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

Marco Marino,Quanying Liu,Dante Mantini,Vlastimil Koudelka,Nicole Wenderoth,Camillo Porcaro,Jaroslav Hlinka

doi:10.1038/s41598-018-27187-6

Abstract

Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.

Highlights

Despite the clear advantages of EEG-functional magnetic resonance imaging (fMRI) integration, the combination of the two techniques raises important technical challenges[14,20,21,22]
Based on these EEG and ECG data, we could proceed with the assessment of adaptive Optimal Basis Set (aOBS), our BCG artifact removal method, against artifact subtraction (AAS), Independent component analysis (ICA) and optimal basis set (OBS)
We assessed the performance of aOBS in terms of BCG artifact attenuation, by quantifying BCG residual intensity and maximum cross-correlation between EEG signals and ECG signal

Summary

Introduction

Despite the clear advantages of EEG-fMRI integration, the combination of the two techniques raises important technical challenges[14,20,21,22]. As BCG artifact occurrences are approximately time-locked to cardiac activity, AAS can be applied by averaging the EEG data epoched across successive cardiac cycles[29]. This method cannot take into account the large variability of the BCG artifact shape in any single EEG channel. Principal component analysis (PCA) is applied to EEG segments time-locked to the cardiac events, and the first components are used for adaptive artifact removal[36]. The performance of OBS is hampered by the fact that events for data epoching are typically identified on a simultaneously recorded ECG signal, assuming a fixed delay between the cardiac event and the artifactual occurrence in the EEG recording. The variability between ECG and BCG events[38,39,40] may have negative impact on the OBS performance

Methods

Results

Discussion

Conclusion