Manuscripts on this dataset

  1. CMS Manager

    Manuscripts on this dataset, "ds000117"
  2. CMS Manager

    1. S Moia, HT Wang, AS Heinsfeld, D Jarecka, YF Yang, Proceedings of the OHBM Brainhack 2022, 2024, Cited by 0,
    2. AS Olsen, JD Nielsen, M Mørup, Coupled generator decomposition for fusion of electro-and magnetoencephalography data, arXiv preprint arXiv:2403.15409, 2024, Cited by 0,
    3. C Gohil, R Huang, E Roberts, MWJ van Es, AJ Quinn, osl-dynamics, a toolbox for modeling fast dynamic brain activity, Elife, 2024, Cited by 2,
    4. X Qin, L Du, X Jiao, J Wang, S Tong, Evaluation of Brain Source Localization Methods Based on Test-Retest Reliability With Multiple Session EEG Data, IEEE Transactions on Biomedical Engineering, 2023, Cited by 0,
    5. J Polzehl, K Tabelow, Functional Magnetic Resonance Imaging, Magnetic Resonance Brain Imaging: Modelling and Data Analysis Using R, 2023, Cited by 1,
    6. DM El-Din, AE Hassanein, A Darwish, MultiModal Data Challenge in Metaverse Technology, The Future of Metaverse in the Virtual Era and Physical World, 2023, Cited by 1,
    7. NE Souter, L Lannelongue, G Samuel, C Racey, Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging, Imaging Neuroscience, 2023, Cited by 1,
    8. V Litvak, A Delorme, F Tadel, A Gramfort, R Oostenveld, From raw MEG/EEG to publication: How to perform MEG/EEG group analysis with free academic software, 2023, Cited by 0,
    9. J Liang, ZL Yu, Z Gu, Y Li, Electromagnetic source imaging with a combination of sparse Bayesian learning and deep neural network, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, Cited by 2,
    10. H Sinha, PR Raamana, Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA, bioRxiv, 2023, Cited by 1,
    11. H Sotudeh, SM Sakhaei, A novel brain source reconstruction using a multivariate mode decomposition, Journal of Neural Engineering, 2023, Cited by 0,
    12. Y Takeda, T Gomi, R Umebayashi, S Tomita, K Suzuki, Sensor array design of optically pumped magnetometers for accurately estimating source currents, NeuroImage, 2023, Cited by 3,
    13. A Delorme, R Oostenveld, F Tadel, A Gramfort, From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software, Frontiers in Neuroscience, 2022, Cited by 4,
    14. SM Lee, R Tibon, P Zeidman, PS Yadav, R Henson, Effects of face repetition on ventral visual stream connectivity using dynamic causal modelling of fMRI data, Neuroimage, 2022, Cited by 2,
    15. K Robbins, D Truong, A Jones, I Callanan, S Makeig, Building FAIR functionality: annotating events in time series data using hierarchical event descriptors (HED), Neuroinformatics, 2022, Cited by 7,
    16. J Yang, Discovering the units in language cognition: From empirical evidence to a computational model, 2022, Cited by 2,
    17. B Couvy-Duchesne, S Bottani, E Camenen, F Fang, Main existing datasets for open data research on humans, 2022, Cited by 2,
    18. AA Vergani, Solving clustering as ill-posed problem: experiments with K-Means algorithm, arXiv preprint arXiv:2211.08302, 2022, Cited by 0,
    19. J Liang, ZL Yu, Z Gu, Y Li, Electromagnetic source imaging via bayesian modeling with smoothness in spatial and temporal domains, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, Cited by 1,
    20. 刘柯, 杨东, 邓欣, 基于 fMRI 功能网络和贝叶斯矩阵分解的脑电源成像方法, 电子与信息学报, 2022, Cited by 0,
    21. R Keßler, Connectivity models in the neural face perception domain–interfaces to understand the human brain in health and disease?, 2022, Cited by 0,
    22. AS Olsen, RMT Høegh, JL Hinrich, Combining electro-and magnetoencephalography data using directional archetypal analysis, Frontiers in Neuroscience, 2022, Cited by 4,
    23. YF Zhang, S Mameri, T Xie, A Sadoun, Local similarity of activity patterns during auditory and visual processing, Neuroscience Letters, 2022, Cited by 0,
    24. J Li, J Pan, F Wang, Z Yu, Inter-subject MEG decoding for visual information with hybrid gated recurrent network, Applied Sciences, 2021, Cited by 3,
