TY - JOUR
T1 - The classification of EEG-based wink signals
T2 - A CWT-Transfer Learning pipeline
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:
This study is funded by Universiti Malaysia Pahang via RDU180321.
Publisher Copyright:
© 2021 The Korean Institute of Communications and Information Sciences (KICS)
PY - 2021/12
Y1 - 2021/12
N2 - Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets.
AB - Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets.
KW - BCI
KW - CWT
KW - EEG
KW - SVM
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=85100054065&partnerID=8YFLogxK
U2 - 10.1016/j.icte.2021.01.004
DO - 10.1016/j.icte.2021.01.004
M3 - Article
AN - SCOPUS:85100054065
SN - 2405-9595
VL - 7
SP - 421
EP - 425
JO - ICT Express
JF - ICT Express
IS - 4
ER -