脑电图
工件(错误)
计算机科学
人工智能
信号(编程语言)
模式识别(心理学)
大脑活动与冥想
语音识别
神经科学
心理学
程序设计语言
作者
Xiao Jiang,Gui-Bin Bian,Zean Tian
出处
期刊:Sensors
[MDPI AG]
日期:2019-02-26
卷期号:19 (5): 987-987
被引量:280
摘要
Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
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