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Manuscripts on this dataset

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    Manuscripts on this dataset, "ds000117"

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    1. S Moia, HT Wang, AS Heinsfeld, D Jarecka, YF Yang, Proceedings of the OHBM Brainhack 2022, 2024, Cited by 0, https://inria.hal.science/hal-04478342/
    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, https://arxiv.org/abs/2403.15409
    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, https://elifesciences.org/articles/91949
    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, https://ieeexplore.ieee.org/abstract/document/10012314/
    5. J Polzehl, K Tabelow, Functional Magnetic Resonance Imaging, Magnetic Resonance Brain Imaging: Modelling and Data Analysis Using R, 2023, Cited by 1, https://link.springer.com/chapter/10.1007/978-3-031-38949-8_4
    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, https://link.springer.com/chapter/10.1007/978-3-031-29132-6_11
    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, https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00043/118246
    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, https://books.google.com/books?hl=en&lr=&id=c-vDEAAAQBAJ&oi=fnd&pg=PP1&dq=ds000117&ots=FOnm7oEG6Q&sig=HUOkRQWhvJeLOxQCufTz-DqSxPs
    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, https://ieeexplore.ieee.org/abstract/document/10071956/
    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, https://www.biorxiv.org/content/10.1101/2023.07.17.548591.abstract
    11. H Sotudeh, SM Sakhaei, A novel brain source reconstruction using a multivariate mode decomposition, Journal of Neural Engineering, 2023, Cited by 0, https://iopscience.iop.org/article/10.1088/1741-2552/acdffe/meta
    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, https://www.sciencedirect.com/science/article/pii/S1053811923004081
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.854471/full
    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, https://www.sciencedirect.com/science/article/pii/S1053811922008291
    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, https://link.springer.com/article/10.1007/s12021-021-09537-4
    16. J Yang, Discovering the units in language cognition: From empirical evidence to a computational model, 2022, Cited by 2, https://pure.mpg.de/rest/items/item_3424007/component/file_3429797/content
    17. B Couvy-Duchesne, S Bottani, E Camenen, F Fang, Main existing datasets for open data research on humans, 2022, Cited by 2, https://hal.science/hal-03666558/
    18. AA Vergani, Solving clustering as ill-posed problem: experiments with K-Means algorithm, arXiv preprint arXiv:2211.08302, 2022, Cited by 0, https://arxiv.org/abs/2211.08302
    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, https://ieeexplore.ieee.org/abstract/document/9832821/
    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, https://jeit.ac.cn/article/exportPdf?id=0b5c99ae-f050-4904-9c9a-37fce4f7764f
    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, https://d-nb.info/126810678X/34
    22. AS Olsen, RMT Høegh, JL Hinrich, Combining electro-and magnetoencephalography data using directional archetypal analysis, Frontiers in Neuroscience, 2022, Cited by 4, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.911034/full
    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, https://www.sciencedirect.com/science/article/pii/S0304394022004529
    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, https://www.mdpi.com/2076-3417/11/3/1215
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.610388
    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, https://onlinelibrary.wiley.com/doi/abs/10.1111/ejn.15469
    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, https://www.sciencedirect.com/science/article/pii/S2666956021000209
    28. CC Tsai, WK Liang, Event-related components are structurally represented by intrinsic event-related potentials, Scientific Reports, 2021, Cited by 7, https://www.nature.com/articles/s41598-021-85235-0
    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, https://www.sciencedirect.com/science/article/pii/S0925231221009905
    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, https://www.sciencedirect.com/science/article/pii/S266695602100043X
    31. CH Hsu, YN Wu, Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography, Sensors, 2021, Cited by 1, https://www.mdpi.com/1424-8220/21/18/6235
    32. K Suzuki, O Yamashita, MEG current source reconstruction using a meta-analysis fMRI prior, Neuroimage, 2021, Cited by 8, https://www.sciencedirect.com/science/article/pii/S1053811921003116
    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, https://www.sciencedirect.com/science/article/pii/S1053811921010387
    34. PHA Chen, D Fareri, B Güroğlu, MR Delgado, Towards a Neurometric-based Construct Validity of Trust, BioRxiv, 2021, Cited by 3, https://www.biorxiv.org/content/10.1101/2021.07.04.451074.abstract
    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, https://link.springer.com/article/10.1007/s00521-020-05603-1
