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52. Z. Liu, B. He, fMRI–EEG integrated cortical source imaging by use of time-variant spatial
constraints. Neuroimage 39(3), 1198–1214 (2008)
53. M. Luessi, S.D. Babacan, R. Molina et al., Bayesian symmetrical EEG/fMRI fusion with
spatially adaptive priors. Neuroimage 55(1), 113–132 (2011)
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Biomagnetic Fields (Oxford University Press, USA, 1995)
55. E. Martınez-Montes, P.A. Valdés-Sosa, F. Miwakeichi et al., Concurrent EEG/fMRI analysis
by multiway partial least squares. NeuroImage 22(3), 1023–1034 (2004)
56. T. Medani, D. Lautru, D. Schwartz et al., FEM method for the EEG forward problem and
improvement based on modification of the saint venant’s method. Prog Electromagn Res 153,
11–22 (2015)
57. J.W. Meijs, O.W. Weier, M.J. Peters, A. Van Oosterom, On the numerical accuracy of the
boundary element method (EEG application). IEEE Trans. Biomed. Eng. 36(10), 1038–1049
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58. M.M. Monti, Statistical analysis of fMRI time-series: a critical review of the GLM approach.
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59. M. Moosmann, T. Eichele, H. Nordby et al., Joint independent component analysis for simultaneous EEG–fMRI: principle and simulation. Int. J. Psychophysiol. 67(3), 212–221 (2008)
60. T. Nguyen, T. Potter, T. Nguyen, et al., EEG source imaging guided by spatiotemporal specific
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61. T. Nguyen, T. Potter, R. Grossman, Y. Zhang, Characterization of dynamic changes of current
source localization based on spatiotemporal fMRI constrained EEG source imaging. J. Neural
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62. T.F. Oostendorp, J. Delbeke, D.F. Stegeman, The conductivity of the human skull: results of
in vivo and in vitro measurements. IEEE Trans. Biomed. Eng. 47(11), 1487–1492 (2000)
63. D. Ostwald, C. Porcaro, A.P. Bagshaw, An information theoretic approach to EEG–fMRI
integration of visually evoked responses. Neuroimage 49(1), 498–516 (2010)
64. R.D. Pascual-Marqui, C.M. Michel, D. Lehmann, Low resolution electromagnetic tomography:
a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18(1), 49–65
(1994)
65. R.D. Pascual-Marqui, Review of methods for solving the EEG inverse problem. Int. J. Bioeletromagn. 1(1), 75–86 (1999)
66. R.D. Pascual-Marqui, Standardized low-resolution brain electromagnetic tomography
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67. S. Peca, C.R. McCreary, E. Donaldson et al., Neurovascular decoupling is associated with
severity of cerebral amyloid angiopathy. Neurology 81(19), 1659–1665 (2013)
68. Y. Peng, J. He, B. Yao et al., Motor unit number estimation based on high-density surface
electromyography decomposition. Clin. Neurophysiol. 127(9), 3059–3065 (2016)
69. T.K. Perrachione, S.S. Ghosh, Optimized design and analysis of sparse-sampling FMRI experiments. Front. Neurosci. 7, 55 (2013)
70. S.E. Robinson, J. Vrba, Functional Neuroimaging by Synthetic Aperture Magnetometry (SAM).
Recent Advances in Biomagnetism (Tokyo University Press, Sendai, Japan, 1999), pp 302–305
71. M. Rosa, J. Daunizeau, K.J. Friston, EEG-fMRI integration: a critical review of biophysical
modeling and data analysis approaches. J. Integr. Neurosci. 9(04), 453–476 (2010)
72. S. Rush, D.A. Driscoll, Current distribution in the brain from surface electrodes. Anesth. Analg.
47(6), 717–723 (1968)
