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eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity
Open accessAbstract
We have developed a MATLAB-based toolbox, eConnectome (electrophysiological connectome), for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG), as well as from the electrocorticogram (ECoG). Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks, over the scalp and cortical sensor spaces. Cortical current density inverse imaging was implemented using a generic realistic geometry brain-head model from scalp EEGs. Granger causality could be further estimated over the cortical source domain from the inversely reconstructed cortical source signals as derived from the scalp EEG. Users may implement other connectivity estimators in the framework of eConnectome for various applications. The toolbox package is open-source and freely available at http://econnectome.umn.edu under the GNU general public license for noncommercial and academic uses.
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“The eConnectome approach (104, 105) has stirred interest in studying pathological networks such as epilepsy, where determining the source that drives seizures is of particular importance (106).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [104]
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Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · cited 1×
“, and Λ (p) are the N×N matrices of model coefficients and the model order p can be determined using the Final Prediction Error (FPE) criterion (Akaike, 1971) and Schwarz Bayesian Criterion (SBC) (Schwarz, 1978).”
MethodDelorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · cited 1×
“EEGLAB (Delorme and Makeig, 2004) is an interactive MATLAB toolbox for processing continuous and event-related electrophysiological data.”
MethodInvestigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761 · cited 1×
“The concept of brain functional connectivity thus now plays an important role in neuroscience as a way to understand the organized behavior of brain regions beyond mapping and localization of their activities (Varela et al., 2001).”
BackgroundTesting for nonlinearity in time series: the method of surrogate data
Theiler, J. P., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90102-s · Cited by 3,759 · cited 1×
“Instead, a nonparametric method based on surrogate data is used to assess the significance of the estimated connectivity measures (Theiler et al., 1992; Palus and Hoyer, 1998; Ding et al., 2007).”
MethodAutomatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431
Dale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003
The five percent electrode system for high-resolution EEG and ERP measurements
Oostenveld, R., & Praamstra, P. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(00)00527-7 · Cited by 1,915 · cited 1×
“Connectivity patterns at the EEG sensor level can also be visualized overlaying on a spherical head model, where the standard 10-5 electrode system (Oostenveld and Praamstra, 2001) is used.”
MethodPartial directed coherence: a new concept in neural structure determination
Baccalá, L. A., & Sameshima, K. (2001) · Biological Cybernetics · doi:10.1007/pl00007990 · Cited by 1,799 · cited 1×
“In addition, partial directed coherence (PDC) has also been proposed to assess the Granger causality (Baccala and Sameshima, 2001).”
Background
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GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
Wang, J., Wang, X., Xia, M., Liao, X., Evans, A. C., & Yong He, A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00386 · Cited by 1,439
“…visualize graph-based topological analyses of brain networks, such as the Brain Connectivity Toolbox (BCT; Rubinov and Sporns, 2010), eConnectome (He et al., 2011), CONN (Whitfield-Gabrieli and Nieto-Castanon, 2012), Graph-Analysis Toolbox (GAT; Hosseini et al., 2012) and GraphVar (Kruschwitz et…”
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Mullen, T. R., Kothe, C. A., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T., & Cauwenberghs, G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2481482 · Cited by 838
“For instance, eConnectome [23] offers routines for adaptive con-”
EEG Source Imaging: A Practical Review of the Analysis Steps
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Hosseini, S. M. H., Hoeft, F., & Kesler, S. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040709 · Cited by 365
“Brain Connectivity Toolbox [27]; eConnectome [28]; NetworkX (http://networkx.”
BackgroundElectrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297
Self-citationDynamicBC : A MATLAB Toolbox for Dynamic Brain Connectome Analysis
Liao, W., Wu, G., Xu, Q., Ji, G., Zhang, Z., Zang, Y., & Lu, G. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0253 · Cited by 304
BRAPH: A graph theory software for the analysis of brain connectivity
Mijalkov, M., Kakaei, E., Pereira, J. B., Westman, E., Volpe, G., & Initiative, A. D. N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178798 · Cited by 253
“Several toolboxes have been developed to study brain connectivity, including the Brain Connectivity Toolbox [9], eConnectome [10], GAT [11], CONN [12], BrainNet Viewer [13], GraphVar [14] and GRETNA [15].”
