21.9.7.8 EEG-MRI, EEG-fMRI, Combined Analysis

Chapter Contents (Back)
Magnetic Resonance. MRI. fMRI. Electroencephalogram. EEG.

Jeong, J.W.[Jeong-Won], Kim, T.S.[Tae-Seong], Kim, S.H.[Sung-Heon], Singh, M.[Manbir],
Application of independent component analysis with mixture density model to localize brain alpha activity in fMRI and EEG,
IJIST(14), No. 4, 2004, pp. 170-180.
DOI Link 0408
BibRef

Wotawa, N.[Nicolas], Thirion, B.[Bertrand], Castet, E.[Eric], Anton, J.L.[Jean-Luc], Faugeras, O.D.[Olivier D.],
Human Retinotopic Mapping Using fMRI,
INRIARR-5472, 2005.
HTML Version. BibRef 0500

Deneux, T.[Thomas], Faugeras, O.D.[Olivier D.],
EEG-fMRI fusion of non-triggered data using Kalman filtering,
INRIARR-5760, 2005.
HTML Version. BibRef 0500
And:
Parameter estimation efficiency using nonlinear models in fMRI,
INRIARR-5758, 2005.
HTML Version. BibRef

Colliot, O.[Olivier], Camara, O.[Oscar], Bloch, I.[Isabelle],
Integration of fuzzy spatial relations in deformable models: Application to brain MRI segmentation,
PR(39), No. 8, August 2006, pp. 1401-1414.
Elsevier DOI Spatial relations; Deformable models; Fuzzy sets; MRI; Subcortical structures 0606
BibRef

Burguet, J., Gadi, N., Bloch, I.,
Realistic Models of Children Heads from 3D-MRI Segmentation and Tetrahedral Mesh Construction,
3DPVT04(631-638).
IEEE DOI 0412
BibRef

Pescatore, J., Garnero, L., Bloch, I.,
Tetrahedral 3D finite element meshes of head tissues from MRI for the MEG/EEG forward problem,
SCIA01(O-Tu3A). 0206
BibRef

Peterson, M.F.[Matthew F.], Das, K.[Koel], Sy, J.L.[Jocelyn L.], Li, S.[Sheng], Giesbrecht, B.[Barry], Kourtzi, Z.[Zoe], Eckstein, M.P.[Miguel P.],
Ideal observer analysis for task normalization of pattern classifier performance applied to EEG and fMRI data,
JOSA-A(27), No. 12, December 2010, pp. 2670-2683.
WWW Link. 1101
BibRef

Al-Baddai, S., Al-Subari, K., Tome, A.M., Volberg, G., Lang, E.W.,
A Combined EMD-ICA Analysis of Simultaneously Register EEG-fMRI Data,
BMVA(2015), No. 2, 2015, pp. 1-15.
PDF File. 1509
BibRef

Wen, X.T.[Xiao-Tong], Kang, M.X.[Ming-Xuan], Yao, L.[Li], Zhao, X.J.[Xiao-Jie],
Real-time ballistocardiographic artifact reduction using the k-teager energy operator detector and multi-channel referenced adaptive noise cancelling,
IJIST(26), No. 3, 2016, pp. 209-215.
DOI Link 1609
EEG-fMRI BibRef

Cruttenden, C.E.[Corey E.], Zhu, W.[Wei], Zhang, Y.[Yi], Zhu, X.H.[Xiao-Hong], Chen, W.[Wei], Rajamani, R.[Rajesh],
Toward Completely Sampled Extracellular Neural Recording During fMRI,
MedImg(41), No. 7, July 2022, pp. 1735-1746.
IEEE DOI 2207
Functional magnetic resonance imaging, Extracellular, Electroencephalography, Electrodes, Electric potential, Rats, singular value shrinkage BibRef

Yao, W.H.[Wei-Heng], Lyu, Z.H.[Zhi-Han], Mahmud, M.[Mufti], Zhong, N.[Ning], Lei, B.Y.[Bai-Ying], Wang, S.Q.[Shu-Qiang],
CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation,
MedImg(44), No. 7, July 2025, pp. 2757-2767.
IEEE DOI 2507
Electroencephalography, Brain, Functional magnetic resonance imaging, Spatial resolution, temporal super-resolution BibRef

Vinding, M.C.[Mikkel C.],
A Unified Framework for Reliability Analysis in Neuroimaging With Krippendorff's alpha,
IJIST(35), No. 5, 2025, pp. e70192.
DOI Link 2509
For MRI, PET, and M/EEG. Includes MATLAB code. data analysis, Krippendorff's alpha, MATLAB, neuroimaging, reliability analysis, reproducibility BibRef

Das, D.[Daisy], Deb, N.[Nabamita], Choudhury, S.S.[Saswati Sanyal],
Inter-Trial Coherence Reveals Enhanced Synchrony During Mantra Listening,
SPLetters(33), 2026, pp. 291-295.
IEEE DOI 2601
Coherence, Electroencephalography, Synchronization, Analysis of variance, Pregnancy, Oscillators, Filtering, neural synchrony BibRef

Morik, M.[Marco], Hashemi, A.[Ali], Müller, K.R.[Klaus-Robert], Haufe, S.[Stefan], Nakajima, S.[Shinichi],
Enhancing Brain Source Reconstruction by Initializing 3-D Neural Networks With Physical Inverse Solutions,
MedImg(45), No. 1, January 2026, pp. 231-242.
IEEE DOI 2601
Brain modeling, Electroencephalography, Data models, Location awareness, Inverse problems, Deep learning, Training, source localization BibRef


Phadikar, S.[Souvik], Agcaoglu, O.[Oktay], Calhoun, V.D.[Vince D],
Group Joint Independent Component Analysis (Group jICA): a Novel Method to Jointly Decompose and Link Simultaneous EEG and fMRI,
ICIP25(2277-2281)
IEEE DOI 2601
Data integration, Independent component analysis, Transforms, Functional magnetic resonance imaging, Electroencephalography, joint ICA BibRef

Fosco, C.[Camilo], Lahner, B.[Benjamin], Pan, B.[Bowen], Andonian, A.[Alex], Josephs, E.[Emilie], Lascelles, A.[Alex], Oliva, A.[Aude],
Brain Netflix: Scaling Data to Reconstruct Videos from Brain Signals,
ECCV24(XXVI: 457-474).
Springer DOI 2412
BibRef

Quan, R.J.[Rui-Jie], Wang, W.G.[Wen-Guan], Tian, Z.B.[Zhi-Bo], Ma, F.[Fan], Yang, Y.[Yi],
Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity,
CVPR24(233-243)
IEEE DOI 2410
Measurement, Bridges, Computational modeling, Functional magnetic resonance imaging, Brain modeling, Diffusion Model BibRef

Tu, T., Koss, J., Sajda, P.,
Relating Deep Neural Network Representations to EEG-fMRI Spatiotemporal Dynamics in a Perceptual Decision-Making Task,
Cognitive18(2066-20666)
IEEE DOI 1812
Electroencephalography, Face, Functional magnetic resonance imaging, Training, Automobiles, Spatiotemporal phenomena BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Cortex, Brain Waves, EEG Analysis, Electroencephalogram .


Last update:Feb 26, 2026 at 10:58:24