Review of challenges associated with the EEG artifact removal methods. (July 2021)

• Record Type: Journal Article
• Authors: Mumtaz, Wajid; Rasheed, Suleman; Irfan, Alina
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: EEG artifact removal method -- Independent component analysis -- Challenges of artifact removal methods -- EEG preprocessing methods -- EEG artifact correction methods; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102741
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Electroencephalography (EEG), as a non-invasive modality, enables the representation of the underlying neuronal activities as electrical signals with high temporal resolution. In general, the EEG artifact removal methods have been considered as a fundamental preliminary step during EEG analysis. However, the associated challenges of EEG artifact removal methods should be addressed carefully, to fully utilize the data. This manuscript is based on the notion that the full capacity of the EEG artifact removal methods can be achieved while addressing the associated challenges well. Because these methods could enhance the inferences deduced from the EEG data. The focus of this manuscript is to elaborate challenges (e.g., the algorithm-specific challenges and general challenges) of the EEG artifact removal methods. Considering the challenges, the manuscript has presented recommendations to address them. The manuscript also provides information on Matlab and Python-based toolboxes developed for EEG preprocessing. In addition, this manuscript provides a brief account of the EEG artifact types along with an overview of the EEG artifact removal methods. In short, this manuscript provides information on various EEG artifact removal methods and the recommendations provided serve as guidelines for the selection of suitable tools and methods for EEG artifact corrections.