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EEG-Informed fMRI: A Review of Data Analysis Methods.

Rodolfo Abreu1, Alberto Leal2, Patrícia Figueiredo1

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Simultaneously acquiring electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offers insights into brain function. This review details methods for analyzing this complex data, focusing on artifact correction and signal integration for better brain activity research.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a powerful non-invasive technique for studying human brain function.
  • Despite its promise, challenges in data acquisition and analysis hinder widespread adoption and standardization.

Purpose of the Study:

  • To review current methodologies for analyzing simultaneously acquired EEG and fMRI data.
  • To address challenges in pre-processing, artifact correction, and data integration for EEG-fMRI research.

Main Methods:

  • Surveying pre-processing techniques for both EEG (MR-induced artifact correction) and fMRI (hardware artifact mitigation, physiological noise removal).
  • Reviewing EEG-informed fMRI integration strategies for predicting the blood oxygenation level dependent (BOLD) signal.
  • Systematically examining methods for extracting neuronal features from EEG data.

Main Results:

  • Identified key MR-induced artifacts in EEG and hardware/physiological noise in fMRI that require specific correction methods.
  • Highlighted the common EEG-informed fMRI strategy for BOLD signal prediction.
  • Cataloged various approaches for EEG feature extraction relevant to neuronal activity.

Conclusions:

  • Standardized methodologies for EEG-fMRI data analysis are still evolving.
  • Effective artifact correction and signal integration are crucial for reliable EEG-fMRI research.
  • Further development in feature extraction will enhance the understanding of brain function using combined EEG-fMRI data.