Zhang Rui, Liu Jiajun, Chen Mingming, Zhang Lipeng, Hu Yuxia
Henan Key Laboratory of Brain Science & Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Jun 25;38(3):473-482. doi: 10.7507/1001-5515.202012017.
The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
实际应用中使用的脑机接口（BCI）系统需要尽可能少的脑电图（EEG）采集通道。然而，当通道数减少到一个时，很难去除眼电图（EOG）伪迹。因此，本文提出了一种基于小波变换和总体经验模态分解的EOG伪迹去除算法。首先，对单通道EEG信号进行小波变换，然后通过总体经验模态分解对包含EOG伪迹的小波分量进行分解。接着，使用预定义的自相关系数阈值自动选择并去除主要由EOG分量组成的本征模态函数。最后重建“干净”的EEG信号。对模拟数据和实际数据的对比实验表明，本文提出的算法解决了单通道EEG信号中EOG伪迹的自动去除问题。它能够在引起较少EEG失真的同时有效去除EOG伪迹，并且算法复杂度较低。这有助于推动BCI技术走出实验室并走向商业应用。
