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Related Experiment Videos

Improved algorithm for ballistocardiac artifact removal from EEG simultaneously recorded with fMRI.

Kyung Hwan Kim1, Hyo Woon Yoon, Hyun Wook Park

  • 1Dept. of Biomed. Eng., Yonsei Univ., Wonju, South Korea.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
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This study presents a new algorithm to remove heartbeat artifacts from simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) recordings. The novel method improves the quality of brain mapping data by effectively reducing noise.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) offer advanced spatiotemporal brain mapping capabilities.
  • Heartbeat artifacts in EEG recordings within fMRI scanners degrade data quality, posing a significant challenge.
  • Existing methods for artifact removal have limitations, necessitating novel approaches.

Purpose of the Study:

  • To develop and validate a novel algorithm for the effective removal of heartbeat artifacts from simultaneous EEG/fMRI data.
  • To overcome the limitations of previous heartbeat artifact removal techniques.
  • To improve the quality of high spatiotemporal resolution brain mapping.

Main Methods:

  • A multi-stage artifact removal process involving mean artifact waveform subtraction.

Related Experiment Videos

  • Selective removal of wavelet coefficients for noise reduction.
  • Application of recursive least-square adaptive filtering when initial methods are insufficient, without requiring a dedicated reference sensor.

    • Main Results:

    • The developed algorithm demonstrated effective removal of heartbeat artifacts in simulated and experimental EEG/fMRI data.
    • Performance was validated across diverse spectral characteristics, including normal alpha-wave dominant EEG and epileptic EEG.
    • The recursive least-square adaptive filtering component proved valuable for challenging artifact scenarios.

    Conclusions:

    • The novel algorithm successfully mitigates heartbeat artifacts in simultaneous EEG/fMRI recordings.
    • This method enhances the reliability and quality of data for brain mapping studies.
    • The approach offers a robust solution for improving EEG/fMRI data acquisition.