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

Jothi Letchumy, Mahendra Kumar and Rashid, Mamunur and Musa, Rabiu Muazu and Mohd Azraai, Mohd Razman and Norizam, Sulaiman and Rozita, Jailani and Anwar, P. P. Abdul Majeed (2021) The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline. ICT Express. pp. 1-5. ISSN 2405-9595. (In Press / Online First) (In Press / Online First)

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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.

Item Type: Article
Uncontrolled Keywords: BCI; CWT; EEG; Transfer Learning; SVM
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Manufacturing and Mechatronic Engineering Technology
Institute of Postgraduate Studies
Depositing User: Dr Anwar P. P. Abdul Majeed
Date Deposited: 23 Feb 2021 01:37
Last Modified: 23 Feb 2021 01:37
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