Quantitative EEG Features and Machine Learning Classifiers for Eye-Blink Artifact Detection: A Comparative Study

Abstract Ocular artifact, namely eye-blink artifact, is an inevitable and one of the most destructive noises of EEG signals. Many solutions of detecting the eye-blink artifact were proposed. Different subsets of EEG features and Machine Learning (ML) classifiers were used for this purpose. But no comprehensive comparison of these features and ML classifiers was presented. This paper presents the comparison of twelve EEG features and five ML classifiers, commonly used in existing studies for the detection of eye-blink artifacts. An EEG dataset, containing 2958 epochs of eye-blink, non-eye-blink, and eye-blink-like (non-eye-blink) EEG activities, is used in this study. The performance of each feature and classifier has been measured using accuracy, precision, recall, and f1-score. Experimental results reveal that scalp topography…