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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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“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).”
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“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).”
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“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
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“In addition, partial directed coherence (PDC) has also been proposed to assess the Granger causality (Baccala and Sameshima, 2001).”
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Items 101–200 of 229
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Information transmission velocity-based dynamic hierarchical brain networks
Jiang, L., Li, F., Chen, Z., Zhu, B., Yi, C., Li, Y., Zhang, T., Peng, Y., Si, Y., Cao, Z., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119997 · Cited by 15
“Some approaches, such as MNE, have been proven to be challenged if underlying sources are correlated with each other (Michel and He, 2019).”
Khadhraoui, E., Nickl‐Jockschat, T., Henkes, H. H., Behme, D., & Müller, S. J. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1459652 · Cited by 18
“…be designed using graph theory approaches, and several tools have been built, e.g., Brain Connectivity Toolbox (Rubinov and Sporns, 2010), eConnectome (He et al., 2011), BRAPH (Mijalkov et al., 2017), GRETNA (Wang et al., 2015), GAT (Hosseini et al., 2012), and GraphVar (Kruschwitz et al., 2015).”
MethodOchoa, J. F., Alonso, J. F., Duque, J. E., Tobón, C., Baena, A. Y., Lopera, F. J., Mañanas, M. Á., & Hernández, A. M. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161291 · Cited by 15
“1e), ii) the estimation of the signal in the cortex using the EEG for each subject, through the weighted Minimum Norm Estimation (wMNE) implementation included in eConnectome [49], using personalized ROIs according to the brain localization from the previous step (Fig.”
Ochoa, J. F., Alonso, J. F., Duque, J. E., Tobón, C., Mañanas, M. Á., Lopera, F. J., & Hernández, A. M. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160803 · Cited by 12
Electrophysiological Mapping and Source Imaging
He, B., Ding, L., & Sohrabpour, A. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_13 · Cited by 11
Self-citationCooperation driven coherence: Brains working hard together
Bezerianos, A., Sun, Y., Chen, Y., Wong, K. F., Taya, F., Aricò, P., Borghini, G., Babiloni, F., & Thakor, N. V. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319442 · Cited by 14
Self-citationSpatiotemporal dynamics of working memory under the influence of emotions based on EEG
Zhang, Y., Liu, B., & Gao, X. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7f50 · Cited by 11
Cortical Dynamic Causality Network for Auditory-Motor Tasks
Liu, T., Li, F., Jiang, Y., Zhang, T., Wang, F., Gong, D., Li, P., Ma, T., Kan, Q., Li, H., et al. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2608359 · Cited by 13
“For this module, the eConnectome toolbox [43] (website: http://econnectome.”
Automated Data Cleaning for the Muse EEG
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Altered Directed Connectivity in Patients with Early Vascular Dementia During a Visual Oddball Task
Xu, J., Lou, W., Zhao, S., & Wang, C. (2014) · Brain Topography · doi:10.1007/s10548-014-0385-3 · Cited by 13
“The figures were plotted by using the programs modified from eConnectome (He et al. 2011)) (Color figure online) Brain Topogr (2015) 28:330–339 335”
MethodXu, H., Lu, Y., Zhu, S., & He, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2311034 · Cited by 12
Self-citationTaberna, G. A., Samogin, J., Marino, M., & Mantini, D. (2021) · Brain Sciences · doi:10.3390/brainsci11060741 · Cited by 12
“In recent years, an increasing number of studies have performed source-level functional connectivity analyses using hdEEG [1,10,18,19,49,68,69].”
