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Related Experiment Video

Updated: May 9, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Reproducible paired sources from concurrent EEG-fMRI data using BICAR.

Kevin S Brown1, Ryan Kasper, Barry Giesbrecht

  • 1Department of Physics, University of California, Santa Barbara, CA 93106, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, USA; Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA; Department of Marine Sciences, University of Connecticut, Groton, CT 06340, USA.

Journal of Neuroscience Methods
|August 13, 2013
PubMed
Summary

We developed BICAR, a new algorithm for robust electroencephalography-functional magnetic resonance imaging (EEG-fMRI) component pairing. BICAR objectively identifies reproducible neural signals for individual subjects, improving multimodal neuroimaging analysis.

Keywords:
Concurrent EEG-fMRIEEGIndependent component analysisMultimodal data fusionfMRI

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Last Updated: May 9, 2026

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) offers rich spatiotemporal brain activity insights.
  • Extracting reliable paired components from multimodal EEG-fMRI data presents significant challenges.

Purpose of the Study:

  • Introduce BICAR, an algorithm for robust and reproducible individual subject-level EEG-fMRI component pairing.
  • Provide a task-independent measure of component quality (reproducibility) to objectively discard spurious pairings.

Main Methods:

  • BICAR processes minimally preprocessed fMRI and EEG data.
  • Applies standard fMRI preprocessing (alignment, motion correction) and EEG artifact removal/filtering.
  • Derives a reproducibility cutoff for objective component selection.

Main Results:

  • BICAR successfully identified robust, reproducible paired temporal and spatial components in five subjects.
  • Identified components aligned with visual processing, motor planning, execution, and attention.
  • Demonstrated the flexibility of BICAR components for various downstream analyses.

Conclusions:

  • BICAR provides a reliable method for EEG-fMRI component analysis at the individual subject level.
  • The algorithm facilitates objective identification of biologically relevant neural signals.
  • BICAR enhances the utility of multimodal neuroimaging for understanding brain function.