73. S. Rush, D.A. Driscoll, EEG electrode sensitivity-an application of reciprocity. IEEE Trans.
Biomed. Eng. 1, 15–22 (1969)
Y. Zhang
50. Y. Liu, Y. Ning, S. Li et al., Three-dimensional innervation zone imaging from multi-channel
surface EMG recordings. Int. J. Neural Syst. 25(06), 1550024 (2015)
51. Z. Liu, F. Kecman, B. He, Effects of fMRI–EEG mismatches in cortical current density estimation integrating fMRI and EEG: a simulation study. Clin. Neurophysiol. 117(7), 1610–1622
(2006)
52. Z. Liu, B. He, fMRI–EEG integrated cortical source imaging by use of time-variant spatial
constraints. Neuroimage 39(3), 1198–1214 (2008)
53. M. Luessi, S.D. Babacan, R. Molina et al., Bayesian symmetrical EEG/fMRI fusion with
spatially adaptive priors. Neuroimage 55(1), 113–132 (2011)
54. J. Malmivuo, R. Plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and
Biomagnetic Fields (Oxford University Press, USA, 1995)
55. E. Martınez-Montes, P.A. Valdés-Sosa, F. Miwakeichi et al., Concurrent EEG/fMRI analysis
by multiway partial least squares. NeuroImage 22(3), 1023–1034 (2004)
56. T. Medani, D. Lautru, D. Schwartz et al., FEM method for the EEG forward problem and
improvement based on modification of the saint venant’s method. Prog Electromagn Res 153,
11–22 (2015)
57. J.W. Meijs, O.W. Weier, M.J. Peters, A. Van Oosterom, On the numerical accuracy of the
boundary element method (EEG application). IEEE Trans. Biomed. Eng. 36(10), 1038–1049
(1989)
58. M.M. Monti, Statistical analysis of fMRI time-series: a critical review of the GLM approach.
Front. Hum. Neurosci. 5, 28 (2011)
59. M. Moosmann, T. Eichele, H. Nordby et al., Joint independent component analysis for simultaneous EEG–fMRI: principle and simulation. Int. J. Psychophysiol. 67(3), 212–221 (2008)
60. T. Nguyen, T. Potter, T. Nguyen, et al., EEG source imaging guided by spatiotemporal specific
fMRI: toward an understanding of dynamic cognitive processes. Neural Plast. (2016)
61. T. Nguyen, T. Potter, R. Grossman, Y. Zhang, Characterization of dynamic changes of current
source localization based on spatiotemporal fMRI constrained EEG source imaging. J. Neural
Eng. (2017)
62. T.F. Oostendorp, J. Delbeke, D.F. Stegeman, The conductivity of the human skull: results of
in vivo and in vitro measurements. IEEE Trans. Biomed. Eng. 47(11), 1487–1492 (2000)
63. D. Ostwald, C. Porcaro, A.P. Bagshaw, An information theoretic approach to EEG–fMRI
integration of visually evoked responses. Neuroimage 49(1), 498–516 (2010)
64. R.D. Pascual-Marqui, C.M. Michel, D. Lehmann, Low resolution electromagnetic tomography:
a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18(1), 49–65
(1994)
65. R.D. Pascual-Marqui, Review of methods for solving the EEG inverse problem. Int. J. Bioeletromagn. 1(1), 75–86 (1999)
66. R.D. Pascual-Marqui, Standardized low-resolution brain electromagnetic tomography
(sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl D), 5–12 (2002)
67. S. Peca, C.R. McCreary, E. Donaldson et al., Neurovascular decoupling is associated with
severity of cerebral amyloid angiopathy. Neurology 81(19), 1659–1665 (2013)
68. Y. Peng, J. He, B. Yao et al., Motor unit number estimation based on high-density surface
electromyography decomposition. Clin. Neurophysiol. 127(9), 3059–3065 (2016)
69. T.K. Perrachione, S.S. Ghosh, Optimized design and analysis of sparse-sampling FMRI experiments. Front. Neurosci. 7, 55 (2013)
70. S.E. Robinson, J. Vrba, Functional Neuroimaging by Synthetic Aperture Magnetometry (SAM).
Recent Advances in Biomagnetism (Tokyo University Press, Sendai, Japan, 1999), pp 302–305
71. M. Rosa, J. Daunizeau, K.J. Friston, EEG-fMRI integration: a critical review of biophysical
modeling and data analysis approaches. J. Integr. Neurosci. 9(04), 453–476 (2010)
72. S. Rush, D.A. Driscoll, Current distribution in the brain from surface electrodes. Anesth. Analg.
47(6), 717–723 (1968)
73. S. Rush, D.A. Driscoll, EEG electrode sensitivity-an application of reciprocity. IEEE Trans.
Biomed. Eng. 1, 15–22 (1969)