Sparse source imaging in electroencephalography with accurate field modeling
Open accessAbstract
We have developed a new L1‐norm based generalized minimum norm estimate (GMNE) and have fully characterized the concept of sparseness regularization inherited in the proposed algorithm, which is termed as sparse source imaging (SSI). The new SSI algorithm corrects inaccurate source field modeling in previously reported L1‐norm GMNEs and proposes that sparseness a priori should only be applied to the regularization term, not to the data term in the formulation of the regularized inverse problem. A new solver to the newly developed SSI has been adopted and known as the second‐order cone programming. The new SSI is assessed by a series of simulations and then evaluated using somatosensory evoked potential (SEP) data with both scalp and subdural recordings in a human subject. The performance of SSI is compared with other L1‐norm GMNEs and L2‐norm GMNEs using three evaluation metrics, i.e., localization error, orientation error, and strength percentage. The present simulation results indicate that the new SSI has significantly improved performance in all evaluation metrics, especially in the metric of orientation error. L2‐norm GMNEs show large orientation errors because of the smooth regularization. The previously reported L1‐norm GMNEs show large orientation errors due to the inaccurate source field modeling. The SEP source imaging results indicate that SSI has the best accuracy in the prediction of subdural potential field as validated by direct subdural recordings. The new SSI algorithm is also applicable to MEG source imaging. Hum Brain Mapp 2008. © 2007 Wiley‐Liss, Inc.
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Sparsity is often assumed to avoid over-fitting in inverse problems and has increasing applications in signal and image processing.
“Sparsity is often assumed to avoid over-fitting in inverse problems, and has increasing applications in signal and image processing (43, 50–54).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [51]
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Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
Sturm, J. F. (1999) · Optimization methods & software · doi:10.1080/10556789908805766 · Cited by 7,320 · see citing works
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 · see citing works
Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details.
Pascual-Marqui, R. D. (2002) · PubMed · Cited by 3,340 · see citing works
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994) · International Journal of Psychophysiology · doi:10.1016/0167-8760(84)90014-x · Cited by 2,869 · see citing works
Electric Fields of the Brain: The Neurophysics of EEG
Nunez, P. L., & Harth, E. M. (1982) · Physics Today · doi:10.1063/1.2915137 · Cited by 2,257 · see citing works
Dale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003 · see citing works
Electric Fields of the Brain: The Neurophysics of EEG
Fermaglich, J. (1982) · JAMA · doi:10.1001/jama.1982.03320380071046 · Cited by 1,557 · see citing works
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 · see citing works
On the solution of functional equations by the method of regularization
Морозов, В. А. (1966) · Doklady Mathematics · Cited by 725 · see citing works
Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm
Gorodnitsky, I. F., George, J. S., & Rao, B. D. (1995) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(95)00107-a · Cited by 567 · see citing works
Brain processing of visual sexual stimuli in human males
Redout�, J., Stol�ru, S., Gr�goire, M.-C., Costes, N., Cinotti, L., Lavenne, F., Bars, D. L., Forest, M. G., & Pujol, J. F. (2000) · Human Brain Mapping · doi:10.1002/1097-0193(200011)11:3 · Cited by 517 · see citing works
Visualization of Magnetoencephalographic Data Using Minimum Current Estimates
Uutela, K., Hämäläinen, M., & Somersalo, E. (1999) · NeuroImage · doi:10.1006/nimg.1999.0454 · Cited by 512 · see citing works
Linear and Nonlinear Current Density Reconstructions
Fuchs, M., Wagner, M., Köhler, T., & Wischmann, H.-A. (1999) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-199905000-00006 · Cited by 436 · see citing works
Liu, A. K., Belliveau, J. W., & Dale, A. M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.15.8945 · Cited by 384 · see citing works
Monte Carlo simulation studies of EEG and MEG localization accuracy
