Due to the weak electroencephalogram (EEG) signal during the acquisition process, the EEG is mixed with various physiological artifacts, so it is particularly susceptible to electrooculography (EOG) interference caused by eye blinking and eye movement. A method for constructing a blind deconvolution (BD) model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed to achieve EOG artifact separation. Firstly, the CEEMDAN method is used to decompose the EEG signal containing artifacts into several intrinsic mode functions (IMF). Secondly, the modal component is used as the observation signal to send the EEG signal and the EOG artifacts to form a BD model. Finally, the separation of EEG signal and EOG artifacts is realized by constructing the cost function iteratively. To verify the proposed algorithm, the standard Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT) (CHB-MIT) scalp EEG database is used for experimental verification. The correlation between the EOG artifact separation data and the original EEG data is analyzed, and the correlation coefficient is 0.82. The results confirm that this method retains most of the original EEG signal components and has a good effect on the separation of EOG artifacts.