BackgroundDevelopment of EEG connectivity from preschool to school-age children
Kang, J., Mao, W., Wu, J., Huang, X., Casanova, M. F., Sokhadze, E. M., Xiaoli Li, A., & Geng, X. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1277786 · Cited by 11
Zhang, S., Cui, H., Li, Y., Chen, X., Gao, X., & Guan, C. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3389051 · Cited by 11
aBEAT: A Toolbox for Consistent Analysis of Longitudinal Adult Brain MRI
Dai, Y., Wang, Y., Wang, L., Guorong Wu, A., Shi, F., Shen, D. G., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060344 · Cited by 11
Self-citationDirectional connectivity in the EEG is able to discriminate wakefulness from NREM sleep
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Comparison of statistical tests in effective connectivity analysis of ECoG data
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FCLAB: An EEGLAB module for performing functional connectivity analysis on single-subject EEG data
Pezoulas, V. C., Athanasiou, A., Nolte, G., Zervakis, M., Fratini, A., Fotiadis, D. I., & Klados, M. A. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333378 · Cited by 8
Exploring the Neuroplastic Effects of Biofeedback Training on Smokers
Pandria, N., Athanasiou, A., Terzopoulos, N., Paraskevopoulos, E., Karagianni, M. A., Styliadis, C., Kourtidou‐Papadeli, C., Pataka, A., Lymperaki, E., & Bamidis, P. D. (2018) · Behavioural Neurology · doi:10.1155/2018/4876287 · Cited by 8
EEGsig machine learning-based toolbox for End-to-End EEG signal processing.Preprint
Ghorbani, F., Hashemi, S., Abdolali, A., & Soleimani, M. (2020) · arXiv (Cornell University) · Cited by 8
Ma, P., Dong, C., Lin, R., Liu, H., Lei, D., Chen, X., & Liu, H. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1306283 · Cited by 7
“To alleviate the impact of singular sample data, the DTF matrix is normalized (He et al., 2011).”
EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis
Shi, J., Gong, X., Song, Z., Xie, W., Yang, Y., Sun, X., Wei, P., Wang, C., & Zhao, G. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1384250 · Cited by 7
“EPAT toolbox is built upon EEGLAB, a widely used MATLAB toolbox for processing EEG data (He et al., 2011).”
MethodAssi, E. B., Sawan, M., Nguyen, D. K., & Rihana, S. A. (2016) · Middle East Conference on Biomedical Engineering · doi:10.1109/mecbme.2016.7745410 · Cited by 7
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Directional brain functional interaction analysis in patients with amyotrophic lateral sclerosis
Shahriari, Y., Sellers, E. W., McCane, L. M., Vaughan, T. M., & Krusienski, D. J. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146788 · Cited by 7
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“However, the QR based regression used here is similar to that used in popular MAR statistical packages (e.g., Schneider and Neumaier, 2001; He et al., 2011; Seth, 2010).”
MethodTask-related reconfiguration patterns of frontoparietal network during motor imagery
Chen, L., Zhang, L., Wang, Z., Li, Q., Gu, B., & Ming, D. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.05.035 · Cited by 6
Cortical networks underlying successful control of nociceptive processing using real-time fMRI
Bucolo, M. A. R., Rance, M., Nees, F., Ruttorf, M., Stella, G., Monarca, N., Andoh, J., & Flor, H. (2022) · Frontiers in Pain Research · doi:10.3389/fpain.2022.969867 · Cited by 6
Kang, J., Xiaoli Li, A., Casanova, M. F., Sokhadze, E. M., & Geng, X. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02693-y · Cited by 6
MEG-based brain functional connectivity analysis using eConnectome
Dai, Y., & He, B. (2011) · Joint Meeting of International Symposium on Noninvasive Functional Source Imaging of Brain and Heart and International Conference on Functional Biomedical Imaging · doi:10.1109/nfsi.2011.5936808 · Cited by 6
“This work was supported in part by NIH/NIBIB under grants RO1EB006433 and RO1EB007920 to Bin He. brain functional connectivity [14].”
MethodSelf-citationLateralization of Epileptic Foci Through Causal Analysis of Scalp-EEG Interictal Spike Activity
Li, W., Wu, N., Hou, W., Zhang, J., Xu, W., Wu, G.-C., Zheng, X., & Feng, H. (2015) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0000000000000120 · Cited by 6
Leal, A. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2020.11.007 · Cited by 6
Canonical correlation between LFP network and spike network during working memory task in rat
Hu, Y., Zhang, X., Bai, W., Liu, T., & Tian, X. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.04.042 · Cited by 6
Diffusion adaptive filtering for modelling brain responses to motor tasks
Eftaxias, K., & Sanei, S. (2013) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2013.6622799 · Cited by 6
Domingos, C., Więcławski, W., Frycz, S., Wójcik, M., Jáni, M., Dudzińska, O., Adamczyk, P., & Ros, T. (2025) · Brain and Behavior · doi:10.1002/brb3.70370 · Cited by 5
“In addition, we performed our analysis in sensor space to maintain inter-electrode phase relations that can be affected by varying orientations of dipoles (He et al. 2011).”
