电子测量与仪器学报

王东庆,周建华,伏云发.EWT 算法在单通道脑电信号眼电伪迹自动去除中的研究[J].电子测量与仪器学报,2023,37(2):121-129

EWT 算法在单通道脑电信号眼电伪迹自动去除中的研究

Empirical wavelet transform algorithm for automatic removal of EOGartifacts from single-channel EEG signals

DOI：

英文关键词:electroencephalogram electroencephalogram (EOG) artifact empirical wavelet transform (EWT) automatic removal

基金项目:国家自然科学基金面上项目(82172058)资助

作者	单位
王东庆	1. 昆明理工大学信息工程与自动化学院,2. 昆明理工大学脑认知与脑机智能融合创新团队
周建华	1. 昆明理工大学信息工程与自动化学院,2. 昆明理工大学脑认知与脑机智能融合创新团队
伏云发	1. 昆明理工大学信息工程与自动化学院,2. 昆明理工大学脑认知与脑机智能融合创新团队

Author	Institution
Wang Dongqing	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology
Zhou Jianhua	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology
Fu Yunfa	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology

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中文摘要:

针对单通道脑电信号眼电伪迹去除算法中存在信息丢失和计算速度慢的问题,提出了一种基于经验小波变换 (empirical wavelet transform,EWT)、小波变换(wavelet transform,WT)和近似熵的眼电伪迹去除方法。首先,采用 EWT 算法自适应分割脑电信号频谱,在分割的区间上构造合适的滤波器组提取具有紧支撑结构的经验模态分量。然后对各模态分量进行 WT 分解,计算分解后的近似熵,同时设置近似熵阈值对眼电伪迹自动识别并去除。最后采用 WT 和 EWT 的逆变换重构信号。采用公开的 Klados 数据集和 Mohit Agarwal 的 EEG-VR 数据集对算法进行实验,实验结果表明:该方法计算时间的平均值为 0. 199 5 s,Alpha 波的功率失真均值和方差分别为 0. 128 4 和 0. 151 1,Beta 波的功率失真均值和方差分别为 0. 097 7 和 0. 158 0。所提算法与 EMD-ICA、CEEMDAN-ICA 和 WT 算法相比,计算速度快,伪迹去除能力强,能够保留脑电信号有用信息更多。

英文摘要:

In response to the problems of information loss and slow computation in previous studies of single-channel EEG signal EOG artifact removal algorithms, a method for removing EOG artifacts based on empirical wavelet transform (EWT), wavelet transform (WT) and approximate entropy is proposed. Firstly, the empirical wavelet transform (EWT) is used to adaptively segment the EEG signal, and the appropriate wavelet filter banks are constructed in the segmentation interval to extract the tightly supported modal components. Then, the WT decomposition is performed for each modal component, and the approximate entropy of the decomposition is calculated, while the approximate entropy threshold is set for automatic identification and removal of EOG artifacts. Finally, the signal is reconstructed using the inverse transform of wavelet transform (WT) and empirical wavelet transform (EWT). The algorithm was tested using the publicly available Klados dataset and Mohit Agarwal’ s EEG-VR dataset, and the experimental results showed that the mean value of the computation time of the method was 0. 199 5 s, and the mean value and variance of the power distortion of the Alpha wave were 0. 128 4 and 0. 151 1, the mean value and variance of the power distortion of the Beta wave were 0. 097 7 and 0. 158 0. Compared with EMDICA, CEEMDAN-ICA and WT algorithms, the proposed algorithm has faster computation speed, better artifact removal ability, and can retain more useful information of EEG signals.

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