Liu, A. K., Dale, A. M., & Belliveau, J. W. (2002) · Human Brain Mapping · doi:10.1002/hbm.10024 · Cited by 348 · see citing works
Electric Dipole Tracing in the Brain by Means of the Boundary Element Method and Its Accuracy
He, B., Musha, T., Okamoto, Y., Homma, S., Nakajima, Y., & Sato, T. (1987) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1987.326056 · Cited by 334 · see citing works
Self-citationA Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem
Baillet, S., & Garnero, L. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.568913 · Cited by 326 · see citing works
Selective minimum-norm solution of the biomagnetic inverse problem
Matsuura, K., & Okabe, Y. (1995) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.387200 · Cited by 243 · see citing works
Yao, J., & Dewald, J. P. A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.036 · Cited by 153 · see citing works
An Evaluation of Methods for Neuromagnetic Image Reconstruction
Jeffs, B. D., Leahy, R. M., & Singh, M. (1987) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1987.325996 · Cited by 149 · see citing works
The localization of equivalent dipoles of EEG sources by the application of electrical field theory
Henderson, C. J., Butler, S. R., & Glass, A. (1975) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(75)90002-4 · Cited by 146 · see citing works
Estimation of in vivo brain-to-skull conductivity ratio in humans
Zhang, Y., van Drongelen, W., & He, B. (2006) · Applied Physics Letters · doi:10.1063/1.2398883 · Cited by 143 · see citing works
Self-citationFriedemann Pulvermüller, A., Юрьевич, Ш. Ю., & Ilmoniemi, R. J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00356-2 · Cited by 109 · see citing works
Metz, C. E., & Fencil, L. E. (1989) · Medical Physics · doi:10.1118/1.596401 · Cited by 108 · see citing works
Brain electric source imaging: scalp Laplacian mapping and cortical imaging.
He, B. (1999) · PubMed · Cited by 97 · see citing works
Self-citationHe, B., Zhang, X., Lian, J., Sasaki, H., Wu, D., & Towle, V. L. (2002) · NeuroImage · doi:10.1006/nimg.2002.1127 · Cited by 95 · see citing works
Self-citationFripp, A. L., Fripp, J. B., & Fripp, M. (2003) · Elsevier eBooks · doi:10.1016/b978-075067535-2/50010-3 · Cited by 89 · see citing works
A MATLAB toolbox for optimization over symmetric cones
Strum, J. F. (2001) · Medical Entomology and Zoology · Cited by 87 · see citing works
A method for localization of sources of human cerebral potentials evoked by sensory stimuli.
Sidman, R. D., Giambalvo, V., Allison, T., & Bergey, P. D. (1978) · PubMed · Cited by 66 · see citing works
He, B., Yao, D., Lian, J., & Wu, D. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.991155 · Cited by 61 · see citing works
Self-citationTowle, V. L., Khorasani, L., Uftring, S. J., Pelizzari, C., Erickson, R. K., Spire, J., Hoffmann, K., Chu, D., & Scherg, M. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00147-2 · Cited by 59 · see citing works
Valeriani, M., Pera, D. L., Niddam, D. M., Arendt‐Nielsen, L., & Chen, A. C. N. (2000) · Muscle & Nerve · doi:10.1002/1097-4598(200008)23:8 · Cited by 51 · see citing works
Ding, L., Lai, Y. L., & He, B. (2004) · Physics in Medicine and Biology · doi:10.1088/0031-9155/50/1/004 · Cited by 49 · see citing works
Self-citationEvaluation of L1 and L2 minimum norm performances on EEG localizations
Silva, C., Maltez, J., Trindade, E. A., Arriaga, A., & Ducla‐Soares, E. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.02.009 · Cited by 43 · see citing works
Electrophysiological Neuroimaging
He, B., & Lian, J. (2007) · Springer eBooks · doi:10.1007/0-306-48610-5_7 · Cited by 23 · see citing works
Self-citation
Flagged first, then by citations
1 of the 36 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Wagner M Wischmann HA Fuchs M Kohler T Drenckhahn R 1998 Current density reconstruction using the L1 norm Advances in Biomagnetism Research Biomag96, Springer-Verlag, New York 393 396
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He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297 · see citing works
Self-citationElectrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities
He, B., Yang, L., Wilke, C., & Yuan, H. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2139210 · Cited by 300 · see citing works
Self-citationElectrophysiological Brain Connectivity: Theory and Implementation
He, B., Laura Astolfi, A., 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 236 · see citing works
Self-citationA distributed spatio-temporal EEG/MEG inverse solver