MethodElectrophysiological Brain Connectivity: Theory and Implementation
He, B., Astolfi, L., Valdes-Sosa, P. A., Marinazzo, D., Palva, S., Bénar, C., Michel, C. M., & Koenig, T. W. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913928 · Cited by 242
Self-citationArtoni, F., Fanciullacci, C., Bertolucci, F., Panarese, A., Makeig, S., Micera, S., & Chisari, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.013 · Cited by 189
“Relevant steps are explained in (He et al., 2011) and reported here for convenience.”
BackgroundMethodInfluentialShim, M., Hwang, H., Kim, D.-W., Lee, S., & Im, C. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.05.007 · Cited by 181
Sun, Y., Lim, J., Kwok, K., & Bezerianos, A. (2014) · Brain and Cognition · doi:10.1016/j.bandc.2013.12.011 · Cited by 165
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Santamaria, L., Noreika, V., Georgieva, S., Clackson, K., Wass, S. V., & Leong, V. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116341 · Cited by 124
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Zarjam, P., Epps, J., Chen, F., & Lovell, N. H. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.08.021 · Cited by 123
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Daly, I. P., Malik, A., Hwang, F., Roesch, E. B., Weaver, J., Kirke, A., Williams, D. A. H., Miranda, E. R., & Nasuto, S. J. (2014) · Neuroscience Letters · doi:10.1016/j.neulet.2014.05.003 · Cited by 128
Hu, L., Peng, W., Valentini, E., Zhang, Z., & Hu, Y. S. (2012) · Journal of Pain · doi:10.1016/j.jpain.2012.10.008 · Cited by 122
Babaeeghazvini, P., Rueda‐Delgado, L. M., Gooijers, J., Swinnen, S. P., & Daffertshofer, A. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.721206 · Cited by 125
Shim, M., Im, C., Kim, Y.-W., & Lee, S. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.06.012 · Cited by 100
Omidvarnia, A., Azemi, G., Boashash, B., O’Toole, J. M., Colditz, P. B., & Vanhatalo, S. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2286394 · Cited by 96
“arate groups of electrodes, and quantitative 3-D maps of directed influences were plotted using customized MATLAB functions of eConnectome toolbox [43].”
EPILAB: A software package for studies on the prediction of epileptic seizures
Teixeira, C., Direito, B., Feldwisch‐Drentrup, H., Valderrama, M., Costa, R. P., Alvarado‐Rojas, C., Nikolopoulos, S., Quyen, M. L. V., Timmer, J., Schelter, B. O., et al. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.07.002 · Cited by 95
Yi, W., Qiu, S., Wang, K., Qi, H., Zhang, L., Zhou, P., He, F., & Ming, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114853 · Cited by 86
Petrichella, S., Johnson, N., & He, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174879 · Cited by 89
Self-citationiBEAT: A Toolbox for Infant Brain Magnetic Resonance Image Processing
Dai, Y., Shi, F., Wang, L., Guorong Wu, A., & Shen, D. G. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9164-z · Cited by 86
Self-citationCausality in the Association between P300 and Alpha Event-Related Desynchronization
Peng, W., Hu, L., Zhang, Z., & Hu, Y. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034163 · Cited by 84
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Hassan, M., Shamas, M., Khalil, M. A., Falou, W. E., & Wendling, F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138297 · Cited by 85
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BackgroundMethodCoito, A., Michel, C. M., van Mierlo, P., Vulliemoz, S., & Plomp, G. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2619665 · Cited by 75
Rinderer, M., Ali, G. A., & Larsen, L. G. (2018) · Earth-Science Reviews · doi:10.1016/j.earscirev.2018.01.009 · Cited by 85
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Wang, G., Sun, Z., Tao, R., Li, K., Bao, G., & Yan, X. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2424074 · Cited by 80
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Meskaldji, D. E., Fischi-Gomez, E., Griffa, A., Hagmann, P., Morgenthaler, S., & Thiran, J.-P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.084 · Cited by 77
“…software packages have been introduced to analyze network topology (e.g. Brain Connectivity Toolbox (Rubinov and Sporns, 2010); eConnectome (He et al., 2011); NetworkX (http:// networkx.lanl.gov/overview.html); GAT (Hosseini et al., 2012); igraph (http://igraph.sourceforge.net/) and…”
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Zhang, C. H., Sohrabpour, A., Lu, Y., & He, B. (2016) · Human Brain Mapping · doi:10.1002/hbm.23220 · Cited by 80
Self-citationMalekmohammadi, M., Elias, W. J., & Pouratian, N. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht358 · Cited by 74
Shim, M., Kim, D.-W., Lee, S., & Im, C. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.04.012 · Cited by 71
Sun, S., Yu, H., Yu, R., & Wang, S. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02625-w · Cited by 69
“We implemented this analysis using the MATLAB toolbox eConnectome [59].”
