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DC Field | Value | Language |
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dc.contributor.author | Wei, M | - |
dc.contributor.author | Yang, R | - |
dc.contributor.author | Huang, M | - |
dc.contributor.author | Ni, J | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Liu, X | - |
dc.date.accessioned | 2024-02-26T12:11:50Z | - |
dc.date.available | 2024-02-26T12:11:50Z | - |
dc.date.issued | 2023-12-04 | - |
dc.identifier | ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 | - |
dc.identifier | ORCiD: Xiaohui Liu https://orcid.org/0000-0003-1589-1267 | - |
dc.identifier.citation | Wei, M. et al. (2024) 'Sub-Band Cascaded CSP-based Deep Transfer Learning for Cross-Subject Lower Limb Motor Imagery Classification', IEEE Transactions on Cognitive and Developmental Systems, 0 (early access), pp. 1 - 14. doi: 10.1109/TCDS.2023.3338460. | en_US |
dc.identifier.issn | 2379-8920 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28410 | - |
dc.description.abstract | Lower limb motor imagery (MI) classification is a challenging research topic in brain-computer interface (BCI) due to excessively close physiological representation of left and right lower limb movements in the human brain. Moreover, MI signals have severely subject-specific characteristics. The classification schemes designed for a specific subject in previous studies could not meet the requirements of cross-subject classification in a generic BCI system. Therefore, this study aimed to establish a cross-subject lower limb MI classification scheme. Three novel sub-band cascaded common spatial pattern (SBCCSP) algorithms were proposed to extract representative features with low redundancy. The validations had been conducted based on the lower limb stepping-based MI signals collected from subjects performing MI tasks in experiments. The proposed schemes with three SBCCSP algorithms have been validated with better accuracy and running time performances than other common spatial pattern (CSP) variants with the best average accuracy of 98.78%. This study provides the first investigation of a cross-subject MI classification scheme based on experimental stepping-based MI signals. The proposed scheme will make an essential contribution to developing generic BCI systems for lower limb auxiliary and rehabilitation applications. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China (61603223); Jiangsu Provincial Qinglan Project, Suzhou Science and Technology Programme (SYG202106); Research Development Fund of XJTLU (RDF-18-02-30, RDF-20-01-18); Key Program Special Fund in XJTLU (KSF-E-34) and The Natural Science Foundat on of the Jiangsu Higher Education Institutions of China (20KJB520034). | en_US |
dc.format.extent | 1 - 14 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information. | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | brain-computer interface | en_US |
dc.subject | motor imagery classification | en_US |
dc.subject | sub-band cascaded common spatial pattern | en_US |
dc.subject | cross-subject transfer learning | en_US |
dc.subject | deep transfer learning | en_US |
dc.title | Sub-Band Cascaded CSP-based Deep Transfer Learning for Cross-Subject Lower Limb Motor Imagery Classification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TCDS.2023.3338460 | - |
dc.relation.isPartOf | IEEE Transactions on Cognitive and Developmental Systems | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 2379-8939 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information. | 4.31 MB | Adobe PDF | View/Open |
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