Dynamic reorganization of functional networks underlying audiovisual interactions
Akdoğan, İ., Aydın, S., & Kafalıgönül, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-23390-4 · Cited by 7
Chiang, C., Yang, R., Kao, Y.-C., Wu, R., Ouyang, C., & Lin, L. (2023) · Clinical EEG and Neuroscience · doi:10.1177/15500594231177979 · Cited by 5
Han, X., Xie, J., Luo, A., Xu, G., Zhang, X., Wang, J., & Li, M. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441841 · Cited by 5
Ahn, M.-H., Alsabbagh, N., Lee, H., Kim, H., Jung, M. H., & Hong, S. K. (2022) · Biology · doi:10.3390/biology11101501 · Cited by 6
SeroWare: An Open-Source Software Suite for Voltammetry Data Acquisition and Analysis
Movassaghi, C. S., Iyer, R. K., Curry, M. E., Wesely, M. E., Alcañiz-Fillol, M., & Andrews, A. M. (2025) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00799 · Cited by 5
Exploring Functional and Causal Connectivity in the Brain
Ding, M., & He, B. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5227-0_13 · Cited by 5
Self-citationAlterations of source and connectivity EEG patterns under simulated deep-SEA condition
Storti, S. F., Formaggio, E., Melucci, M., Faralli, F., Ricciardi, L., Menegaz, G., & Pastena, L. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163882 · Cited by 5
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
Xu, R., Thomas, M. M., Leow, A., Ajilore, O., & Forbes, A. G. (2019) · Visualization · doi:10.1109/visual.2019.8933544 · Cited by 5
“Other tools instead utilize a 3D layout for investigating dynamic connectome data [16,23,25].”
Method[Effects of virtual reality visual experience on brain functional network].
Zhang, T., Wang, L., Guo, M., & Xu, G. (2020) · PubMed Central · doi:10.7507/1001-5515.201812027 · Cited by 5
Localizing Epileptogenic Zone from High Density EEG Data Using Machine Learning
Khan, S., Khan, A., Hameed, N., Taufiq, M. A., & Riaz, S. (2021) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v17i06.18653 · Cited by 6
Bakhtiari, A., Bjørke, A. B., Larsson, P. G., Olsen, K. B., Nævra, M. C. J., Taubøll, E., Heuser, K., & Østby, Y. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.774532 · Cited by 6
Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept
Athanasiou, A., Arfaras, G., Xygonakis, I., Kartsidis, P. E., Pandria, N., Kavazidi, K. R., Astaras, A., Foroglou, N., Polyzoidis, K. S., & Bamidis, P. D. (2017) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2017.35 · Cited by 5
Measures of states of consciousness during attentional and cognitive loadPreprint
Nilsen, A. S., Juel, B. E., & Storm, J. F. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/586149 · Cited by 5
“The DTF was calculated using the ‘DTF’ function in the eConnectome toolbox (He et al., 2011) , and followed the Juel et al (2018) with some deviations.”
MethodChang, Y.-W., Gerbolés, B. Z., Gómez-Ruiz, E., Canal-García, A., Zhao, H., Mijalkov, M., Pereira, J. B., & Volpe, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648455 · Cited by 5
Jiao, Y., Wang, W., Wang, J., & Hou, Z. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad68a5 · Cited by 4
“In this study, the SE was conducted using eConnectome [27], an open-source Matlab software package.”
BrainSort: a Machine Learning Toolkit for Brain Connectome Data Analysis and Visualization
Liu, M., Liu, T., Wang, Y., Feng, Y., Xie, Y., Yan, T., & Wu, J. (2020) · Journal of Signal Processing Systems · doi:10.1007/s11265-020-01583-6 · Cited by 4
“…available toolkits for extracting brain network topological properties have emerged, including Brain Connectivity Toolbox (BCT) [18], eConnectome [19], Graph-Analysis Toolbox (GAT) [20], Pipeline for Analyzing braiN Diffusion imAges (PANDA) [21], Graph-theoRETical Network Analysis toolkit…”
MethodAuditory Noise Leads to Increased Visual Brain-Computer Interface Performance: A Cross-Modal Study
Xie, J., Cao, G., Xu, G., Fang, P., Cui, G., Xiao, Y. J., Li, G., Li, M., Xue, T., Zhang, Y., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.590963 · Cited by 4
“…i th channel, i.e., the DTF H ij , can be defined as (Kaminski and Blinowska, 1991): The normalization of DTF matrix constructed above is as follows (He et al., 2011): γ ij (cid:0) f (cid:1) represents the ratio of influence of the cortical waveform estimated in the j th channel on the cortical…”
BackgroundMethodIddas, H., & Iramina, K. (2021) · IEEE Access · doi:10.1109/access.2021.3072224 · Cited by 4
Which method should be used for brain connectivity analysis?
