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

• 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: Article
• Language: English
• Published: Elsevier 2021
• Subjects: TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/30701/2/The%20classification%20of%20EEG-based%20wink%20signals.pdf; http://umpir.ump.edu.my/id/eprint/30701/; https://doi.org/10.1016/j.icte.2021.01.004; https://doi.org/10.1016/j.icte.2021.01.004

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.