Srivastava G, Crottaz-Herbette S, Lau K M, Glover G H, Menon V
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
Neuroimage. 2005 Jan 1;24(1):50-60. doi: 10.1016/j.neuroimage.2004.09.041.
Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for removing artifacts that are linearly and independently mixed with signals of interest. Here, we demonstrate and validate the usefulness of ICA in removing BCG artifacts from EEG data acquired in the MRI scanner. In accordance with our hypothesis that BCG artifacts are physiologically independent from EEG, it was found that ICA consistently resulted in five to six independent components representing the BCG artifact. Following removal of these components, a significant reduction in spectral power at frequencies associated with the BCG artifact was observed. We also show that our ICA-based procedures perform significantly better than noise-cancellation methods that rely on estimation and subtraction of averaged artifact waveforms from the recorded EEG. Additionally, the proposed ICA-based method has the advantage that it is useful in situations where ECG reference signals are corrupted or not available.
在MRI扫描仪中采集的脑电图（EEG）数据包含大量伪迹，其中最突出的一种是心冲击图（BCG）伪迹。BCG伪迹是由于与心动周期相关的血流脉动变化，导致EEG电极在磁场中移动而产生的。独立成分分析（ICA）是一种统计算法，可用于去除与感兴趣信号线性且独立混合的伪迹。在此，我们展示并验证了ICA在去除MRI扫描仪中采集的EEG数据中的BCG伪迹方面的有效性。根据我们的假设，即BCG伪迹在生理上与EEG无关，发现ICA始终能产生五到六个代表BCG伪迹的独立成分。去除这些成分后，观察到与BCG伪迹相关频率处的频谱功率显著降低。我们还表明，我们基于ICA的方法比依赖于从记录的EEG中估计和减去平均伪迹波形的噪声消除方法表现得更好。此外，所提出的基于ICA的方法具有在心电图参考信号损坏或不可用时也有用的优点。