Wanmei Ou, A., Hämäläinen, M., & Golland, P. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.063 · Cited by 222 · see citing works
Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
He, B., & Liu, Z. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008233 · Cited by 171 · see citing works
Self-citationA Simulation Framework for Benchmarking EEG-Based Brain Connectivity Estimation Methodologies
Haufe, S., & Ewald, A. (2016) · Brain Topography · doi:10.1007/s10548-016-0498-y · Cited by 131 · see citing works
(2019) · Handbook of clinical neurology · doi:10.1016/b978-0-444-64032-1.00006-0 · Cited by 134 · see citing works
Self-citationGhosh, S., & Rudy, Y. (2009) · Annals of Biomedical Engineering · doi:10.1007/s10439-009-9665-6 · Cited by 120 · see citing works
Large-scale EEG/MEG source localization with spatial flexibility
Haufe, S., Tomioka, R., Dickhaus, T., Sannelli, C., Blankertz, B., Nolte, G., & Müller, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.003 · Cited by 99 · see citing works
Reconstructing cortical current density by exploring sparseness in the transform domain
Ding, L. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/9/006 · Cited by 99 · see citing works
Self-citationBrain-Source Imaging: From sparse to tensor models
Becker, H., Albera, L., Comon, P., Gribonval, R., Wendling, F., & Merlet, I. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2413711 · Cited by 94 · see citing works
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
Dähne, S., Bieszmann, F., Samek, W., Haufe, S., Goltz, D., Gundlach, C., Villringer, A., Fazli, S., & Müller, K. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2425807 · Cited by 88 · see citing works
Sohrabpour, A., Lu, Y., Worrell, G., & He, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.064 · Cited by 89 · see citing works
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Self-citationThree-dimensional brain current source reconstruction from intra-cranial ECoG recordings
Zhang, Y., van Drongelen, W., Kohrman, M. H., & He, B. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.263 · Cited by 69 · see citing works
Self-citationSolving the EEG inverse problem based on space–time–frequency structured sparsity constraints
Castaño-Candamil, S., Höhne, J., Martínez-Vargas, J.-D., An, X., Castellanos-Dominguez, G., & Haufe, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.052 · Cited by 61 · see citing works
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Michel, C. M., & He, B. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190228484.003.0045 · Cited by 55 · see citing works
Self-citationSpatially sparse source cluster modeling by compressive neuromagnetic tomography
Chang, W.-T., Nummenmaa, A., Hsieh, J.-C., & Lin, F.-H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.013 · Cited by 52 · see citing works
How to use fMRI functional localizers to improve EEG/MEG source estimation
Cottereau, B. R., Ales, J. M., & Norcia, A. M. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.07.015 · Cited by 52 · see citing works
Systems Neuroengineering: Understanding and Interacting with the Brain
Edelman, B. J., Johnson, N., Sohrabpour, A., Tong, S., Thakor, N., & He, B. (2015) · Engineering · doi:10.15302/j-eng-2015078 · Cited by 45 · see citing works
Self-citationDCT based multi-head attention-BiGRU model for EEG source location
Zhang, B., Li, D., & Wang, D. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106171 · Cited by 41 · see citing works
Liu, K., Yu, Z. L., Wu, W., Gu, Z., Li, Y., & Nagarajan, S. S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.027 · Cited by 40 · see citing works
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 · see citing works
Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging
Ding, L., & Yuan, H. (2011) · Human Brain Mapping · doi:10.1002/hbm.21473 · Cited by 38 · see citing works
Self-citationRobust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging
Cai, C., Diwakar, M., Chen, D., Sekihara, K., & Nagarajan, S. S. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2932290 · Cited by 39 · see citing works
Karoui, A., Bear, L. R., Migerditichan, P., & Zemzemi, N. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01708 · Cited by 37 · see citing works
Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources Based on Matrix Factorization
Liu, K., Yu, Z. L., Wu, W., Gu, Z., Zhang, J., Cen, L., Nagarajan, S. S., & Li, Y. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2890291 · Cited by 35 · see citing works
