Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram
Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram
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DOI：
10.1109/tbme.2006.879459
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发表时间：
2006-12-01
影响因子：
4.6
通讯作者：
Van Huffel, Sabine
中科院分区：
文献类型：
--
作者：
De Clercq, Wim;Vergult, Anneleen;Van Huffel, Sabine
脑电图（EEG）经常被肌肉伪影污染。本文提出了一种基于典型相关分析（CCA）盲源分离（BSS）技术的脑电信号肌肉伪迹去除新方法。该方法在合成数据集上进行了验证。该方法优于具有不同截止频率的低通滤波器和基于独立分量分析（伊卡）的肌肉伪影去除技术。此外，该方法被应用于一个真实的癫痫发作EEG记录与肌肉伪影污染。所提出的方法成功地消除了肌肉伪影，而不改变记录的潜在发作活动。
The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity.