Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.
Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.
Sci Rep. 2018 Jun 11;8(1):8902. doi: 10.1038/s41598-018-27187-6.
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.
脑电图 (EEG) 信号在进行功能磁共振成像 (fMRI) 同步记录时会受到强烈的伪影干扰。在这些伪影中，最具挑战性的是心动球描记术 (BCG) 伪影，因为它与持续的心脏活动相关联，具有复杂的时空动态。EEG 数据中存在 BCG 残留可能会掩盖真实的，或在 EEG 和 fMRI 时程之间产生虚假的相关性。在这里，我们提出了一种用于去除 BCG 伪影的自适应最优基集 (aOBS) 方法。我们的方法是自适应的，因为它可以逐拍估计心脏活动和 BCG 发生之间的延迟。通过更准确地对齐 BCG 的发生，因此可以确保通过主成分分析 (PCA) 有效地创建最优基集。此外，aOBS 可以自动估计 PCA 产生的哪些分量可能与 BCG 伪影相关，因此需要去除。aOBS 的性能在健康受试者进行视觉刺激时进行同步 fMRI 的高密度 EEG 数据上进行了评估。由于 aOBS 能够在保留脑信号的同时有效减少 BCG 残留，因此我们建议它可能在同步 EEG-fMRI 研究中得到广泛应用。
