Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Biomedical Engineering Research Institute, Xian, China.
Clin EEG Neurosci. 2010 Jan;41(1):53-9. doi: 10.1177/155005941004100111.
The electroencephalogram (EEG) is often contaminated by electromyography (EMG). In this paper, a novel and robust technique is presented to eliminate EMG artifacts from EEG signals in real-time. First, the canonical correlation analysis (CCA) method is applied on the simulated EEG data contaminated by EMG and electrooculography (EOG) artifacts for separating EMG artifacts from EEG signals. The components responsible for EMG artifacts are distinguished from those responsible for brain activity based on the relative low autocorrelation. We demonstrate that the CCA method is more suitable to reconstruct the EMG-free EEG data than independent component analysis (ICA) methods. In addition, by applying CCA to analyze a number of EEG data contaminated by EMG artifacts, a correlation threshold is determined using an unbiased procedure. Hence, CCA can be used to remove EMG artifacts automatically. Finally, an example is given to verify that, after EMG artifacts were removed successfully from the EEG data contaminated by EMG and EOG simultaneously, not only the underlying brain activity signals but the EOG artifacts are preserved with little distortion.
脑电图（EEG）经常受到肌电图（EMG）的干扰。本文提出了一种新颖而稳健的技术，可实时从 EEG 信号中消除 EMG 伪影。首先，将典型相关分析（CCA）方法应用于受肌电图和眼电图（EOG）伪影污染的模拟 EEG 数据，以将 EMG 伪影从 EEG 信号中分离出来。根据相对较低的自相关，将负责 EMG 伪影的分量与负责大脑活动的分量区分开来。我们证明 CCA 方法比独立成分分析（ICA）方法更适合重建无 EMG 的 EEG 数据。此外，通过应用 CCA 分析受 EMG 伪影污染的大量 EEG 数据，使用无偏程序确定相关阈值。因此，CCA 可用于自动去除 EMG 伪影。最后，给出了一个示例，验证了在成功地从同时受到 EMG 和 EOG 污染的 EEG 数据中去除 EMG 伪影后，不仅可以保留大脑活动信号，而且可以保留 EOG 伪影，几乎没有失真。
