Artifacts and noise removal for electroencephalogram (EEG): A literature review

Electroencephalogram (EEG) is a signal collected from the human brain to study and analyze the brain activities. However, raw EEG may be contaminated with unwanted components such as noises and artifacts caused by power source, environment, eye blinks, heart rate and muscle movements, which are unavoidable. These unwanted components will effect the analysis of EEG and provide inaccurate information. Therefore, researchers have proposed all kind of approaches to eliminate unwanted noises and artifacts from EEG. In this paper, a literature review is carried out to study the works that have been done for noise and artifact removal from year 2010 up to the present. It is found that conventional approaches include ICA, wavelet based analysis, statistical analysis and others. However, the existing ways of artifacts removal cannot eliminate certain noise and will cause information lost by directly discard the contaminated components. From the study, it is shown that combination of conventional with other methods is popularly used, as it is able to improve the removal of artifacts. The current trend of artifacts removal makes use of machine learning to provide an automated solution with higher efficiency.