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Y. Zhang
References
1. S.P. Ahlfors, J. Han, J.W. Belliveau, M.S. Hämäläinen, Sensitivity of MEG and EEG to source
orientation. Brain Topogr. 23(3), 227–232 (2010)
2. S. Ahn, S.C. Jun, Multi-modal integration of EEG-fNIRS for brain-computer interfaces–current
limitations and future directions. Front. Hum. Neurosci. 11, 503 (2017)
3. T. Aihara, Y. Takeda, K. Takeda et al., Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior. Neuroimage 59(4), 4006–4021 (2012)
4. A. Babajani-Feremi, H. Soltanian-Zadeh, Multi-area neural mass modeling of EEG and MEG
signals. Neuroimage 52(3), 793–811 (2010)
5. A. Babajani, H. Soltanian-Zadeh, Integrated MEG/EEG and fMRI model based on neural
masses. IEEE Trans. Biomed. Eng. 53(9), 1794–1801 (2006)
6. F. Babiloni, D. Mattia, C. Babiloni et al., Multimodal integration of EEG, MEG and fMRI data
for the solution of the neuroimage puzzle. Magn. Reson. Imaging 22(10), 1471–1476 (2004)
7. S. Baillet, J.C. Mosher, R.M. Leahy, Electromagnetic brain mapping. IEEE Signal Process.
Mag. 18(6), 14–30 (2001)
8. S.B. Baumann, D.R. Wozny, S.K. Kelly, F.M. Meno, The electrical conductivity of human
cerebrospinal fluid at body temperature. IEEE Trans. Biomed. Eng. 44(3), 220–223 (1997)
9. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and blind
deconvolution. Neural Comput. 7(6), 1129–1159 (1995)
10. A. Bradley, J. Yao, J. Dewald, C.-P. Richter, Evaluation of electroencephalography source
localization algorithms with multiple cortical sources. PLoS ONE 11(1), e0147266 (2016)
11. M. Breakspear, Dynamic models of large-scale brain activity. Nat. Neurosci. 20(3), 340 (2017)
12. M.J. Brookes, C.M. Stevenson, G.R. Barnes et al., Beamformer reconstruction of correlated
sources using a modified source model. Neuroimage 34(4), 1454–1465 (2007)
13. R.B. Buxton, E.C. Wong, L.R. Frank, Dynamics of blood flow and oxygenation changes during
brain activation: the balloon model. Magn. Reson. Med. 39(6), 855–864 (1998)
14. R.B. Buxton, K. Uluda˘ g, D.J. Dubowitz, T.T. Liu, Modeling the hemodynamic response to
brain activation. Neuroimage 23, S220–S233 (2004)
15. V.D. Calhoun, T. Adali, G. Pearlson, K. Kiehl, Neuronal chronometry of target detection: fusion
of hemodynamic and event-related potential data. Neuroimage 30(2), 544–553 (2006)
16. D. Cohen, B.N. Cuffin, Demonstration of useful differences between magnetoencephalogram
and electroencephalogram. Electroencephalogr. Clin. Neurophysiol. 56(1), 38–51 (1983)
17. A.M. Dale, M.I. Sereno, Improved localizadon of cortical activity by combining EEG and MEG
with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5(2), 162–176
(1993)
18. A.M. Dale, B. Fischl, M.I. Sereno, Cortical surface-based analysis: I. Segmentation and surface
reconstruction. Neuroimage 9(2), 179–194 (1999)
19. A.M. Dale, A.K. Liu, B.R. Fischl et al., Dynamic statistical parametric mapping: combining
fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26(1), 55–67 (2000)
20. J. Daunizeau, C. Grova, J. Mattout et al., Assessing the relevance of fMRI-based prior in the
EEG inverse problem: a Bayesian model comparison approach. IEEE Trans. Signal Process.
53(9), 3461–3472 (2005)
21. J. Daunizeau, C. Grova, G. Marrelec et al., Symmetrical event-related EEG/fMRI information
fusion in a variational Bayesian framework. Neuroimage 36(1), 69–87 (2007)
