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

Abstract Ocular artifact, namely eye-blink artifact, is an unavoidable and one of the most destructive noises in EEG signals. Many solutions were proposed regarding the detection of the eye-blink artifact. 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 significance of twelve EEG features and five ML classifiers, commonly used in existing studies, for the detection of the eye-blink artifact. 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 significance of each feature and classifier has been measured using accuracy, precision, recall, and f1-score. Experimental results reveal…