Review of challenges associated with the EEG artifact removal methods

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 Pythonbased 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.

Automated summary

This manuscript discusses the challenges of EEG artifact removal methods and provides recommendations to address them. It also introduces Matlab and Python-based toolboxes for EEG preprocessing. Overall, the manuscript provides information on various EEG artifact removal methods, and the recommendations offered can serve as guidelines for selecting appropriate tools and methods for EEG artifact corrections.