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The Classification of EEG-based Wink Signals: A CWT-Transfer Learning Pipeline
Jothi Letchumy Mahendra Kumar,Mamunur Rashid,Rabiu Muazu Musa,Mohd Azraai Mohd Razman,Norizam Sulaiman,Rozita Jailani,Anwar P.P. Abdul Majeed 한국통신학회 2021 ICT Express Vol.7 No.4
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.