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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

Chung-Ki Wong1, Qingfei Luo1, Vadim Zotev1

  • 1Laureate Institute for Brain Research, Tulsa, OK, USA.

Journal of Neuroscience Methods
|April 4, 2018
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately detects cardioballistic artifact (BCG) periods directly from EEG data during simultaneous EEG-fMRI. This method eliminates the need for electrocardiogram (ECG) recordings, improving artifact removal in large datasets.

Keywords:
Cardioballistic artifact (BCG)EEGEEG-fMRIElectrocardiogram (ECG)Independent component analysis (ICA)Multiple-scale peak detection

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Area of Science:

  • Neuroimaging
  • Biomedical Signal Processing
  • Data Science

Background:

  • Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) requires accurate identification of cardioballistic artifact (BCG) periods in EEG for artifact removal.
  • Electrocardiogram (ECG) recording during fMRI is challenging, often leading to imprecise BCG period detection.

Purpose of the Study:

  • To develop and validate a novel algorithm for direct BCG cycle detection from EEG data during simultaneous EEG-fMRI.
  • To improve the accuracy and automation of BCG artifact removal in EEG-fMRI studies.

Main Methods:

  • Independent Component Analysis (ICA) to extract a stable BCG component from EEG.
  • A multiple-scale peak-detection algorithm applied to the extracted BCG component.
  • Band-pass filtering around the fundamental frequency and peak selection for cycle estimation.

Main Results:

  • The algorithm achieves high accuracy in detecting BCG periods on a large EEG-fMRI dataset.
  • Detection accuracy remains high across various heart rates without parameter adjustments.
  • Accurate detection is maintained even with reduced scan durations (down to 30 seconds).

Conclusions:

  • The proposed algorithm offers superior BCG detection accuracy compared to existing methods like fmrib_qrsdetect.
  • Eliminating the need for ECG recordings simplifies and automates EEG-fMRI data processing pipelines.
  • This advancement is crucial for efficient processing of large-scale EEG-fMRI datasets.