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  • Title: [Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition].
    Author: Zhang R, Liu J, Chen M, Zhang L, Hu Y.
    Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2021 Jun 25; 38(3):473-482. PubMed ID: 34180192.
    Abstract:
    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. 在实际应用中的脑-机接口系统要求脑电信号采集通道越少越好，然而当减少到只有一个通道时，其眼电伪迹去除比较困难。因此，本文提出一种基于小波变换和集合经验模态分解的眼电伪迹去除算法，首先将单通道脑电信号进行小波变换，选择包含眼电伪迹的小波成分进行集合经验模态分解，进一步通过设置自相关系数阈值自动去除以眼电伪迹成分为主的固有模态函数，最后重构得到“干净”的脑电信号。在仿真数据和真实数据上的对比实验表明，本文所提算法解决了单通道脑电信号中眼电伪迹的自动去除问题，能够在有效去除眼电伪迹的同时，造成较小的脑电信号失真，同时具有较低的算法复杂度，有助于推动脑-机接口技术走出实验室，走向商业化应用。.
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