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

doi:10.1016/j.icte.2021.01.004

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

Research output: Contribution to journal › Article › peer-review

31 Citations (Scopus)

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 language	English
Pages (from-to)	421-425
Number of pages	5
Journal	ICT Express
Volume	7
Issue number	4
DOIs	https://doi.org/10.1016/j.icte.2021.01.004
Publication status	Published - Dec 2021
Externally published	Yes

Keywords

BCI
CWT
EEG
SVM
Transfer Learning

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10.1016/j.icte.2021.01.004

Cite this

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title = "The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline",

abstract = "Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients{\textquoteright} 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.",

keywords = "BCI, CWT, EEG, SVM, Transfer Learning",

author = "{Mahendra Kumar}, {Jothi Letchumy} and Mamunur Rashid and Musa, {Rabiu Muazu} and {Mohd Razman}, {Mohd Azraai} and Norizam Sulaiman and Rozita Jailani and {P.P. Abdul Majeed}, Anwar",

note = "Funding Information: This study is funded by Universiti Malaysia Pahang via RDU180321. Publisher Copyright: {\textcopyright} 2021 The Korean Institute of Communications and Information Sciences (KICS)",

year = "2021",

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doi = "10.1016/j.icte.2021.01.004",

language = "English",

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AU - Mahendra Kumar, Jothi Letchumy

AU - Rashid, Mamunur

AU - Musa, Rabiu Muazu

AU - Mohd Razman, Mohd Azraai

AU - Sulaiman, Norizam

AU - Jailani, Rozita

AU - P.P. Abdul Majeed, Anwar

PY - 2021/12

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AB - 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.

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KW - Transfer Learning

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

Abstract

Keywords

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