Objective. The excellent signal-to-noise ratio (SNR) is the premise of electroencephalogram (EEG) research and applications. This study aims to use innovative method to swiftly remove the ocular artifacts (OAs) from multichannel EEG to enhance the SNR. Approach. The moment matching method which is prevalently used to removing stripe noise from hyperspectral images is adapted and improved to deduct OAs from EEG. This modified approach regards sampling points of multichannel EEG as pixels in images. It utilizes the propagation characteristics of EEG to correct the potential shift caused by OAs. Main results. By using mathematical derivation and empirical corroboration, the results suggest that the improved moment matching (IMM) is capable of reducing OAs effectively and reserving the EEG waveform information on the greatest extent compared to existing methods, such as independent component analysis (ICA) and second-order blind identification. In the frontal region heavily affected by OAs, the SNR increased by 138.1% compared with ICA, the whole SNR increased by an average of 58.7%. Moreover, low latency superiority is provided for real-time and offline processing. IMM is effective for OAs removal and it is helpful to improve SNR of multichannel EEG. Significance. IMM affords option offering preponderance for enhancement of SNR of multichannel EEG.

目标。优异的信噪比（SNR）是脑电图（EEG）研究和应用的前提。本研究旨在使用创新方法从多通道 EEG 中快速去除眼部伪影 (OA) 以提高 SNR。方法。对普遍用于从高光谱图像中去除条纹噪声的矩匹配方法进行了改进和改进，以从 EEG 中扣除 OA。这种改进的方法将多通道 EEG 的采样点视为图像中的像素。它利用 EEG 的传播特性来纠正由 OA 引起的电位偏移。主要结果. 通过数学推导和经验验证，结果表明改进的矩匹配（IMM）与现有方法（如独立分量分析（ICA）和秒-顺序盲识别。在OAs严重影响的额叶区域，信噪比比ICA提高了138.1%，整体信噪比平均提高了58.7%。此外，为实时和离线处理提供了低延迟优势。IMM 可有效去除 OA，有助于提高多通道 EEG 的 SNR。意义。IMM 提供了为增强多通道 EEG 的 SNR 提供优势的选项。