22. B.G. Edwards, V.D. Calhoun, K.A. Kiehl, Joint ICA of ERP and fMRI during error-monitoring.
Neuroimage 59(2), 1896–1903 (2012)
23. S. Fazli, J. Mehnert, J. Steinbrink et al., Enhanced performance by a hybrid NIRS–EEG brain
computer interface. Neuroimage 59(1), 519–529 (2012)
24. B. Fischl, A. Liu, A.M. Dale, Automated manifold surgery: constructing geometrically accurate
and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging
20(1), 70–80 (2001)
Y. Zhang
References
1. S.P. Ahlfors, J. Han, J.W. Belliveau, M.S. Hämäläinen, Sensitivity of MEG and EEG to source
orientation. Brain Topogr. 23(3), 227–232 (2010)
2. S. Ahn, S.C. Jun, Multi-modal integration of EEG-fNIRS for brain-computer interfaces–current
limitations and future directions. Front. Hum. Neurosci. 11, 503 (2017)
3. T. Aihara, Y. Takeda, K. Takeda et al., Cortical current source estimation from electroencephalography in combination with near-infrared spectroscopy as a hierarchical prior. Neuroimage 59(4), 4006–4021 (2012)
4. A. Babajani-Feremi, H. Soltanian-Zadeh, Multi-area neural mass modeling of EEG and MEG
signals. Neuroimage 52(3), 793–811 (2010)
5. A. Babajani, H. Soltanian-Zadeh, Integrated MEG/EEG and fMRI model based on neural
masses. IEEE Trans. Biomed. Eng. 53(9), 1794–1801 (2006)
6. F. Babiloni, D. Mattia, C. Babiloni et al., Multimodal integration of EEG, MEG and fMRI data
for the solution of the neuroimage puzzle. Magn. Reson. Imaging 22(10), 1471–1476 (2004)
7. S. Baillet, J.C. Mosher, R.M. Leahy, Electromagnetic brain mapping. IEEE Signal Process.
Mag. 18(6), 14–30 (2001)
8. S.B. Baumann, D.R. Wozny, S.K. Kelly, F.M. Meno, The electrical conductivity of human
cerebrospinal fluid at body temperature. IEEE Trans. Biomed. Eng. 44(3), 220–223 (1997)
9. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and blind
deconvolution. Neural Comput. 7(6), 1129–1159 (1995)
10. A. Bradley, J. Yao, J. Dewald, C.-P. Richter, Evaluation of electroencephalography source
localization algorithms with multiple cortical sources. PLoS ONE 11(1), e0147266 (2016)
11. M. Breakspear, Dynamic models of large-scale brain activity. Nat. Neurosci. 20(3), 340 (2017)
12. M.J. Brookes, C.M. Stevenson, G.R. Barnes et al., Beamformer reconstruction of correlated
sources using a modified source model. Neuroimage 34(4), 1454–1465 (2007)
13. R.B. Buxton, E.C. Wong, L.R. Frank, Dynamics of blood flow and oxygenation changes during
brain activation: the balloon model. Magn. Reson. Med. 39(6), 855–864 (1998)
14. R.B. Buxton, K. Uluda˘ g, D.J. Dubowitz, T.T. Liu, Modeling the hemodynamic response to
brain activation. Neuroimage 23, S220–S233 (2004)
15. V.D. Calhoun, T. Adali, G. Pearlson, K. Kiehl, Neuronal chronometry of target detection: fusion
of hemodynamic and event-related potential data. Neuroimage 30(2), 544–553 (2006)
16. D. Cohen, B.N. Cuffin, Demonstration of useful differences between magnetoencephalogram
and electroencephalogram. Electroencephalogr. Clin. Neurophysiol. 56(1), 38–51 (1983)
17. A.M. Dale, M.I. Sereno, Improved localizadon of cortical activity by combining EEG and MEG
with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5(2), 162–176
(1993)
18. A.M. Dale, B. Fischl, M.I. Sereno, Cortical surface-based analysis: I. Segmentation and surface
reconstruction. Neuroimage 9(2), 179–194 (1999)
19. A.M. Dale, A.K. Liu, B.R. Fischl et al., Dynamic statistical parametric mapping: combining
fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26(1), 55–67 (2000)
20. J. Daunizeau, C. Grova, J. Mattout et al., Assessing the relevance of fMRI-based prior in the
EEG inverse problem: a Bayesian model comparison approach. IEEE Trans. Signal Process.
53(9), 3461–3472 (2005)
21. J. Daunizeau, C. Grova, G. Marrelec et al., Symmetrical event-related EEG/fMRI information
fusion in a variational Bayesian framework. Neuroimage 36(1), 69–87 (2007)
22. B.G. Edwards, V.D. Calhoun, K.A. Kiehl, Joint ICA of ERP and fMRI during error-monitoring.
Neuroimage 59(2), 1896–1903 (2012)
23. S. Fazli, J. Mehnert, J. Steinbrink et al., Enhanced performance by a hybrid NIRS–EEG brain
computer interface. Neuroimage 59(1), 519–529 (2012)
24. B. Fischl, A. Liu, A.M. Dale, Automated manifold surgery: constructing geometrically accurate
and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging
20(1), 70–80 (2001)