A Brief Summary of EEG Artifact Handling

TL;DR

This paper reviews the advancements in EEG artifact detection and mitigation techniques, highlighting their importance for improving EEG analysis in various applications like brain-computer interfaces and clinical diagnostics.

Contribution

It provides a concise overview of the main artifact types in EEG and discusses recent methods for their detection and removal.

Findings

Various artifact types identified and categorized

Recent techniques improve artifact detection accuracy

Enhanced EEG signal quality for applications

Abstract

The applications of Electroencephalogram (EEG) have been extended to out of laboratory and clinics recently due to the advancements in the technical capabilities. There are various advantageous of EEG, making it a preferable method for a wide range of applications; it is a noninvasive method, it is portable, it offers good time resolution and sufficient spatial resolution, besides there are low cost EEG systems available for a commercial use. Since the early uses of EEG, mainly as monitoring of diseases and pathologies, sleep staging and event related potential researches, it has been intertwined with undesired signal types which we call as artifacts. These pose great challenges in the practice of EEG based methods such as averaging for monitoring and diagnosis of diseases, and single-trial signal analysis for a relatively recent application in brain-computer interfaces. However, many techniques have been developed and under study for better detection and mitigation of these adverse events. The main artifact types and their handling are discussed in this brief summary.