Artifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage

An important step in reducing the likelihood of erroneous interpretations of EEG in both clinical and non-clinical fields is recognizing, identifying, and eliminating artifacts. There are several artifact removal strategies that combine manual and automatic methods. Numerical algorithms are used in automatic artifact removal techniques for computerized EEG recordings. The most reliable method of artifact removal is the manual approach. Artifacts’ morphology and electrical characteristics can point the way to fake explanations that are intolerable for both clinical and non-clinical usage. Thus, before further interpretation, artifacts in EEG signals must be eliminated or minimized. The proposed work will be based on identifying, categorizing, and minimizing artifacts in the recorded EEG signal. In order to analyze the data and identify research gaps in this area, this paper reviews a thorough survey of methods and techniques used for artifact removal in EEG signals with their advantages and limitations.