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 that scalp topography is the most potential among the selected features in detecting eye-blink artifacts. The best performing classifier is Artificial Neural Network (ANN) among the five classifiers. The scalp topography and ANN classifier combination performed as the most powerful feature-classifier combination. However, it is expected that the findings of this study will help the researchers to select the appropriate feature and classifier for their eye-blink detection studies in the future.