    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, https://www.sciencedirect.com/science/article/pii/S1053811920302652
    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, https://www.biorxiv.org/content/10.1101/2020.10.04.325563.abstract
    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, https://www.sciencedirect.com/science/article/pii/S0925231220300825
    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, https://www.sciencedirect.com/science/article/pii/S1053811920304985
    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, https://www.sciencedirect.com/science/article/pii/S1053811920308120
    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, https://iopscience.iop.org/article/10.1088/1741-2552/ab8b2b/meta
    42. MS Treder, MVPA-light: a classification and regression toolbox for multi-dimensional data, Frontiers in Neuroscience, 2020, Cited by 130, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00289
    43. S Martinelli, Analysis of FMRI Exams Through Unsupervised Learning and Evaluation Index, 2020, Cited by 0, https://irinsubria.uninsubria.it/handle/11383/2115066
    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, https://ieeexplore.ieee.org/abstract/document/9288037/
    45. J Polzehl, K Tabelow, Magnetic resonance brain imaging, 2019, Cited by 10, https://link.springer.com/content/pdf/10.1007/978-3-030-29184-6.pdf
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00705/full
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00300/full
    48. Y Takeda, K Suzuki, M Kawato, MEG source imaging and group analysis using VBMEG, Frontiers in Neuroscience, 2019, Cited by 14, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00241/full
    49. F Tadel, E Bock, G Niso, JC Mosher, MEG/EEG group analysis with brainstorm, Frontiers in neuroscience, 2019, Cited by 145, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00076/full
    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, https://www.biorxiv.org/content/10.1101/726414.abstract
    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, https://www.cjter.com/EN/abstract/abstract14697.shtml
    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, https://link.springer.com/chapter/10.1007/978-3-030-20805-9_12
    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, https://www.frontiersin.org/articles/10.3389/fnbeh.2018.00096/full
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00530
    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, https://aaltodoc.aalto.fi/items/e41f4dea-21ae-4041-a9d6-d8d944162563
    56. M Jas, Contributions pour l'analyse automatique de signaux neuronaux, 2018, Cited by 1, https://www.theses.fr/2018ENST0021
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00603/full
    58. M Jas, Advances in automating analysis of neural time series data, 2018, Cited by 0, https://pastel.hal.science/tel-03411539/
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00468
    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, https://www.nature.com/articles/sdata2018110
    61. LM Núñez Vivero, Segmentación de imágenes de resonancia magnética cerebral mediante redes neuronales artificiales convolucionales, 2018, Cited by 1, https://oa.upm.es/id/eprint/51557
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00586/full
    63. AA Vergani, E Binaghi, A soft davies-bouldin separation measure, 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE), 2018, Cited by 36, https://ieeexplore.ieee.org/abstract/document/8491581/
    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, https://ieeexplore.ieee.org/abstract/document/8553102/
    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, https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00368
    66. SFV Nielsen, MN Schmidt, KH Madsen, M Mørup, Predictive assessment of models for dynamic functional connectivity, Neuroimage, 2018, Cited by 20, https://www.sciencedirect.com/science/article/pii/S1053811917311084
    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, https://europepmc.org/article/ppr/ppr12673
    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, https://www.biorxiv.org/content/10.1101/240044.abstract
    69. M Jas, DA Engemann, Y Bekhti, F Raimondo, Autoreject: Automated artifact rejection for MEG and EEG data, NeuroImage, 2017, Cited by 360, https://www.sciencedirect.com/science/article/pii/S1053811917305013
    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, https://www.biorxiv.org/content/10.1101/172684.abstract
    71. DG Wakeman, RN Henson, A multi-subject, multi-modal human neuroimaging dataset, Scientific data, 2015, Cited by 164, https://www.nature.com/articles/sdata20151
    72. A Cassettes, G Hoods, LG Recall, FP Recall, I Recalled, Blood Pressure Monitor Tubing May Connect to IV Port, 2003, Cited by 0, https://journals.lww.com/biomedicalsafetystandards/fulltext/2003/09150/Laryngeal_Mask_Recalled__Sterility_Not_Assured.10.aspx
    73. P Warren, Neurofusion: Fusing MEG and EEG Data, Cited by 0, http://cs231n.stanford.edu/reports/2016/pdfs/321_Report.pdf

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