A Review on Machine Learning Algorithms in Handling EEG Artifacts

Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical usefulness of EEG signals. Therefore, artifact handling is one of the cornerstones in EEG signal analysis. This paper provides a review of machine learning algorithms that have been applied in EEG artifacts handling such as artifacts identification and removal. In addition, an analysis of these methods has been reported based on their performance.

Begum, ShahinaMälardalens högskola,Inbyggda system(Swepub:mdh)sbm02 (författare)
Mälardalens högskolaInbyggda system (creator_code:org_t)

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