Xu, P., Tian, Y., Lei, X., & Yao, D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.106 · Cited by 32 · see citing works
On epicardial potential reconstruction using regularization schemes with the L1-norm data term
Shou, G., Xia, L., Liu, F., Jiang, M., & Crozier, . (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/1/004 · Cited by 29 · see citing works
Lascano, A. M., Grouiller, F., Genetti, M., Spinelli, L., Seeck, M., Schaller, K., & Michel, C. M. (2014) · Neurosurgery · doi:10.1227/neu.0000000000000298 · Cited by 29 · see citing works
Electrophysiological Mapping and Neuroimaging
He, B., & Ding, L. (2013) · Neural Engineering · doi:10.1007/978-1-4614-5227-0_12 · Cited by 26 · see citing works
Self-citationAhlfors, S. P., & Mody, M. (2016) · Organizational Research Methods · doi:10.1177/1094428116676344 · Cited by 27 · see citing works
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder–Decoder Network
Huang, G., Liu, K., Liang, J., Cai, C., Gu, Z. H., Qi, F., Li, Y., Yu, Z. L., & Wu, W. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3209925 · Cited by 27 · see citing works
Sparse EEG/MEG source estimation via a group lasso
Lim, M., Ales, J. M., Cottereau, B. R., Hastie, T. J., & Norcia, A. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176835 · Cited by 24 · see citing works
Liao, K., Zhu, M., & Ding, L. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.04.015 · Cited by 24 · see citing works
Self-citationSTRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging
Liu, K., Yu, Z. L., Wu, W., Gu, Z., & Li, Y. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500161 · Cited by 25 · see citing works
Sparse imaging of cortical electrical current densities via wavelet transforms
Liao, K., Zhu, M., Ding, L., Valette, S., Zhang, W., & Dickens, D. L. (2012) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/21/6881 · Cited by 23 · see citing works
Self-citationJaiswal, S., Purpura, S. R., Manchanda, J. K., Nan, J., Azeez, N., Ramanathan, D., & Mishra, J. (2024) · JMIR Mental Health · doi:10.2196/49467 · Cited by 27 · see citing works
Dissociable neural mechanisms of cognition and well-being in youth versus healthy aging.
Grennan, G., Balasubramani, P. P., Vahidi, N. W., Ramanathan, D., Jeste, D. V., & Mishra, J. (2022) · Psychology and Aging · doi:10.1037/pag0000710 · Cited by 21 · see citing works
Wang, L., Qin, J., Wong, T. T., & Heng, P. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/19/009 · Cited by 21 · see citing works
Variation sparse source imaging based on conditional mean for electromagnetic extended sources
Liu, K., Yu, Z. L., Wu, W., Gu, Z., Li, Y., & Nagarajan, S. S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.004 · Cited by 20 · see citing works
s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography
Li, Y., Qin, J., Hsin, Y.-L., Osher, S., & Liu, W. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00543 · Cited by 20 · see citing works
A New fMRI Informed Mixed-Norm Constrained Algorithm for EEG Source Localization
Wang, H., Lei, X., Zhan, Z., Yao, L., & Wu, X. (2018) · IEEE Access · doi:10.1109/access.2018.2792442 · Cited by 19 · see citing works
Utility of Cognitive Neural Features for Predicting Mental Health Behaviors
Kato, R., Balasubramani, P. P., Ramanathan, D., & Mishra, J. (2022) · Sensors · doi:10.3390/s22093116 · Cited by 18 · see citing works
Jiao, M., Yang, S., Xian, X., Fotedar, N., & Liu, F. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3424669 · Cited by 18 · see citing works
Saha, S., Nesterets, Y. I., Tahtalı, M., & Gureyev, T. E. (2015) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/1/4/045206 · Cited by 18 · see citing works
Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition
Hansen, S. T., Hemakom, A., Safeldt, M. G., Krohne, L. G., Madsen, K. H., Siebner, H. R., Mandic, D. P., & Hansen, L. K. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/5618303 · Cited by 17 · see citing works
Liu, K., Yu, Z. L., Wu, W., Gu, Z., & Li, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.038 · Cited by 17 · see citing works
A two-way regularization method for MEG source reconstruction
Tian, T. S., Huang, J. Z., Shen, H., & Li, Z. (2012) · The Annals of Applied Statistics · doi:10.1214/11-aoas531 · Cited by 17 · see citing works
A two-way regularization method for MEG source reconstruction
Tian, T. S., Huang, J. Z., Shen, H., & Li, Z. (2012) · OakTrust (Texas A&M University Libraries) · Cited by 19 · see citing works
