TY - GEN
T1 - The Classification of Wink-Based EEG Signals
T2 - 8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020
AU - Kumar, Jothi Letchumy Mahendra
AU - Rashid, Mamunur
AU - Musa, Rabiu Muazu
AU - Razman, Mohd Azraai Mohd
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:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - One of the earliest methods to observe the brain dynamic is through Electroencephalogram (EEG) brain signal. It is widely known as a non-invasive, reliable, and affordable way of recording the brain activities. It has become the most wanted way of diagnosis and treatment for mental and brain neurogenerative diseases and abnormalities. It also one of the most appropriate signals in Brain-Computer Interfaces (BCI) applications. BCI frequently used by neuromuscular disorder (post-stroke) patients to aid them in activities of daily living (ADL). In this study, the adequacy of various TL models, i.e., NasNetMobile, and NasNetLarge in extracting features to classify wink-based EEG signals were investigated. The time-frequency scalogram conversion of the Right Wink, Left Wink, and No Wink based on EEG signals was carried out through Continuous Wavelet Transform (CWT) algorithm. The features that were extracted through Transfer Learning (TL) models were fed into a number of k-Nearest Neighbors (kNN) classifier models to determine the performance of various feature extraction methods to classify the winking signals. The input data are divided into training, validation, and testing datasets via a stratified ratio of 60:20:20. It was shown through this study, that the features extracted by means of NasNetLarge were more efficient compared with NasNetMobile. The Classification Accuracy (CA) of training dataset through NasNetLarge pipeline is 98% which was higher compared to NasNetMobile through the kNN model which consists of k-value of 2 and Minkowski Distance. The validation and testing CA attained through NasNetMobile and NasNetLarge models are 100%. Therefore, it could be concluded that the proposed pipeline which consists of CWT-NasNetLarge-kNN is suitable to be adopted to classify wink-based EEG signals for different BCI applications.
AB - One of the earliest methods to observe the brain dynamic is through Electroencephalogram (EEG) brain signal. It is widely known as a non-invasive, reliable, and affordable way of recording the brain activities. It has become the most wanted way of diagnosis and treatment for mental and brain neurogenerative diseases and abnormalities. It also one of the most appropriate signals in Brain-Computer Interfaces (BCI) applications. BCI frequently used by neuromuscular disorder (post-stroke) patients to aid them in activities of daily living (ADL). In this study, the adequacy of various TL models, i.e., NasNetMobile, and NasNetLarge in extracting features to classify wink-based EEG signals were investigated. The time-frequency scalogram conversion of the Right Wink, Left Wink, and No Wink based on EEG signals was carried out through Continuous Wavelet Transform (CWT) algorithm. The features that were extracted through Transfer Learning (TL) models were fed into a number of k-Nearest Neighbors (kNN) classifier models to determine the performance of various feature extraction methods to classify the winking signals. The input data are divided into training, validation, and testing datasets via a stratified ratio of 60:20:20. It was shown through this study, that the features extracted by means of NasNetLarge were more efficient compared with NasNetMobile. The Classification Accuracy (CA) of training dataset through NasNetLarge pipeline is 98% which was higher compared to NasNetMobile through the kNN model which consists of k-value of 2 and Minkowski Distance. The validation and testing CA attained through NasNetMobile and NasNetLarge models are 100%. Therefore, it could be concluded that the proposed pipeline which consists of CWT-NasNetLarge-kNN is suitable to be adopted to classify wink-based EEG signals for different BCI applications.
KW - BCI
KW - CWT
KW - EEG
KW - kNN
KW - Machine Learning
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=85113722850&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-4803-8_22
DO - 10.1007/978-981-16-4803-8_22
M3 - Conference Proceeding
AN - SCOPUS:85113722850
SN - 9789811648021
T3 - Lecture Notes in Mechanical Engineering
SP - 205
EP - 213
BT - RiTA 2020 - Proceedings of the 8th International Conference on Robot Intelligence Technology and Applications
A2 - Chew, Esyin
A2 - P. P. Abdul Majeed, Anwar
A2 - Liu, Pengcheng
A2 - Platts, Jon
A2 - Myung, Hyun
A2 - Kim, Junmo
A2 - Kim, Jong-Hwan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 December 2020 through 13 December 2020
ER -