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Related Experiment Video

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration.

Giulia Lioi1, Claire Cury2, Lorraine Perronnet2

  • 1Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, F-3500, Rennes, France. giulia.lioi@inria.fr.

Scientific Data
|June 12, 2020
PubMed
Summary

This study presents the first open-access multimodal dataset combining electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) for real-time neurofeedback (NF). This valuable resource aids in advancing multimodal data integration and understanding motor imagery neuromarkers.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer complementary spatiotemporal resolution for brain activity monitoring.
  • Real-time implementation of neurofeedback (NF) loops using combined EEG and fMRI presents significant technical challenges.
  • Previous work has demonstrated the potential of simultaneous EEG-fMRI NF.

Purpose of the Study:

  • To introduce a novel, open-access multimodal dataset integrating simultaneous EEG and fMRI recordings during a motor imagery NF task.
  • To illustrate the data's utility for advancing multimodal data integration methodologies.
  • To facilitate research into improving NF quality, EEG denoising under MRI, and identifying motor-imagery neuromarkers.

Main Methods:

  • Simultaneous acquisition of EEG and fMRI data from 30 healthy volunteers during a motor imagery NF task.
  • Inclusion of supplementary MRI structural data.
  • Five training sessions per participant to ensure robust data collection.

Main Results:

  • The dataset represents one of the first simultaneous EEG-fMRI recordings specifically for NF applications.
  • It is the first open-access bimodal NF dataset integrating EEG and fMRI.
  • The data's potential for advancing multimodal integration, NF quality, EEG denoising, and neuromarker investigation is highlighted.

Conclusions:

  • The presented multimodal EEG-fMRI dataset is a valuable resource for the research community.
  • It will accelerate the development and validation of advanced neurofeedback techniques.
  • The dataset supports the investigation of neural mechanisms underlying motor imagery using integrated neuroimaging approaches.