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The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features

Brain-Computer Interface (BCI) has become popular with physically challenged individuals, particularly in enhancing their activities of daily living. Electroencephalogram (EEG) signals are used to control BCI-based devices. Nonetheless, it is worth noting that the use of a multitude of features may...

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Main Authors:	Jothi Letchumy, Mahendra Kumar, Rashid, Mamunur, Musa, Rabiu Muazu, Mohd Azraai, Mohd Razman, Norizam, Sulaiman, Rozita, Jailani, Anwar, P. P. Abdul Majeed
Format:	Conference or Workshop Item
Language:	English
Published:	Springer, Singapore 2021
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/30699/1/The%20Classification%20ofWink-Based%20EEG%20Signals1.pdf http://umpir.ump.edu.my/id/eprint/30699/ https://doi.org/10.1007/978-981-15-7309-5_28 https://doi.org/10.1007/978-981-15-7309-5_28
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spelling	my.ump.umpir.306992021-02-23T04:20:55Z http://umpir.ump.edu.my/id/eprint/30699/ The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed TK Electrical engineering. Electronics Nuclear engineering Brain-Computer Interface (BCI) has become popular with physically challenged individuals, particularly in enhancing their activities of daily living. Electroencephalogram (EEG) signals are used to control BCI-based devices. Nonetheless, it is worth noting that the use of a multitude of features may impede the real-time execution of BCI devices. The present study aims at identifying significant time-domain based features that could provide a reasonable classification of the right or left wink based on EEG signals evoked by the aforesaid facial expressions. The Emotiv Insight mobile EEG system was used to capture the EEG signals acquired from the winking of the left and right eye of five healthy subjects between the age of 23 and 27 years old. Nine statistical time-domain based features were extracted, namely maximum (Max), minimum (Min), mean, median, standard deviation (SD), variance, skewness, kurtosis, and root mean square (RMS) on five channels. An ensemble learning method, i.e. Extremely Randomised Trees, was used to identify the significant features. The feature selection effect towards wink classification was evaluated via the k-Nearest Neighbours (k-NN) classifier. The training to test ratio of the extracted signals was set to 70:30. It was shown from the study, that five features were found to be significant, viz. Max_AF4, SD_AF4, skewness_AF3, kurtosis_AF4 and kurtosis_AF3, respectively. The training classification accuracy (CA) by considering all features and selected features was ascertained to be both 100%, respectively, whilst, the test CA was also found to be identical for both models with no misclassification transpired. Therefore, it could be established from the study that a comparable classification efficacy is attainable through the identification of significant features. The findings are non-trivial, particularly with respect to the implementation of the developed classifier in real-time. Springer, Singapore 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30699/1/The%20Classification%20ofWink-Based%20EEG%20Signals1.pdf Jothi Letchumy, Mahendra Kumar and Rashid, Mamunur and Musa, Rabiu Muazu and Mohd Azraai, Mohd Razman and Norizam, Sulaiman and Rozita, Jailani and Anwar, P. P. Abdul Majeed (2021) The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features. In: Advances in Mechatronics, Manufacturing, and Mechanical Engineering: Selected articles from MUCET 2019, 19-22 November 2019 , Bukit Gambang Resort City, Pahang, Malaysia. pp. 283-291., 198. ISSN 978-981-15-7309-5 https://doi.org/10.1007/978-981-15-7309-5_28 https://doi.org/10.1007/978-981-15-7309-5_28
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
description	Brain-Computer Interface (BCI) has become popular with physically challenged individuals, particularly in enhancing their activities of daily living. Electroencephalogram (EEG) signals are used to control BCI-based devices. Nonetheless, it is worth noting that the use of a multitude of features may impede the real-time execution of BCI devices. The present study aims at identifying significant time-domain based features that could provide a reasonable classification of the right or left wink based on EEG signals evoked by the aforesaid facial expressions. The Emotiv Insight mobile EEG system was used to capture the EEG signals acquired from the winking of the left and right eye of five healthy subjects between the age of 23 and 27 years old. Nine statistical time-domain based features were extracted, namely maximum (Max), minimum (Min), mean, median, standard deviation (SD), variance, skewness, kurtosis, and root mean square (RMS) on five channels. An ensemble learning method, i.e. Extremely Randomised Trees, was used to identify the significant features. The feature selection effect towards wink classification was evaluated via the k-Nearest Neighbours (k-NN) classifier. The training to test ratio of the extracted signals was set to 70:30. It was shown from the study, that five features were found to be significant, viz. Max_AF4, SD_AF4, skewness_AF3, kurtosis_AF4 and kurtosis_AF3, respectively. The training classification accuracy (CA) by considering all features and selected features was ascertained to be both 100%, respectively, whilst, the test CA was also found to be identical for both models with no misclassification transpired. Therefore, it could be established from the study that a comparable classification efficacy is attainable through the identification of significant features. The findings are non-trivial, particularly with respect to the implementation of the developed classifier in real-time.
format	Conference or Workshop Item
author	Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed
author_facet	Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed
author_sort	Jothi Letchumy, Mahendra Kumar
title	The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
title_short	The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
title_full	The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
title_fullStr	The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
title_full_unstemmed	The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
title_sort	classification of wink-based eeg signals: the identification of significant time-domain features
publisher	Springer, Singapore
publishDate	2021
url	http://umpir.ump.edu.my/id/eprint/30699/1/The%20Classification%20ofWink-Based%20EEG%20Signals1.pdf http://umpir.ump.edu.my/id/eprint/30699/ https://doi.org/10.1007/978-981-15-7309-5_28 https://doi.org/10.1007/978-981-15-7309-5_28
_version_	1692991967216009216
score	13.211869

The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features

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