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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings.

Zhongming Liu1, Jacco A de Zwart, Peter van Gelderen

  • 1Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20982-1065, USA. liuz5@mail.nih.gov

Neuroimage
|November 1, 2011
PubMed
Summary

This study introduces new algorithms to remove MRI gradient and cardiac artifacts from electroencephalography (EEG) data. The methods ensure high-quality EEG data for simultaneous EEG-fMRI research.

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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

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) offer rich insights into brain activity.
  • Artifacts from MRI gradient switching and cardiac pulsations contaminate EEG signals, hindering analysis.
  • Effective artifact removal is crucial for accurate interpretation of combined EEG-fMRI data.

Purpose of the Study:

  • To develop and validate algorithms for removing MRI gradient and cardiac artifacts from EEG data.
  • To improve the quality and reliability of EEG recordings acquired during fMRI scans.
  • To provide researchers with robust tools for artifact correction in simultaneous EEG-fMRI studies.

Main Methods:

  • Channel-wise filtering using Singular Value Decomposition (SVD) for gradient artifact removal.
  • Independent Component Analysis (ICA) and mutual information with ECG for pulse artifact identification and removal.
  • SVD filtering of remaining component time courses to eliminate cardiac-related temporal patterns.
  • Reconstruction of artifact-free multi-channel EEG time series.

Main Results:

  • Demonstrated excellent data quality and robust artifact removal performance.
  • Validated methods on extensive simultaneous EEG-fMRI datasets across various experimental conditions.
  • Achieved significant reduction in gradient and pulse artifact contamination.

Conclusions:

  • The proposed algorithms effectively remove MRI gradient and cardiac artifacts from EEG data.
  • The developed methods enhance the quality of simultaneous EEG-fMRI recordings.
  • A freely available Matlab toolbox facilitates the application of these artifact correction techniques in research.