TY - GEN
T1 - The Classification of Blinking
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
AU - Kai, Gavin Lim Jiann
AU - Mahendra Kumar, Jothi Letchumy
AU - Rashid, Mamunur
AU - Musa, Rabiu Muazu
AU - Mohd Razman, Mohd Azraai
AU - Sulaiman, Norizam
AU - Jailani, Rozita
AU - P. P. Abdul Majeed, Anwar
N1 - Funding Information:
Acknowledgements The authors would like to acknowledge University Malaysia Pahang for funding this study via RDU180321.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Blink
KW - EEG
KW - Feature selection
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85112550113&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_91
DO - 10.1007/978-981-33-4597-3_91
M3 - Conference Proceeding
AN - SCOPUS:85112550113
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 999
EP - 1004
BT - Recent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Ibrahim, Ahmad Najmuddin
A2 - Ishak, Ismayuzri
A2 - Mat Yahya, Nafrizuan
A2 - Zakaria, Muhammad Aizzat
A2 - P. P. Abdul Majeed, Anwar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 August 2020 through 6 August 2020
ER -