Noise removal methods on ambulatory EEG: A Survey

TL;DR

This survey reviews over 100 research papers on noise detection and removal in ambulatory EEG, highlighting the variability in pattern recognition techniques needed for different conditions and emphasizing the importance of effective noise filtering.

Contribution

It provides a comprehensive overview of existing noise removal methods in ambulatory EEG and discusses the challenges in pattern recognition across diverse conditions.

Findings

Pattern recognition techniques vary with EEG conditions.

Effective noise removal is crucial for accurate ambulatory EEG analysis.

Diverse datasets require tailored noise detection approaches.

Abstract

Over many decades, research is being attempted for the removal of noise in the ambulatory EEG. In this respect, an enormous number of research papers is published for identification of noise removal, It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection and removal of an noise. More than 100 research papers have been discussed to discern the techniques for detecting and removal the ambulatory EEG. Further, the literature survey shows that the pattern recognition required to detect ambulatory method, eye open and close, varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG noise data from a various condition of EEG data.