TY - GEN
T1 - The Identification of Significant Time-Domain Features for Wink-Based EEG Signals
AU - Cheng, Tang Jin
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 Universiti 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 - Brain-Computer Interface (BCI) is said to be a system that can measure and convert the brain activity into readable outputs. These outputs are said to be beneficial to the people who face physical challenges in carrying out their daily life as the outputs can be employed to control the BCI-based assistive device. Electroencephalography (EEG) is one of the electrophysiological monitoring techniques that record the brain’s electrical activity. Informative attributes can be extracted from the massive outputs of EEG signal and help in increasing the effectiveness of the BCI-based device. This study aims to discover the significant statistical time-domain features that can be used in the classification of the left wink, right wink and no wink utilising EEG signals. EMOTIV Insight was used as the EEG recording device to obtain the EEG signals triggered from the winking motion of the left and right wink. Six healthy subjects that ranged between 23 years old to 27 years old were involved in the wink-based EEG recordings. Nine statistical time-domain features were extracted, namely mean, median, standard deviation, variance, root-mean-square (RMS), minimum (Min), maximum (Max), skewness and kurtosis. The identification of the significant features is attained via a filter method known as information gain ratio. The ratio of training data to testing data was set to 70:30. The selected features for classification of winking is fed into various types of classifiers to observe the effect of this feature selection method on the performance of the classification, i.e. k-Nearest Neighbour (k-NN), Support Vector Machine (SVM), and Decision Tree. It was established from the present investigation that Standard Deviation, Variance and Min from channel AF4 were found to be significant. The classification accuracy (CA) for both train and test data with the filter feature selection method is observed to be comparably equal to the CA obtained from utilising all features. The findings from the study are non-trivial towards the realisation of a real-time BCI-based system.
AB - Brain-Computer Interface (BCI) is said to be a system that can measure and convert the brain activity into readable outputs. These outputs are said to be beneficial to the people who face physical challenges in carrying out their daily life as the outputs can be employed to control the BCI-based assistive device. Electroencephalography (EEG) is one of the electrophysiological monitoring techniques that record the brain’s electrical activity. Informative attributes can be extracted from the massive outputs of EEG signal and help in increasing the effectiveness of the BCI-based device. This study aims to discover the significant statistical time-domain features that can be used in the classification of the left wink, right wink and no wink utilising EEG signals. EMOTIV Insight was used as the EEG recording device to obtain the EEG signals triggered from the winking motion of the left and right wink. Six healthy subjects that ranged between 23 years old to 27 years old were involved in the wink-based EEG recordings. Nine statistical time-domain features were extracted, namely mean, median, standard deviation, variance, root-mean-square (RMS), minimum (Min), maximum (Max), skewness and kurtosis. The identification of the significant features is attained via a filter method known as information gain ratio. The ratio of training data to testing data was set to 70:30. The selected features for classification of winking is fed into various types of classifiers to observe the effect of this feature selection method on the performance of the classification, i.e. k-Nearest Neighbour (k-NN), Support Vector Machine (SVM), and Decision Tree. It was established from the present investigation that Standard Deviation, Variance and Min from channel AF4 were found to be significant. The classification accuracy (CA) for both train and test data with the filter feature selection method is observed to be comparably equal to the CA obtained from utilising all features. The findings from the study are non-trivial towards the realisation of a real-time BCI-based system.
KW - Brain-computer interface (BCI)
KW - Classification
KW - Electroencephalogram (EEG)
KW - Feature selection
KW - Machine learning
KW - Time-domain features
KW - Winking
UR - http://www.scopus.com/inward/record.url?scp=85112529024&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_87
DO - 10.1007/978-981-33-4597-3_87
M3 - Conference Proceeding
AN - SCOPUS:85112529024
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 957
EP - 965
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
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
Y2 - 6 August 2020 through 6 August 2020
ER -