Time, frequency, and time‐varying Granger‐causality measures in neuroscience
Cekic, S., Grandjean, D., & Renaud, O. (2018) · Statistics in Medicine · doi:10.1002/sim.7621 · Cited by 71
Discriminative Analysis of Brain Functional Connectivity Patterns for Mental Fatigue Classification
Sun, Y., Lim, J., Meng, J., Kwok, K., Thakor, N. V., & Bezerianos, A. (2014) · Annals of Biomedical Engineering · doi:10.1007/s10439-014-1059-8 · Cited by 65
Investigating the Role of Alpha and Beta Rhythms in Functional Motor Networks
Athanasiou, A., Klados, M. A., Styliadis, C., Foroglou, N., Polyzoidis, K. S., & Bamidis, P. D. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.05.044 · Cited by 64
Sohrabpour, A., Ye, S., Worrell, G. A., Zhang, W., & He, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2616474 · Cited by 59
“ness and strength of combining source imaging with directional functional connectivity analyses, in particular Granger–Geweke causality analysis [23], [24], [30]–[32], [47], [59].”
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Broccard, F. D., Mullen, T. R., Chi, Y. M., Peterson, D., Iversen, J. R., Arnold, M., Kreutz-Delgado, K., Jung, T., Makeig, S., Howard Poizner, A., et al. (2014) · Annals of Biomedical Engineering · doi:10.1007/s10439-014-1032-6 · Cited by 62
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Lehnertz, K. (2011) · Physiological Measurement · doi:10.1088/0967-3334/32/11/r01 · Cited by 60
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Fasoula, A., Attal, Y., & Schwartz, D. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.02.021 · Cited by 58
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Kontson, K. L., Megjhani, M., Brantley, J. A., Cruz-Garza, J. G., Nakagome, S., Robleto, D., White, M., Civillico, E. F., & Contreras-Vidal, J. L. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00626 · Cited by 58
Directed functional connections underlying spontaneous brain activity
Coito, A. L., Michel, C. M., Vulliémoz, S., & Plomp, G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24418 · Cited by 60
Mahmud, M., & Vassanelli, S. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00248 · Cited by 55
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MethodSource Connectivity Analysis from MEG and its Application to Epilepsy Source Localization
Dai, Y., Zhang, W., Dickens, D. L., & He, B. (2011) · Brain Topography · doi:10.1007/s10548-011-0211-0 · Cited by 55
“Significant time points around the peak of the interictal spike were selected by visual inspection of the waveform and global field power (see Fig.”