Distinct neural activations correlate with maximization of reward magnitude versus frequency
Balasubramani, P. P., Diaz-Delgado, J., Grennan, G., Alim, F., Zafar-Khan, M., Maric, V., Ramanathan, D., & Mishra, J. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac482 · Cited by 16 · see citing works
Electro- and Magneto-Encephalography in the Study of Emotion
Keil, A. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9780511843716.007 · Cited by 16 · see citing works
Total Variation Regularization in Electrocardiographic Mapping
Shou, G., Xia, L., & Jiang, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15615-1_7 · Cited by 15 · see citing works
Neural dynamics during emotional video engagement relate to anxiety
Nan, J., Balasubramani, P. P., Ramanathan, D., & Mishra, J. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.993606 · Cited by 15 · see citing works
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Self-citationThreshold selection for group sparsity
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Koulouri, A., Rimpiläinen, V., Brookes, M., & Kaipio, J. P. (2016) · Applied Numerical Mathematics · doi:10.1016/j.apnum.2016.01.005 · Cited by 15 · see citing works
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Ahlfors, S. P., & Hämäläinen, M. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511979958.007 · Cited by 14 · see citing works
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Ojeda, A., Wagner, M., Maric, V., Ramanathan, D., & Mishra, J. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108445 · Cited by 13 · see citing works
Ramanathan, D., Nan, J., Grennan, G., Jaiswal, S., Purpura, S. R., Manchanda, J. K., Maric, V., Balasubramani, P. P., & Mishra, J. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100384 · Cited by 13 · see citing works
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Discriminating hand gesture motor imagery tasks using cortical current density estimation
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Xu, F., Liu, K., Yu, Z., Deng, X., & Wang, G. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05603-1 · Cited by 10 · see citing works
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Wahbeh, H., Cannard, C., Kriegsman, M., & Arnaud Delorme, A. (2023) · Progress in brain research · doi:10.1016/bs.pbr.2022.12.011 · Cited by 7 · see citing works
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Point source reconstruction principle of linear inverse problems
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Ahmad, M. F., Murphy, J., Vatansever, D., Stamatakis, E. A., & Godsill, S. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2599010 · Cited by 5 · see citing works
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Source Localization for Brain-Computer Interfaces
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Electromagnetic Source Imaging via a Data-Synthesis-Based Denoising Autoencoder.Preprint
Huang, G., Yu, Z. L., Wu, W., Liu, K., Gu, Z., Qi, F., Li, Y., & Liang, J. (2020) · arXiv (Cornell University) · Cited by 4 · see citing works
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Jaiswal, S., Nan, J., Purpura, S. R., Manchanda, J. K., Yogaratnam, N., Ramanathan, D., & Mishra, J. (2024) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2024.112486 · Cited by 3 · see citing works
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Self-citation
- Authors
- Ding, L., & He, B.
- Affiliations
- University of Minnesota (US)
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2007-09-25
- Volume
- 29
- Issue
- 9
- Pages
- 1053-1067
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC2612127
- OpenAlex
- W2085300014
- Cited by
- 131
- References
- 36
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 12 · 2023: 5 · 2022: 11 · 2021: 8 · 2020: 3 · 2019: 6 · 2018: 7 · 2017: 3 · 2016: 9 · 2015: 8 · 2014: 7 · 2013: 8 · 2012: 10
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01-EB-00178, R01EB000178)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Mathematics › Mathematical Physics › Numerical methods in inverse problems
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Keywords
- L1 regularization, second-order cone programming, somatosensory evoked potentials, MEG source imaging
- MeSH terms
- Computer Simulation, Electroencephalography, Evoked Potentials, Somatosensory, Humans, Models, Neurological
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