    25. CR Pernet, R Martinez-Cancino, D Truong, From BIDS-formatted EEG data to sensor-space group results: a fully reproducible workflow with EEGLAB and LIMO EEG, Frontiers in Neuroscience, 2021, Cited by 30,
    26. A Sadoun, T Chauhan, YF Zhang, Intensity patterns at the peaks of brain activity in fMRI and PET are highly correlated with neural models of spatial integration, European Journal of Neuroscience, 2021, Cited by 0,
    27. A Maffei, P Sessa, Time-resolved connectivity reveals the “how” and “when” of brain networks reconfiguration during face processing, Neuroimage: Reports, 2021, Cited by 11,
    28. CC Tsai, WK Liang, Event-related components are structurally represented by intrinsic event-related potentials, Scientific Reports, 2021, Cited by 7,
    29. K Liu, ZL Yu, W Wu, X Chen, Z Gu, C Guan, fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging, Neurocomputing, 2021, Cited by 2,
    30. R Kessler, KM Rusch, KC Wende, V Schuster, Revisiting the effective connectivity within the distributed cortical network for face perception, NeuroImage: Reports, 2021, Cited by 11,
    31. CH Hsu, YN Wu, Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography, Sensors, 2021, Cited by 1,
    32. K Suzuki, O Yamashita, MEG current source reconstruction using a meta-analysis fMRI prior, Neuroimage, 2021, Cited by 8,
    33. K Robbins, D Truong, S Appelhoff, A Delorme, Capturing the nature of events and event context using hierarchical event descriptors (HED), NeuroImage, 2021, Cited by 15,
    34. PHA Chen, D Fareri, B Güroğlu, MR Delgado, Towards a Neurometric-based Construct Validity of Trust, BioRxiv, 2021, Cited by 3,
    35. F Xu, K Liu, Z Yu, X Deng, G Wang, EEG extended source imaging with structured sparsity and -norm residual, Neural Computing and Applications, 2021, Cited by 6,
    36. R Martínez-Cancino, A Delorme, D Truong, F Artoni, The open EEGLAB portal interface: High-performance computing with EEGLAB, NeuroImage, 2021, Cited by 54,
    37. NP Subramaniyam, F Tronarp, S Särkkä, L Parkkonen, Joint estimation of neural sources and their functional connections from MEG data, bioRxiv, 2020, Cited by 0,
    38. K Liu, ZL Yu, W Wu, Z Gu, Y Li, Imaging brain extended sources from EEG/MEG based on variation sparsity using automatic relevance determination, Neurocomputing, 2020, Cited by 7,
    39. L Henschel, S Conjeti, S Estrada, K Diers, B Fischl, Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline, NeuroImage, 2020, Cited by 316,
    40. A Sadoun, T Chauhan, S Mameri, YF Zhang, P Barone, Stimulus-specific information is represented as local activity patterns across the brain, NeuroImage, 2020, Cited by 10,
    41. B Belaoucha, T Papadopoulo, Structural connectivity to reconstruct brain activation and effective connectivity between brain regions, Journal of Neural Engineering, 2020, Cited by 7,
    42. MS Treder, MVPA-light: a classification and regression toolbox for multi-dimensional data, Frontiers in Neuroscience, 2020, Cited by 130,
    43. S Martinelli, Analysis of FMRI Exams Through Unsupervised Learning and Evaluation Index, 2020, Cited by 0,
    44. R Martínez-Cancino, A Delorme, Computing phase amplitude coupling in EEGLAB: PACTools, 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), 2020, Cited by 14,
    45. J Polzehl, K Tabelow, Magnetic resonance brain imaging, 2019, Cited by 10,
    46. Y Wang, H Huang, H Yang, J Xu, S Mo, H Lai, Influence of EEG references on N170 component in human facial recognition, Frontiers in neuroscience, 2019, Cited by 13,
    47. RN Henson, H Abdulrahman, G Flandin, Multimodal Integration of M/EEG and f/MRI Data in SPM12, Frontiers in neuroscience, 2019, Cited by 31,
    48. Y Takeda, K Suzuki, M Kawato, MEG source imaging and group analysis using VBMEG, Frontiers in Neuroscience, 2019, Cited by 14,
    49. F Tadel, E Bock, G Niso, JC Mosher, MEG/EEG group analysis with brainstorm, Frontiers in neuroscience, 2019, Cited by 145,