BackgroundMethodSelf-citationEEG Mapping and Source Imaging
Michel, C. M., & He, B. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190228484.003.0045 · Cited by 56
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Hong, S. K., Park, S., Ahn, M.-H., & Min, B. (2016) · Hearing Research · doi:10.1016/j.heares.2016.10.002 · Cited by 54
Functional Brain Dysfunction in Patients with Benign Childhood Epilepsy as Revealed by Graph Theory
Adebimpe, A., Aarabi, A., Bourel‐Ponchel, E., Mahmoudzadeh, M., & Wallois, F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139228 · Cited by 53
Ferreri, F., Guerra, A., Vollero, L., Ponzo, D., Määttä, S., Mervaala, E., Iannello, G., & Lazzaro, V. D. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.06.014 · Cited by 55
A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging
Bore, J. C., Li, P., Jiang, L., Ayedh, W. M. A., Chen, C., Harmah, D. J., Yao, D., Cao, Z., & Xu, P. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3097758 · Cited by 52
A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer's disease
Modir, A., Shamekhi, S., & Ghaderyan, P. (2023) · Measurement · doi:10.1016/j.measurement.2023.113274 · Cited by 52
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Samdin, S. B., Ting, C., Ombao, H., & Salleh, S. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2580738 · Cited by 52
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Lu, Y., Worrell, G., Zhang, H., Yang, L., Brinkmann, B., Nelson, C., & He, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2297332 · Cited by 46
Self-citationMuraskin, J., Dodhia, S., Lieberman, G., Garcia, J. O., Verstynen, T., Vettel, J. M., Sherwin, J. S., & Sajda, P. (2016) · Human Brain Mapping · doi:10.1002/hbm.23321 · Cited by 50
Aydın, S., & Onbaşı, L. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09931-5 · Cited by 46
Aydın, S., Çetin, F. H., Uytun, M. Ç., Babadağı, Z., Güven, A. S., & Işık, Y. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103626 · Cited by 45
Zhang, C. H., Sha, Z., Mundahl, J., Liu, S., Lu, Y., Henry, T. R., & He, B. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.07.014 · Cited by 45
“This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).”
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Aydın, S. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09843-w · Cited by 46
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Storti, S. F., Galazzo, I. B., Khan, S., Manganotti, P., & Menegaz, G. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2607802 · Cited by 41
Baxter, B., Edelman, B. J., Sohrabpour, A., & He, B. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00691 · Cited by 41
“Source imaging, which involves solving the inverse problem of mapping EEG sensor activity to the brain using Maxwell’s equations and the physical properties of head tissues, has been developed over the last few decades (He et al., 1987, 2011b; Hämäläinen and Sarvas, 1989).”
BackgroundMethodSelf-citationGraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures
Waller, L., Brovkin, A., Dorfschmidt, L., Bzdok, D., Walter, H., & Kruschwitz, J. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.07.001 · Cited by 46
Sparse EEG Source Localization Using LAPPS: Least Absolute l -P (0<p<1) Penalized Solution
Bore, J. C., Yi, C., Li, P., Li, F., Harmah, D. J., Si, Y., Guo, D., Yao, D., Wan, F., & Xu, P. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2881092 · Cited by 39
“For this analysis, we employed a 3-shell realistic head model derived from eConnectome software [51], [52] for EEG source localization.”
Hosseini, S. A. H., Sohrabpour, A., & He, B. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.10.027 · Cited by 39
Self-citationExploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging
Sohrabpour, A., & He, B. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100277 · Cited by 42
“Some of the more famous open-source software and toolboxes include Brainstorm [50], eConnectome [51], Fieldtrip [52], MNE [53], and NUTMEG [54], to name a few.”
