ArticleFrontiers in neuroscience2019
A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Simultaneous real-time EEG-fMRI neurofeedback: A systematic review.Frontiers in human neuroscience · 2023Pooled it
- Theta/Beta Ratio Neurofeedback Effects on Resting and Task-Related Theta Activity in Children with ADHD.Applied psychophysiology and biofeedback · 2025Trial
- EEG-fMRI neurofeedback versus motor imagery after stroke, a randomized controlled trial.Journal of neuroengineering and rehabilitation · 2025Trial
- AI-Driven Multimodal Brain-State Decoding for Personalized Closed-Loop TENS: A Comprehensive Review.Brain sciences · 2025Review
- Amygdala EFP Neurofeedback Effects on PTSD Symptom Clusters and Emotional Regulation Processes.Journal of clinical medicine · 2025Article
- Neural networks and foundation models: two strategies for EEG-to-fMRI prediction.Frontiers in systems biology · 2025Article
- EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.Human brain mapping · 2024Article
- Test-retest reliability of EEG microstate metrics for evaluating noise reductions in simultaneous EEG-fMRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation.NeuroImage · 2023Article
- EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI.NeuroImage · 2023Article
- Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal.Journal of neural engineering · 2022Article
- Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback.Frontiers in human neuroscience · 2022Article
- Suppressing of Power Line Artifact From Electroencephalogram Measurements Using Sparsity in Frequency Domain.Frontiers in neuroscience · 2021Article
- An Intelligence EEG Signal Recognition Method via Noise Insensitive TSK Fuzzy System Based on Interclass Competitive Learning.Frontiers in neuroscience · 2020Article
- Auto-Weighted Multi-View Discriminative Metric Learning Method With Fisher Discriminative and Global Structure Constraints for Epilepsy EEG Signal Classification.Frontiers in neuroscience · 2020Article
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4 authors.
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Abstract
Measures of brain activity through functional magnetic resonance imaging (fMRI) or electroencephalography (EEG), two complementary modalities, are ground solutions in the context of neurofeedback (NF) mechanisms for brain rehabilitation protocols. While NF-EEG (in which real-time neurofeedback scores are computed from EEG signals) has been explored for a very long time, NF-fMRI (in which real-time neurofeedback scores are computed from fMRI signals) appeared more recently and provides more robust results and more specific brain training. Using fMRI and EEG simultaneously for bi-modal neurofeedback sessions (NF-EEG-fMRI, in which real-time neurofeedback scores are computed from fMRI and EEG) is very promising for the design of brain rehabilitation protocols. However, fMRI is cumbersome and more exhausting for patients. The original contribution of this paper concerns the prediction of bi-modal NF scores from EEG recordings only, using a training phase where EEG signals as well as the NF-EEG and NF-fMRI scores are available. We propose a sparse regression model able to exploit EEG only to predict NF-fMRI or NF-EEG-fMRI in motor imagery tasks. We compared different NF-predictors stemming from the proposed model. We showed that predicting NF-fMRI scores from EEG signals adds information to NF-EEG scores and significantly improves the correlation with bi-modal NF sessions compared to classical NF-EEG scores.
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