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

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

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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 researc...

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Bibliographic Details
Main Authors: Jailani, R (Author), Kumar, JLM (Author), Majeed, APPA (Author), Musa, RM (Author), Rashid, M (Author), Razman, MAM (Author), Sulaiman, N (Author)
Format: Article
Language:English
Published: 2021
Subjects:
BCI
CWT
EEG
STROKE
SVM
Transfer Learning
Online Access:View Fulltext in Publisher
Description
Summary: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. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
DOI:10.1016/j.icte.2021.01.004

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