Wang Kai, Li Wenjie, Dong Li, Zou Ling, Wang Changming
School of Information Science and Engineering, Changzhou University, Changzhou, China.
Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, China.
Front Neurosci. 2018 Feb 13;12:59. doi: 10.3389/fnins.2018.00059. eCollection 2018.
Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude () computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA ( < 0.005). data analysis, the Improvement of Normalized Power Spectrum () calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods ( < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications.
脑电图（EEG）记录与功能磁共振成像（fMRI）相结合，因其具有高空间和时间分辨率，在神经成像中发挥着潜在作用。然而，EEG很容易受到心冲击图（BCG）伪迹的影响，可能会导致对相关EEG特征（如癫痫棘波）的错误识别。有许多相关方法来去除它们，然而，这些方法没有考虑BCG伪迹的时变特征。本文提出了一种使用聚类算法捕捉BCG伪迹特征并结合约束独立成分分析（ccICA）来去除BCG伪迹的新方法。我们首先将该方法应用于模拟数据，该模拟数据是通过将BCG伪迹添加到从传统环境获得的EEG信号中构建的。然后，我们的方法在对10名健康受试者进行的EEG和fMRI实验中进行了测试，以证明其有效性。在模拟数据分析中，ccICA方法计算的信号幅度误差值（）低于包括AAS、OBS和cICA在内的其他方法（<0.005）。在数据分析中，ccICA方法计算的所有电极归一化功率谱（）的改善远高于AAS、OBS和cICA方法（<0.005）。我们还使用了其他评估指标（如功率分析）将我们的方法与其他传统方法进行比较。总之，我们的新方法在模拟和EEG数据测试中都成功且有效地去除了BCG伪迹，显示了在EEG-fMRI应用中去除伪迹的潜力。
