The Classification of Blinking: An Evaluation of Significant Time-Domain Features

Gavin Lim Jiann Kai, Jothi Letchumy Mahendra Kumar, Mamunur Rashid, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Norizam Sulaiman, Rozita Jailani, Anwar P. P. Abdul Majeed*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


Stroke is one of the most widespread causes of disability-adjusted life-years (DALYs). EEG-based Brain-Computer Interface (BCI) system is a potential solution for the patients to help them regain their mobility. The study aims to classify eye blinks through features extracted from time-domain EEG signals. Six features (mean, standard deviation, root mean square, skewness, kurtosis and peak-to-peak) from five channels (AF3, AF4, T7, T8 and Pz) were collected from five healthy subjects (three male and two female) aged between 22 and 24. The Chi-square (χ2) method was used to identify significant features. Six machine learning models, i.e. Support Vector Machine (SVM)), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and Artificial Neural Networks (ANN), were developed based on all the extracted features as well as the identified significant features. The training and test datasets were divided into a ratio of 70:30. It is shown that the classification accuracy of the evaluated classifiers by considering the fifteen features selected through the Chi-square is comparable to that of the selection of all features. The highest classification accuracy was demonstrated via the RF classifier for both cases. The findings suggest that even that with a reduced feature set, a reasonably high classification accuracy could be achieved, i.e., 91% on the test set. This observation further implies the viable implementation of BCI applications with a reduced computational expense.

Original languageEnglish
Title of host publicationRecent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
EditorsAhmad Fakhri Ab. Nasir, Ahmad Najmuddin Ibrahim, Ismayuzri Ishak, Nafrizuan Mat Yahya, Muhammad Aizzat Zakaria, Anwar P. P. Abdul Majeed
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages6
ISBN (Print)9789813345966
Publication statusPublished - 2022
Externally publishedYes
EventInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 - Gambang, Malaysia
Duration: 6 Aug 20206 Aug 2020

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceInnovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020


  • Blink
  • EEG
  • Feature selection
  • Machine learning


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