Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Signal Processing

Background:

• Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are powerful tools for studying brain activity.
• Ballistocardiographic (BCG) artifacts, arising from cardiac activity, significantly contaminate EEG signals during simultaneous fMRI acquisition.
• These BCG residuals can obscure true neural signals or introduce false correlations in EEG-fMRI analyses.

Purpose of the Study:

• To develop and evaluate an adaptive Optimal Basis Set (aOBS) method for effective removal of BCG artifacts from simultaneous EEG-fMRI data.
• To improve the accuracy of artifact removal by adaptively estimating the beat-to-beat delay between cardiac activity and BCG occurrence.
• To enhance the reliability of EEG-fMRI studies by preserving underlying brain signals.

Main Methods:

• An adaptive Optimal Basis Set (aOBS) method was proposed, utilizing principal component analysis (PCA).
• The method adaptively estimates the delay between cardiac activity and BCG occurrence on a beat-to-beat basis.
• aOBS automatically identifies and removes PCA components associated with BCG artifacts.

Main Results:

• The aOBS method demonstrated effective reduction of BCG residuals in high-density EEG data acquired during simultaneous fMRI.
• The method successfully preserved neural brain signals while removing artifactual components.
• Performance was validated in healthy subjects during visual stimulation tasks.

Conclusions:

• The adaptive Optimal Basis Set (aOBS) method provides an effective solution for BCG artifact removal in simultaneous EEG-fMRI.
• This technique enhances the quality of EEG data by preserving brain signals, making it suitable for robust EEG-fMRI analysis.
• aOBS is expected to be widely applicable in the field of simultaneous EEG-fMRI research.