Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

Abstract

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.