Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics

Neurophysiological investigation of the basis of the fMRI signal

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EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

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Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep

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Simultaneous EEG and fMRI of the alpha rhythm

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Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep

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EEG microstate sequences in healthy humans at rest reveal scale-free dynamics.

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Biophysical and Physiological Origins of Blood Oxygenation Level-Dependent fMRI Signals

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Methods for cleaning the BOLD fMRI signal

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Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability

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EEG correlates of time-varying BOLD functional connectivity

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Tracking brain arousal fluctuations with fMRI

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Neuromodulation of brain state and behavior

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Simultaneous EEG–fMRI

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Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

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Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

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Alpha-Band Phase Synchrony Is Related to Activity in the Fronto-Parietal Adaptive Control Network

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EEG Signatures of Dynamic Functional Network Connectivity States

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Subcortical evidence for a contribution of arousal to fMRI studies of brain activity

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Functional Magnetic Resonance Imaging Methods

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Infra-Slow EEG Fluctuations Are Correlated with Resting-State Network Dynamics in fMRI

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Layer-dependent activity in human prefrontal cortex during working memory

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Fast fMRI can detect oscillatory neural activity in humans

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Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

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EEG-Informed fMRI: A Review of Data Analysis Methods

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Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

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The relationship between oscillatory EEG activity and the laminar-specific BOLD signal

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Resting-state “physiological networks”

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Layer-dependent functional connectivity methods

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Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

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Intracranial Electrophysiology Reveals Reproducible Intrinsic Functional Connectivity within Human Brain Networks

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The Neural Basis of Time-Varying Resting-State Functional Connectivity

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Ultra-Slow Single-Vessel BOLD and CBV-Based fMRI Spatiotemporal Dynamics and Their Correlation with Neuronal Intracellular Calcium Signals

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Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources

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Sympathetic activity contributes to the fMRI signal

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Finding thalamic BOLD correlates to posterior alpha EEG

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Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential

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Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal

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Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI

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Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity

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Cortical layers, rhythms and BOLD signals

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Advances in multimodal neuroimaging: Hybrid MR–PET and MR–PET–EEG at 3T and 9.4T

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On wakefulness fluctuations as a source of BOLD functional connectivity dynamics

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Template-based prediction of vigilance fluctuations in resting-state fMRI

Falahpour, M., Chang, C. E., Wong, C. W., & Liu, T. T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.012 · Cited by 79 · see citing works

Vascular physiology drives functional brain networks

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Simultaneous EEG–fMRI at ultra-high field: Artifact prevention and safety assessment

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Vigilance Effects in Resting-State fMRI

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A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates

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Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

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Connectivity dynamics from wakefulness to sleep

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EEG-fMRI Methods for the Study of Brain Networks during Sleep

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Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics

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Post-stimulus fMRI and EEG responses: Evidence for a neuronal origin hypothesised to be inhibitory

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Phase–amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task

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Physiological changes in sleep that affect fMRI inference

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Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses

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Pushing the spatio-temporal limits of MRI and fMRI

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Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

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Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality

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The relationship between negative BOLD responses and ERS and ERD of alpha/beta oscillations in visual and motor cortex

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The Quest for EEG Power Band Correlation with ICA Derived fMRI Resting State Networks

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Adapted cabling of an EEG cap improves simultaneous measurement of EEG and fMRI at 7T

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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

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Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

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Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI

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Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

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Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI

Dowdle, L. T., Ghose, G. M., Chen, C. C. C., Uǧurbil, K., Yacoub, E. S., & Vizioli, L. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102171 · Cited by 23 · see citing works

Multimodal Covariance Network Reflects Individual Cognitive Flexibility

Jiang, L., Eickhoff, S. B., Genon, S., Wang, G., Yi, C., He, R., Huang, X., Yao, D., Dong, D., Li, F., et al. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500187 · Cited by 18 · see citing works

Neuroplasticity-Informed Learning Under Cognitive Load: A Systematic Review of Functional Imaging, Brain Stimulation, and Educational Technology Applications

Gkintoni, E., Sortwell, A., Vassilopoulos, S. P., & Nikolaou, G. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti10010005 · Cited by 24 · see citing works

Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity

Phadikar, S., Pusuluri, K., Iraji, A., & Calhoun, V. D. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1484954 · Cited by 16 · see citing works

On the Role of Neural Oscillations Across Timescales in Speech and Music Processing

Gnanateja, G. N., Devaraju, D. S., Heyne, M., Quique, Y. M., Sitek, K. R., Tardif, M. C., Tessmer, R., & Dial, H. R. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.872093 · Cited by 11 · see citing works

Leveraging sinusoidal representation networks to predict fMRI signals from EEG

Li, Y., Lou, A., Xu, Z., Wang, S., & Chang, C. E. (2024) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3007677 · Cited by 9 · see citing works

Consistency of resting-state correlations between fMRI networks and EEG band power

