Shallow Inception Domain Adaptation Network for EEG-Based Motor Imagery Classification

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dc.authoridHUANG, Xiuyu/0000-0003-1600-9109
dc.contributor.authorHuang, Xiuyu
dc.contributor.authorChoi, Kup-Sze
dc.contributor.authorZhou, Nan
dc.contributor.authorZhang, Yuanpeng
dc.contributor.authorChen, Badong
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:41:28Z
dc.date.available2024-05-19T14:41:28Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractElectroencephalography (EEG) data across multiple individuals have a high variance. Directly using the data to train a deep learning (DL) model usually degrades the performance. To address this issue, we propose a shallow Inception domain adaptation framework to extract informative deep features from data of multiple subjects for accurate motor imagery (MI) recognition. To our best knowledge, the Inception architecture in DL is combined with a domain adaptation (DA) scheme for the first time for the MI classification task. The approach contains two compact Inception blocks that decode temporal features in different scales. In addition, we jointly optimize a novel combined loss function to reduce both marginal and class conditional discrepancies caused by the multimodal structure of EEG signals. The DA-based loss enables Inception blocks to take full advantage of their learning abilities to capture discriminative patterns of MI data from multiple subjects instead of relying on the target user only. To demonstrate the effectiveness of our approach, we conduct substantial experiments on two well-known data sets, brain-computer interface competition IV-2a and competition IV-2b. Results show that our model achieves better performance than state-of-the-art strategies. The proposed model is able to extract informative features from high-variant EEG data collected from different individuals and achieves accurate MI classifications.en_US
dc.description.sponsorshipHong Kong Research Grants Councilen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TCDS.2023.3279262
dc.identifier.endpage533en_US
dc.identifier.issn2379-8920
dc.identifier.issn2379-8939
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85161050222en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage521en_US
dc.identifier.urihttps://doi.org10.1109/TCDS.2023.3279262
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5115
dc.identifier.volume16en_US
dc.identifier.wosWOS:001197861000017en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Cognitive and Developmental Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFeature Extractionen_US
dc.subjectElectroencephalographyen_US
dc.subjectBrain Modelingen_US
dc.subjectTrainingen_US
dc.subjectTask Analysisen_US
dc.subjectData Modelsen_US
dc.subjectDeep Learningen_US
dc.subjectDeep Neural Networken_US
dc.subjectDomain Adaptation (Da)en_US
dc.subjectInceptionen_US
dc.subjectMotor Imagery (Mi)en_US
dc.titleShallow Inception Domain Adaptation Network for EEG-Based Motor Imagery Classificationen_US
dc.typeArticleen_US

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