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.