The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
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 inv...
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| Veröffentlicht in: | ICT express 2021, 7(4), , pp.421-425 |
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| container_title | ICT express |
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| creator | Mahendra Kumar, Jothi Letchumy Rashid, Mamunur Musa, Rabiu Muazu Mohd Razman, Mohd Azraai Sulaiman, Norizam Jailani, Rozita P.P. Abdul Majeed, Anwar |
| description | 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. |
| doi_str_mv | 10.1016/j.icte.2021.01.004 |
| format | Article |
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| subjects | BCI Computer Science Computer Science, Information Systems CWT EEG Science & Technology SVM Technology Telecommunications Transfer Learning 전자/정보통신공학 |
| title | The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
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