Artifact Detection and Correction in EEG data: A Review

TL;DR

This review paper surveys recent and classical methods for detecting and correcting artifacts in EEG data, highlighting their strengths and weaknesses, and suggests future research directions.

Contribution

It provides a comprehensive comparison of EEG artifact detection and correction techniques from the last five years, guiding future developments.

Findings

Various techniques have different strengths and weaknesses.

Detection methods range from segment rejection to noise extraction.

Future directions include improved algorithms and integration.

Abstract

Electroencephalography (EEG) has countless applications across many of fields. However, EEG applications are limited by low signal-to-noise ratios. Multiple types of artifacts contribute to the noisiness of EEG, and many techniques have been proposed to detect and correct these artifacts. These techniques range from simply detecting and rejecting artifact ridden segments, to extracting the noise component from the EEG signal. In this paper we review a variety of recent and classical techniques for EEG data artifact detection and correction with a focus on the last half-decade. We compare the strengths and weaknesses of the approaches and conclude with proposed future directions for the field.