MethodSelf-citationShim, M., Im, C., & Lee, S.-H. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.200 · Cited by 40
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Self-citationEEG Source Imaging of Movement Decoding: The State of the Art and Future Directions
Handiru, V. S., Vinod, A. P., & Guan, C. (2018) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2017.2778458 · Cited by 34
Li, G., Jiang, S., Chen, C., Brunner, P., Wu, Z., Schalk, G., Chen, L., & Zhang, D. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab51a5 · Cited by 34
Early recurrence and ongoing parietal driving during elementary visual processing
Plomp, G., Hervais‐Adelman, A., Astolfi, L., & Michel, C. M. (2015) · Scientific Reports · doi:10.1038/srep18733 · Cited by 35
Park, J. H., Jeon, H., Lim, E.-C., Koo, J., Lee, H., Kim, H., Lee, J. S., Song, C. G., & Hong, S. K. (2019) · Clinical and Experimental Otorhinolaryngology · doi:10.21053/ceo.2018.01592 · Cited by 33
Athanasiou, A., Lithari, C. D., Kalogianni, K., Klados, M. A., & Bamidis, P. D. (2012) · Advances in Human-Computer Interaction · doi:10.1155/2012/127627 · Cited by 32
Paraskevopoulou, S. E., Coon, W. G., Brunner, P., Miller, K. J., & Schalk, G. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118127 · Cited by 34
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Zheng, Y. L., & Gao, X. (2011) · Neuroscience Letters · doi:10.1016/j.neulet.2011.05.061 · Cited by 32
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Fanciullacci, C., Panarese, A., Spina, V., Lassi, M., Mazzoni, A., Artoni, F., Micera, S., & Chisari, C. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.669915 · Cited by 31
Decoding of top-down cognitive processing for SSVEP-controlled BMI
Min, B., Dähne, S., Ahn, M.-H., Noh, Y., & Müller, K. (2016) · Scientific Reports · doi:10.1038/srep36267 · Cited by 29
Alzhemimer’s Disease is Characterized by Lower Segregation in Resting-State Eyes-Closed EEG
Aydın, S. (2024) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-024-00917-0 · Cited by 32
“The implementation procedure introduced in reference was followed [41]. e-Connectome-2-full software toolbox was integrated by both Multi-Variate Adaptive Autoregressive modeling toolbox, introduced in reference [42], and ARfit (1) software package, introduced in references [43, 44].”
Wang, C., Xu, J., Zhao, S., & Lou, W. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.04.063 · Cited by 30
Seizure-like episodes and EEG abnormalities in patients with long QT syndrome
González, A., Aurlien, D., Larsson, P. G., Olsen, K. B., Dahl, I. T., Edvardsen, T., Haugaa, K. H., & Taubøll, E. (2018) · Seizure · doi:10.1016/j.seizure.2018.08.020 · Cited by 29
A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy
Assi, E. B., Nguyen, D. K., Rihana, S. A., & Sawan, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2752081 · Cited by 29
Juel, B. E., Romundstad, L. G., Kolstad, F., Storm, J. F., & Larsson, P. G. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00040 · Cited by 29
Huang, Y., Wu, D., Bahuri, N. F. A., Wang, S.-G., Hyam, J. A., Yarrow, S., FitzGerald, J. J., Aziz, T. Z., & Green, A. L. (2018) · British Journal of Anaesthesia · doi:10.1016/j.bja.2018.04.031 · Cited by 31
Characterizing mental load in an arithmetic task using entropy-based features
Zarjam, P., Epps, J., & Lovell, N. H. (2012) · International Conference on Information Science, Signal Processing and their Applications (ISSPA) · doi:10.1109/isspa.2012.6310545 · Cited by 27
“We performed this method using the eConnectome software developed at Minnesota University [23], to select the channels which make the most contribution in discriminating the imposed task load, out of 32 channels recorded for each subject.”
MethodAlternating Zones Selective to Faces and Written Words in the Human Ventral Occipitotemporal Cortex
Matsuo, T., Kawasaki, K., Kawai, K., Majima, K., Masuda, H., Murakami, H., Kunii, N., Kamitani, Y., Kameyama, S., Saito, N., et al. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht319 · Cited by 27
“…29 July 2018 Functional Connectivity Between Channels The eConnectome MATLAB software (http://econnectome.umn.edu, last accessed on 11/12/13) was used to investigate directional interactions (He et al. 2011), or Granger causality, between face-selective channels and letterstring-selective channels.”
