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.
维普
万方数据