The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline

• Main Authors: Jailani, R (Author), Kumar, JLM (Author), Majeed, APPA (Author), Musa, RM (Author), Rashid, M (Author), Razman, MAM (Author), Sulaiman, N (Author)
• Format: Article
• Language: English
• Published: 2021
• Subjects: BCI; CWT; EEG; STROKE; SVM; Transfer Learning
• Online Access: View Fulltext in Publisher

 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. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.