Removal of Artifacts from EEG Signals: A Review

By Xiao Jiang, Guibin Bian, Zean Tian. Italian National Conference on Sensors, 2019

753 citations recorded by the source corpus.

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a413a892efc95dc20a5366938cdc8521dbb9657a

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Citations in the corpus

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “more extensive survey on muscle artefact removal from EEG can be found in [43].”
Reliable but Wrong? Automated Detection of Heart Rate Artifacts as a Source of Bias in Pediatric EEG Data (2026, unknown): “As such, artifacts can easily be misinterpreted as cognitive processes or biomarkers of pathological activity, which will bias interpretations and diagnoses [4].”
RELAX‐Jr: An Automated Pre‐Processing Pipeline for Developmental EEG Recordings (2024, unknown): “Pre-processing strategies for reducing these artefacts can vary widely, both between investigators, and across laboratories, with a growing array of EEG artefact removal algorithms available to investigators (Jiang, Bian, and Tian 2019; Roy et al. 2021).”