title: [Removal of artifacts from EEG signal].
abstract: As a kind of physiological signals, the electroencephalogram (EEG) represents the electrical activity of the brain. Because of its higher time-varying sensitivity, EEG is susceptible to many artifacts, such as eye-movements, blinks, cardiac signals, muscle noise. These noises in recording EEG pose a major embarrassment for EEG interpretation and disposal. A number of methods have been proposed to overcome this problem, ranging from the rejection of various artifacts to the effect estimate of removing artifacts. This paper reviews many kinds of methods for artifact rejection in the EEC recently, including regression-based methods, artifact subtraction, principal component analysis (PCA), independent component analysis (ICA) and wavelet transform. The specific assumptions of each method and its advantage/disadvantage are also summarized.
authors.affiliation: Biomedical Engineering Institute of Communication and Information Engineering School, Shanghai University, Shanghai 200072, China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Du
authors.forename: Yingjie
authors.initials: Y
authors.lastname: Li
authors.forename: Yisheng
authors.initials: Y
authors.lastname: Zhu
authors.forename: Qiushi
authors.initials: Q
authors.lastname: Ren
authors.forename: Lun
authors.initials: L
authors.lastname: Zhao
mesh_terms: D016477:Artifacts; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted