Abstract
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.
Highlights
With the emergence of non-invasive techniques, the research of neuroscience, cognitive science and cognitive psychology has been developed by electroencephalograph (EEG), functional near-infrared spectroscopy, magnetoencephalography (EMG) and other key tools [1]
EEG is high temporal resolution and its signals are contaminated by undesired noise, which will resulting in various artifacts [6]
The cause of artifacts may arise from measurement instrument and human subjects, the prior one including faulty electrodes, line noise and high electrode impedance [7], which can be avoided by more precise recording system and strictly recording procedures, whereas physiological artifacts are more complicated to remove
Summary
With the emergence of non-invasive techniques, the research of neuroscience, cognitive science and cognitive psychology has been developed by electroencephalograph (EEG), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (EMG) and other key tools [1]. Eye movements, eye blinks, cardiac activity and muscle activity occurred in EEG signal are some major types of physiological artifacts [8] Such physiological artifacts may interfere with neural information and even be used as normal phenomena to misleadingly drive a practical application such as brain-computer interface [9]. A variety of efficient loss possibly useful neural signals To this end, a variety of efficient techniques for artifact removal, techniques for artifact removal, especially for physiological artifacts, has been proposed in the especially for physiological artifacts, has been proposed in the published literatures. To obtain a comprehensive overview of artifact removal techniques developed in different studies, EEG artifact removal techniques developed in different studies, we used Google Scholar as the main we usedengine.
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