Bucolo, M. A. R., Rance, M., Muscarello, A., Spampinato, A., Baeuchl, C., & Flor, H. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6695991 · Cited by 4
Spatio-temporal variations in hand movement trajectory based brain activation patterns
Robinson, N., Vinod, A. P., & Guan, C. (2014) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2014.7064273 · Cited by 4
Hsu, C.-H., & Wu, Y.-N. (2021) · Sensors · doi:10.3390/s21186235 · Cited by 3
“Using integrative and multi-subject analysis tools on EMD processed data, we could move a step forward from viewing regions of interests separately as we did in this study [14,26,27].”
MethodReduced Prefrontal-Thalamic Theta Flow During Working Memory Retrieval in APP/PS1 Mice
Zhang, S., Ai, H., Wang, J., Liu, T., Zheng, X., Tian, X., & Bai, W. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231078 · Cited by 3
“The significance of γ ij ( f ) needs to be tested to remove pseudo-connections to get the actual connectivity strength DTF ij [38].”
BackgroundAnalysis Tools for Large Connectomes
Scheffer, L. K. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00085 · Cited by 3
“This paper does not cover analysis of such connectomes, which has its own literature (Sporns, 2003; He et al., 2011; Kaiser, 2011; Leergaard et al., 2012; Xia et al., 2013).”
BackgroundThum, J. A., Malekmohammadi, M., Toker, D., Sparks, H. D., Alijanpourotaghsara, A., Choi, J. W., Hudson, A. E., Monti, M. M., & Pouratian, N. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae243 · Cited by 3
Zhang, H., Xie, J., Tao, Q., Ge, Z., Xiong, Y., Xu, G., Li, M., & Han, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1448051 · Cited by 4
Chen, K., Wu, Z., Xu, Y., Wang, R., Jiang, S., Chen, L., Zhao, F., Mao, Y. Q., & Li, M. (2025) · Open Research (University of Surrey) · doi:10.1002/viw.20240138 · Cited by 3
Cerebral functional connectivity analysis based on scalp EEG in epilepsy patients
Sun, Z., Wang, G., Li, K., Zhang, Z., & Bao, G. (2014) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2014.7002786 · Cited by 3
Frequency-Specific Non-Linear Granger Causality in a Network of Brain Signals
Biswas, A., & Ombao, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746794 · Cited by 3
EEGsig: an open-source machine learning-based toolbox for EEG signal processingPreprint
Ghorbani, F., Shabanpour, J., Monjezi, S., Soleimani, H., Hashemi, S., & Abdolali, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12877 · Cited by 3
“…[6] and Brainstorm [7]. also, Brain Connectivity Toolbox (BCT) [8], BrainNet Viewer [9], the GCCA toolbox [10], the connectome mapper [11], Gephi [12], TMSEEG [13], the connectome Viewer [14], the eConnectome [15], the Connectome Visualization Utility (CVU) [16], GraphVar [17] and EEGNET [18].”
MethodNewborn EEG connectivity analysis using time-frequency signal processing techniques
Omidvarnia, A. (2014) · The University of Queensland · doi:10.14264/uql.2014.449 · Cited by 3
“The two hemispheres were analyzed as separate groups of electrodes, and quantitative 3-D maps of directed influences were plotted using customized MATLAB functions of eConnectome toolbox [136].”
MethodChen, C., Xie, Y., Zhao, J., Xu, S., Cheng, L., Yi, C., Yao, D., Zhang, X., Li, F., & Xu, P. (2025) · Communications Biology · doi:10.1038/s42003-025-09029-0 · Cited by 2
“Moreover, certain approaches like MNE present challenges in physiological situations requiring interac-tionsbetweendistributedsources 76 .”
SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies
Galindo, S. E., Toharia, P., Robles, Ó. D., & Pastor, L. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.753997 · Cited by 2
“On one hand, eConnectome , developed by He et al. (2011), is a Matlab toolbox intended for mapping and imaging functional connectivity at both the scalp and cortical levels from electroencephalograms and elecotrocorticograms.”
