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

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  •   Dang-Khoa Tran

  •   Thanh-Hai Nguyen

  •   Thanh-Nghia Nguyen

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%.


Keywords: EEG, EOG, artifact removal, peak detection, peak thresholding, eye-blink detection, Gaussian smoothing, notch filtering

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How to Cite
[1]
Tran, D.-K., Nguyen, T.-H. and Nguyen, T.-N. 2021. Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm. European Journal of Engineering and Technology Research. 6, 4 (May 2021), 6-12. DOI:https://doi.org/10.24018/ejers.2021.6.4.2438.