Artifact Detection and Correction in EEG data: A Review
This is an incremental review paper summarizing existing methods to address noise issues in EEG data, which is crucial for improving signal quality in various applications.
The paper reviews recent and classical techniques for detecting and correcting artifacts in EEG data, comparing their strengths and weaknesses and proposing future directions.
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.