Xavier, M., Esteves, I., Jorge, J., Abreu, R., Giraud, A., Sadaghiani, S., Wirsich, J., & Figueiredo, P. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.37 · Cited by 8 · see citing works

Review of the Brain’s Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

Irastorza-Valera, L., Gómez, E. S., Benítez, J. M., Montáns, F. J., & Saucedo‐Mora, L. (2024) · Biomimetics · doi:10.3390/biomimetics9060362 · Cited by 7 · see citing works

Future of Smart Classroom in the Era of Wearable NeurotechnologyPreprint

Taherisadr, M., Demirel, B. U., Faruque, M. A. A., & Elmalaki, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11475 · Cited by 6 · see citing works

Charting the neuroscience of interpersonal trust: A bibliographic literature review

Wu, Y. H., & Krüeger, F. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105930 · Cited by 6 · see citing works

EEG-Based Emotion Recognition by Retargeted Semi-Supervised Regression with Robust Weights

Chen, Z., Duan, S., & Peng, Y. K. (2022) · Systems · doi:10.3390/systems10060236 · Cited by 4 · see citing works

An EEG-based digital biomarker for personalizing transcranial magnetic stimulation in major depressive disorder

Wan, L., Chen, Y., Zhang, Q., He, S., Ye, Q., Wang, E., Yang, T., & Xie, W. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02603-8 · Cited by 3 · see citing works

Transforming of scalp EEGs with different channel locations by REST for comparative study

Dong, L., Yang, R., Xie, A., Wang, X., Feng, Z., Li, F., Ren, J., Li, J., & Yao, D. (2024) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2024.111064 · Cited by 3 · see citing works

Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology

Pourmotabbed, H., Clarke, D., Chang, C. E., & Abbas Babajani-feremi, A. (2024) · Communications Biology · doi:10.1038/s42003-024-06807-0 · Cited by 3 · see citing works

Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG representative learning

Fu, X., Wang, B., Guo, X., Liu, G., & Xiang, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107657 · Cited by 3 · see citing works

Understanding multi-modal brain network data: An immersive 3D visualization approach

Pester, B., Russig, B., Winke, O., Ligges, C., Dachselt, R., & Gumhold, S. (2022) · Computers & Graphics · doi:10.1016/j.cag.2022.05.024 · Cited by 3 · see citing works

Simultaneous and independent electroencephalography and magnetic resonance imaging: A multimodal neuroimaging dataset

Gallego-Rudolf, J., Corsi‐Cabrera, M., Concha, L., Ricardo-Garcell, J., & Pasaye, E. H. (2023) · Data in Brief · doi:10.1016/j.dib.2023.109661 · Cited by 3 · see citing works

Future perspectives of functional connectivity: Advances in data acquisition and analysis

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Enhancing Signal and Network Integrity: Evaluating BCG Artifact Removal Techniques in Simultaneous EEG-fMRI Data

Gülhan, P. G., & Özmen, G. (2025) · Sensors · doi:10.3390/s25227036 · Cited by 2 · see citing works

Analysing cognitive states from EEG signals with deep learning techniques

Singh, A. K., & Pal, D. (2025) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci63961.2025.11048335 · Cited by 1 · see citing works

东方正念的心理治疗机制与神经基础

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Low-dimensional representation of fMRI disentangles temporal contributions of EEG-determined vigilance from other global effects

Pourmotabbed, H., Vivek, A. S. D., Martin, C. G., Goodale, S. E., Wang, S., Morgan, V. L., Englot, D. J., & Chang, C. E. (2024) · SPIE Medical Imaging: Clinical and Biomedical Imaging · doi:10.1117/12.3005182 · Cited by 1 · see citing works

Topological Biomarkers of Alzheimer’s Disease from Functional Brain Network Analysis

Behrouzinia, S., & Khanteymoori, A. (2025) · Current Alzheimer Research · doi:10.2174/0115672050399190250815070642 · Cited by 1 · see citing works

Differentially Private Multimodal Laplacian Dropout (Dp-Mld) for Eeg Representative LearningPreprint

Fu, X., Wang, B., Guo, X., Liu, G., & Xiang, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4990230 · Cited by 1 · see citing works

Deep learning for EEG analysis

Lourenço, C. D. S. (2023) · doi:10.3990/1.9789036556910

Efficient subject identification based EEG using CNN with minimal electrodes

Mohammed, R. S., & Al-Hamadani, A. A. (2025) · AIP conference proceedings · doi:10.1063/5.0257713

Methods in Neurosociology

Pollock, J., Pfeiffer, M., Sigelmier, G., & Miller, C. (2025) · Handbooks of sociology and social research · doi:10.1007/978-3-031-95615-7_6

Current limitations in functional connectivity assessment: suggestions for analysis improvements

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Evoked potential-based sensory monitoring in chronic itch: toward objective assessment of neural hyperexcitability

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