MethodLin, Y., Liu, T., Huang, Q., Su, Y., Chen, W., Gao, D., Tian, X., Huang, T., Zhen, Z., Han, T., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00746 · Cited by 24
ToolConnect: A Functional Connectivity Toolbox for In vitro Networks
Pastore, V. P., Poli, D., Godjoski, A., Sergio Martinoia, A., & Massobrio, P. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00013 · Cited by 25
“Concerning in vivo data captured from different acquisition systems like EEG, ECoG, MEG, fMRI, a plethora of software has been developed to infer functional networks: E EGLAB (Delorme and Makeig, 2004), E C ONNECTOME (He et al., 2011), NIP Y (Millman and Brett, 2007), T RENTOOL (Lindner et al., 2011) are some available…”
MethodEffective Connectivity in Cortical Networks During Deception: A Lie Detection Study Based on EEG
Gao, J., Min, X., Kang, Q., Si, H., Zhan, H., Manyande, A., Tian, X., Dong, Y., Zheng, H., & Song, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172994 · Cited by 26
The effect of fatigue on brain connectivity networks
Zhang, S., Sun, J., & Gao, X. (2020) · Brain Science Advances · doi:10.26599/bsa.2020.9050008 · Cited by 23
Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox
Ribeiro, A., Lacerda, L. M., & Ferreira, H. A. (2015) · PeerJ · doi:10.7717/peerj.1078 · Cited by 23
Increased neuronal synchrony prepares mesial temporal networks for seizures of neocortical origin
Misra, A., Long, X., Sperling, M. R., Sharan, A. D., & Moxon, K. A. (2018) · Epilepsia · doi:10.1111/epi.14007 · Cited by 23
Wang, S., Zhang, D., Fang, B., Liu, X., Yan, G., Sui, G., Huang, Q., Sun, L., & Wang, S. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2020.12.038 · Cited by 22
Ying, J., Zhou, D., Lin, K. C., & Gao, X. (2015) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-015-0004-0 · Cited by 21
Fujiwara, T., Chou, J., McCullough, A. M., Charan Ranganath, A., & Ma, K. (2017) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis.2017.8031601 · Cited by 21
Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury
Athanasiou, A., Terzopoulos, N., Pandria, N., Xygonakis, I., Foroglou, N., Polyzoidis, K. S., & Bamidis, P. D. (2018) · Neural Plasticity · doi:10.1155/2018/9354207 · Cited by 21
Ahn, M.-H., Park, J. H., Jeon, H., Lee, H., Kim, H., & Hong, S. K. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.600839 · Cited by 20
Sun, J., He, J., & Gao, X. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.08.010 · Cited by 20
An ensemble learning model for continuous cognition assessment based on resting-state EEG
Sun, J., Sun, Y., Shen, A., Li, Y., Gao, X., & Lu, B. (2024) · npj Aging · doi:10.1038/s41514-023-00129-x · Cited by 20
“In this part of the study, the EEG was averaged by superimposing 20 trials 2 s segments, and then the information transfer between individual leads was calculated based on the directed transfer function (DTF) to form a directed graph (calculated with eConnectome toolbox 47 ).”
MethodThe absence of resting-state high-gamma cross-frequency coupling in patients with tinnitus
Ahn, M.-H., Hong, S. K., & Min, B. (2017) · Hearing Research · doi:10.1016/j.heares.2017.10.008 · Cited by 20
Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity
Xu, N., Shan, W., Qi, J., Wu, J., & Wang, Q. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.649074 · Cited by 19
Metadata
- Authors
- He, B., Dai, Y., Astolfi, L., Babiloni, F., Yuan, H., & Yang, L.
- Affiliations
- University of Minnesota (US)
- Fondazione Santa Lucia (IT)
- Corresponding author
- He, Bin
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 195
- Issue
- 2
- Pages
- 261-269
- ISSN
- 0165-0270 (ISSN-L)
- PMC
- PMC3244474
- OpenAlex
- W2086498469
- Cited by
- 229
- References
- 46
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 13 · 2023: 8 · 2022: 14 · 2021: 17 · 2020: 17 · 2019: 18 · 2018: 22 · 2017: 20 · 2016: 19 · 2015: 23 · 2014: 17 · 2013: 11 · 2012: 9
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- NIH/NIBIB (RO1EB006433, RO1EB007920)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain functional connectivity, EEG, electrocorticography, Granger causality, directed transfer function, MATLAB toolbox
- MeSH terms
- Brain, Brain Mapping, Computer Simulation, Diagnostic Imaging, Electroencephalography, Evoked Potentials, Humans, Signal Processing, Computer-Assisted, Software, Time Factors
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