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AuthorNilashi, Mehrbakhsh
AuthorAbumalloh, Rabab Ali
AuthorAhmadi, Hossein
AuthorSamad, Sarminah
AuthorAlghamdi, Abdullah
AuthorAlrizq, Mesfer
AuthorAlyami, Sultan
AuthorNayer, Fatima Khan
Available date2024-01-25T11:21:57Z
Publication Date2023-04-01
Publication NameHeliyon
Identifierhttp://dx.doi.org/10.1016/j.heliyon.2023.e15258
CitationNilashi, M., Abumalloh, R. A., Ahmadi, H., Samad, S., Alghamdi, A., Alrizq, M., ... & Nayer, F. K. (2023). Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. Heliyon, 9(4).‏
ISSN24058440
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85152135095&origin=inward
URIhttp://hdl.handle.net/10576/51184
AbstractThe analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).
SponsorThis research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R4), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code NU/RG/SERC/12/12.
Languageen
PublisherElsevier Ltd
SubjectBagged trees
Electroencephalography
Eye state classification
Human cognition state
Learning vector quantization
TitleElectroencephalography (EEG) eye state classification using learning vector quantization and bagged trees
TypeArticle
Issue Number4
Volume Number9
dc.accessType Open Access


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