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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30701/ http://umpir.ump.edu.my/id/eprint/30701/2/The%20classification%20of%20EEG-based%20wink%20signals.pdf |
| _version_ | 1848823579165065216 |
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| author | Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed |
| author_facet | Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed |
| author_sort | Jothi Letchumy, Mahendra Kumar |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:59:22Z |
| format | Article |
| id | ump-30701 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:59:22Z |
| publishDate | 2021 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-307012021-02-23T01:37:13Z http://umpir.ump.edu.my/id/eprint/30701/ The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier 2021 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30701/2/The%20classification%20of%20EEG-based%20wink%20signals.pdf 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) https://doi.org/10.1016/j.icte.2021.01.004 https://doi.org/10.1016/j.icte.2021.01.004 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Jothi Letchumy, Mahendra Kumar Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
| title | The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
| title_full | The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
| title_fullStr | The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
| title_full_unstemmed | The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
| title_short | The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline |
| title_sort | classification of eeg-based wink signals: a cwt-transfer learning pipeline |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30701/ http://umpir.ump.edu.my/id/eprint/30701/ http://umpir.ump.edu.my/id/eprint/30701/ http://umpir.ump.edu.my/id/eprint/30701/2/The%20classification%20of%20EEG-based%20wink%20signals.pdf |