Wang Guojing, Liu Hongyun, Wang Weidong, Kang Hongyan
School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191.
Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, 100853.
Zhongguo Yi Liao Qi Xie Za Zhi. 2024 May 30;48(3):298-305. doi: 10.12455/j.issn.1671-7104.230374.
Electroencephalogram (EEG) is a non-invasive measurement method of brain electrical activity. In recent years, single/few-channel EEG has been used more and more, but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG. In this paper, the regression and filtering methods, decomposition methods, blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed. According to the characteristics of single/few-channel EEG signals, hybrid EEG artifact removal methods for different scenarios are analyzed and summarized, mainly including single-artifact/multi-artifact scenes and online/offline scenes. In addition, the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed. Finally, the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.
脑电图（EEG）是一种用于测量大脑电活动的非侵入性方法。近年来，单通道/少通道脑电图的使用越来越广泛，但各种类型的生理伪迹严重影响了单通道/少通道脑电图的分析和广泛应用。本文综述了单通道/少通道脑电图中各种生理伪迹所涉及的回归与滤波方法、分解方法、盲源分离方法和机器学习方法。根据单通道/少通道脑电信号的特点，分析和总结了适用于不同场景的混合脑电伪迹去除方法，主要包括单伪迹/多伪迹场景和在线/离线场景。此外，还综述了在半模拟和真实脑电数据上验证算法性能的方法和指标。最后，简要描述了单通道/少通道脑电图应用和生理伪迹处理的发展趋势。