MethodEEG correlates of acquiring race driving skills
Sultana, M., Gheorghe, L., & Perdikis, S. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adb077 · Cited by 2
“We make use of the DTF algorithm implementation from the eConnec-tome toolbox [41], with order 10 for the embedded autoregressive model.”
Lioi, G., Bell, S. L., & Simpson, D. M. (2016) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-32703-7_1 · Cited by 2
Juel, B. E., Romundstad, L. G., Storm, J. F., & Larsson, P. G. (2020) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-020-00603-x · Cited by 3
Cortical control of walking with and without powered exoskeleton assistance: An EEG pilot study
Saleh, S., Ramanujam, A., Momeni, K., Hoxha, A., Husain, S. R., Allexandre, D., Yue, G. H., & Forrest, G. F. (2017) · International Symposium on Wearable Robotics and Rehabilitation (WeRob) · doi:10.1109/werob.2017.8383865 · Cited by 2
EEG Data Modeling for Brain Connectivity Estimation in 3D Graphs
Espinoza-Valdez, A., Negrón, A. P. P., Salido-Ruiz, R. A., & Carranza, D. B. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-63329-5_19 · Cited by 2
Alanis-Espinosa, M., & Gutiérrez, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.02723 · Cited by 2
“Based on eConnectome toolbox (He et al., 2011), a nonparametric method based on surrogate data is used to assess the statistical significance of our calculated values of PDC.”
MethodNeurofeedback training of control network improves SSVEP-based BCI performance in childrenPreprint
Sun, J., He, J., & Gao, X. (2020) · doi:10.21203/rs.2.24387/v1 · Cited by 2
Untangling cross-regional cross-frequency coupling in dynamic neural oscillations
Niketeghad, S., Mirpour, K., Malekmohammadi, M., Tsolaki, E. C., Pouratian, N., & Speier, W. F. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae2293 · Cited by 1
“Granger causality was estimated by calculating DTF of the system of the two time series based on a MVAR model of the signals in the frequency range of 2–35 Hz using Matlab EConnectome toolbox (He et al 2011).”
Statistical methods for comparing brain connectomes at different scales
Meskaldji, D.-E., Morgenthaler, S., & Ville, D. V. D. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2189847 · Cited by 1
Automated dense neuronal fiber tracing and connectivity mapping at cellular level
Brattain, L. J., Telfer, B. A., Samsi, S., Ku, T., Choi, H., & Chung, K. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950531 · Cited by 1
Can Electroencephalogram (EEG) Signals Predict Postural Balance Performance?
Rodrick, D., & Malik, H. M. A. (2013) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213571208 · Cited by 1
A cloud-based brain connectivity analysis tool
Brattain, L. J., Bulugioiu, M., Brewster, A., Hernandez, M. A., Choi, H., Ku, T., Chung, K., & Gadepally, V. N. (2017) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2017.8091080 · Cited by 1
Bansal, D., & Mahajan, R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814687-3.00003-x · Cited by 1
Lei, X. (2019) · Springer eBooks · doi:10.1007/978-981-13-9113-2_9 · Cited by 1
V‐NeuroStack: Open‐source 3D time stack software for identifying patterns in neuronal data
Naik, A. G., Kenyon, R. V., Taheri, A., Berger-Wolf, T. Y., Ibrahim, B. A., Shinagawa, Y., & Llano, D. A. (2022) · Journal of Neuroscience Research · doi:10.1002/jnr.25139 · Cited by 1
“BrainNet Viewer (Xia et al., 2013), DynamicBC (Liao et al., 2014), EEGNET (Hassan et al., 2015), Connectome visualization utility (CVU) (LaPlante et al., 2014), and eConnectome (He et al., 2011) are visualization utilities implemented as open-source toolboxes to visualize large brain networks.”
MethodTime, Frequency & Time-Varying Causality Measures in NeurosciencePreprint
Cekic, S., Grandjean, D., & Renaud, O. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.03177 · Cited by 1
Long range, high-gamma phase coherence in the human brain during overt and covert speechPreprint
Valiante, T. A., & Garic, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.23.427916 · Cited by 1
Chang, Y.-W., Gerbolés, B. Z., Gómez-Ruiz, E., Canal-García, A., Zhao, H., Mijalkov, M., Pereira, J. B., & Volpe, G. (2026) · SoftwareX · doi:10.1016/j.softx.2026.102791 · Cited by 6
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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