    50. A Sadoun, T Chauhan, S Mameri, Y Zhang, P Barone, Stimulation-specific information is represented as local activity patterns across the brain, bioRxiv, 2019, Cited by 0,
    51. Y Wang, H Huang, H Lai, J Zhang, Influence of reference electrode on face recognition event-related potential components, Chinese Journal of Tissue Engineering Research, 2019, Cited by 0,
    52. AA Vergani, S Martinelli, E Binaghi, Clustering functional mri patterns with fuzzy and competitive algorithms, Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications: 6th International Conference, CompIMAGE 2018, Cracow, Poland, July 2--5, 2018, Revised Selected Papers 6, 2019, Cited by 4,
    53. Y Huang, J Zhang, Y Cui, G Yang, Q Liu, Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG, Frontiers in behavioral neuroscience, 2018, Cited by 3,
    54. M Jas, E Larson, DA Engemann, A reproducible MEG/EEG group study with the MNE software: recommendations, quality assessments, and good practices, Frontiers in neuroscience, 2018, Cited by 97,
    55. M van Vliet, M Liljeström, S Aro, R Salmelin, J Kujala, Analysis of functional connectivity and oscillatory power using DICS, 2018, Cited by 0,
    56. M Jas, Contributions pour l'analyse automatique de signaux neuronaux, 2018, Cited by 1,
    57. AJ Quinn, D Vidaurre, R Abeysuriya, R Becker, Task-evoked dynamic network analysis through hidden Markov modeling, Frontiers in neuroscience, 2018, Cited by 140,
    58. M Jas, Advances in automating analysis of neural time series data, 2018, Cited by 0,
    59. J Yang, H Zhu, X Tian, Group-level multivariate analysis in EasyEEG toolbox: examining the temporal dynamics using topographic responses, Frontiers in neuroscience, 2018, Cited by 17,
    60. G Niso, KJ Gorgolewski, E Bock, TL Brooks, G Flandin, MEG-BIDS, the brain imaging data structure extended to magnetoencephalography, Scientific data, 2018, Cited by 128,
    61. LM Núñez Vivero, Segmentación de imágenes de resonancia magnética cerebral mediante redes neuronales artificiales convolucionales, 2018, Cited by 1,
    62. M Van Vliet, M Liljeström, S Aro, R Salmelin, Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in python, Frontiers in Neuroscience, 2018, Cited by 29,
    63. AA Vergani, E Binaghi, A soft davies-bouldin separation measure, 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2018, Cited by 36,
    64. MJ Abdulaal, AJ Casson, Performance of nested vs. non-nested SVM cross-validation methods in visual BCI: Validation study, 2018 26th European Signal Processing Conference (EUSIPCO), 2018, Cited by 14,
    65. JJ Fahrenfort, J Van Driel, S Van Gaal, From ERPs to MVPA using the Amsterdam decoding and modeling toolbox (ADAM), Frontiers in Neuroscience, 2018, Cited by 121,
    66. SFV Nielsen, MN Schmidt, KH Madsen, M Mørup, Predictive assessment of models for dynamic functional connectivity, Neuroimage, 2018, Cited by 20,
    67. V Mv, M Liljeström, S Aro, R Salmelin, J Kujala, Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python, 2018, Cited by 0,
    68. M Jas, E Larson, D Engemann, J Leppäkangas, MEG/EEG group study with MNE: recommendations, quality assessments and best practices, bioRxiv, 2017, Cited by 8,
    69. M Jas, DA Engemann, Y Bekhti, F Raimondo, Autoreject: Automated artifact rejection for MEG and EEG data, NeuroImage, 2017, Cited by 360,
    70. G Niso, KJ Gorgolewski, E Bock, TL Brooks, G Flandin, MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography, bioRxiv, 2017, Cited by 4,
    71. DG Wakeman, RN Henson, A multi-subject, multi-modal human neuroimaging dataset, Scientific data, 2015, Cited by 164,
    72. A Cassettes, G Hoods, LG Recall, FP Recall, I Recalled, Blood Pressure Monitor Tubing May Connect to IV Port, 2003, Cited by 0,
    73. P Warren, Neurofusion: Fusing MEG and EEG Data, Cited by 0,

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