited by 1

Multimodal Integration: Constraining MEG Localization with EEG and fMRI

Henson, R. N. (2010) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-12197-5_18 · Cited by 1

Improving EEG Source Localization with a Novel Regularization: Spatiotemporal Graph Total Variation (STGTV) Method

Zhou, H., Wang, Y., Li, Y., Ruan, D., & Liu, W. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513128 · Cited by 1

Signal Processing for Diffuse Correlation Spectroscopy with Support Vector Regression

Zhang, P., Gui, Z., Ling, H., Liu, J., Zhang, X., Liu, Y., Li, A., & Shang, Y. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-30825-4_15 · Cited by 1

The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data

Daniela Calvetti, A., Pascarella, A., Pitolli, F., Somersalo, E., & Vantaggi, B. (2022) · Brain Topography · doi:10.1007/s10548-022-00926-9 · Cited by 1

Post-processing of a distributed source method for the localization of somatosensory cortex in a cohort of epilepsy patients

Tyner, K., McCumber, M., Das, S., Urban, C., Maxin, A. J., Chu, T., Alfatlawi, M., & Gliske, S. (2024) · Neuroimage Reports · doi:10.1016/j.ynirp.2024.100204 · Cited by 2

Volumetric mesoscopic electrophysiology: a new imaging modality for the nonhuman primate

Teichert, T., Papp, L., Vincze, F., Burns, N., Goodell, B., Ahmed, Z., Holmes, A., Gray, C. M., Chamanzar, M., & Gurnsey, K. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00399.2024 · Cited by 1

Relating dynamic brain states to dynamic machine states: human and machine solutions to the speech recognition problemPreprint

Wingfield, C., Su, L., Liu, X., Zhang, C., Woodland, P. C., Thwaites, A., Fonteneau, E., & Marslen‐Wilson, W. D. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/074799 · Cited by 1

Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph PriorPreprint

Liu, F., Wang, L., Lou, Y., Ren-cang Li, C., & Purdon, P. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02252 · Cited by 1

Human brain mechanisms of auditory and audiovisual selective attention

Degerman, A. (2008) · Työväentutkimus Vuosikirja

Methodological and clinical aspects of ictal and interictal MEG

Medvedovsky, M. (2015) · Työväentutkimus Vuosikirja

Sparse electromagnetic source imaging using EEG and MEG

Ding, L., & Yuan, H. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347416

Wavelet based sparse source imaging technique

Ding, L., Zhu, M., & Liao, K. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610774

State-space modeling of MEG time series

Jiménez, A. M. (2010) · DSpace@MIT (Massachusetts Institute of Technology)

Empirical evaluation of fused EEG-MEG source reconstruction. Application to auditory mismatch generatorsPreprint

Lecaignard, F., Bertrand, O., Caclin, A., & Mattout, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/765966

MEG and Multimodal Integration

Ahlfors, S. P. (2019) · Springer eBooks · doi:10.1007/978-3-319-62657-4_7-1

Mapping Brain Networks Using Multimodal Data

Shou, G., Yuan, H., & Ding, L. (2022) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_83-1

Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Sadat-Nejad, S.-Y. (2021) · doi:10.32920/ryerson.14661900

Automated Pre-Processing and Automated Post-Processing in EEG/MEG Brain Source Analysis

Sadat-Nejad, S.-Y. (2021) · doi:10.32920/ryerson.14661900.v1

Using a linear dynamic system to measure functional connectivity from M/EEG

Drew, J., Foti, N. J., Nadkarni, R., Larson, E., Fox, E. B., & Lee, A. K. C. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad5cc1

Postmovement Beta Synchronization Induced by Speed Effects on IHI from the Ipsilateral to Contralateral Motor Cortex

Zhang, X., Zhang, S., Zhang, H., Wang, H., & Long, J. (2025) · eNeuro · doi:10.1523/eneuro.0370-24.2025

Combined MEG and EEG suggest a limbic source network of the P3 including retrosplenial cortex and hippocampusPreprint

Das, D., Hämäläinen, M., Dykstra, A. R., Rupp, A., & Gutschalk, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.24.645008

Simultaneous Localization of Multiple Oscillations Using Dynamic Solutions to the Inverse Problem in E/MEG Source Analysis

He, M., Das, P., Beck, A. M., Hotan, G. C., & Purdon, P. L. (2024) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf60004.2024.10942699

Linear and nonlinear multidimensional functional connectivity methods reveal similar networks for semantic processing in EEG/MEG data

Rahimi, S., Jackson, R. L., & Hauk, O. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1533034

Non-uniform effects of remaining field spread on the estimation of M/EEG activity and connectivity between regions of interestPreprint

Kapralov, N., Studenova, A. A., Eguinoa, R., Nolte, G., Haufe, S., Villringer, A., & Nikulin, V. V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.688708

Leadfield Correction within a Two-Stage Champagne Framework for Head Motion Artifact Suppression in MEG

Wang, C., Li, W., Gao, Z., Xiang, M., Cao, F., An, N., & Ning, X. (2025) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3774976.3774987

Kymata Soto Language Dataset: an electro-magnetoencephalographic dataset for natural speech processing

Yang, C., Parish, O., Klimovich‐Gray, A., Wingfield, C., Marslen‐Wilson, W. D., Zhang, C., Woolgar, A., & Thwaites, A. (2026) · Scientific Data · doi:10.1038/s41597-026-06579-8

Dynamic cortical routing mediates temporal attentionPreprint

Zhu, J., Tian, K. J., Carrasco, M., & Denison, R. N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.03.716160

Source localization and connectivity analysis of uterine activity

Zahran, S. (2018) · doi:10.70675/530df620z8b3cz42cczb630z537cc45a011f

A method for combining MEG and EEG to determine the sources

Cohen, D., & Cuffin, B. N. (1987) · Physics in Medicine and Biology · doi:10.1088/0031-9155/32/1/013 · Cited by 42

MEG and EEG Source Estimation: from Surface Mapping to Multimodal Imaging

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