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*

*Corresponding author for this work

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Abstract

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.

Original languageEnglish
Pages (from-to)421-425
Number of pages5
JournalICT Express
Volume7
Issue number4
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes

Keywords

  • BCI
  • CWT
  • EEG
  • SVM
  • Transfer Learning

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Mahendra Kumar, J. L., Rashid, M., Musa, R. M., Mohd Razman, M. A., Sulaiman, N., Jailani, R., & P.P. Abdul Majeed, A. (2021). The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline. ICT Express, 7(4), 421-425. https://doi.org/10.1016/j.icte.2021.01.004