TY - GEN
T1 - The classification of wink-based eeg signals
T2 - 11th Malaysian Technical Universities Conference on Engineering and Technology,MUCET 2019
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:
Acknowledgement. The authors would like to acknowledge Universiti Malaysia Pahang for funding this study via RDU180321.
Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2021.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - BCI
KW - Classification
KW - EEG
KW - Feature selection
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85090535115&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-7309-5_28
DO - 10.1007/978-981-15-7309-5_28
M3 - Conference Proceeding
AN - SCOPUS:85090535115
SN - 9789811573088
T3 - Lecture Notes in Mechanical Engineering
SP - 283
EP - 291
BT - Advances in Mechatronics, Manufacturing, and Mechanical Engineering - Selected articles from MUCET 2019
A2 - Zakaria, Muhammad Aizzat
A2 - Abdul Majeed, Anwar P.P.
A2 - Hassan, Mohd Hasnun Arif
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 November 2019 through 22 November 2019
ER -