REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification

Xu, M; Jiao, J; Chen, D; Ding, Y; Chen, Q; Wu, J; Gu, P; Pan, Y; Peng, X; Xiao, N; Yang, B; Li, Q; Guo, J

REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification

Xu, M Jiao, J Chen, D Ding, Y Chen, Q Wu, J Gu, P Pan, Y Peng, X

Xiao, N Yang, B Li, Q Guo, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Journal of Biomedical and Health Informatics, 2024, PP, (99), pp. 1-13
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Xu, M
dc.contributor.author	Jiao, J
dc.contributor.author	Chen, D
dc.contributor.author	Ding, Y
dc.contributor.author	Chen, Q
dc.contributor.author	Wu, J
dc.contributor.author	Gu, P
dc.contributor.author	Pan, Y
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472
dc.contributor.author	Xiao, N
dc.contributor.author	Yang, B
dc.contributor.author	Li, Q
dc.contributor.author	Guo, J
dc.date.accessioned	2025-01-28T08:50:04Z
dc.date.available	2025-01-28T08:50:04Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Journal of Biomedical and Health Informatics, 2024, PP, (99), pp. 1-13
dc.identifier.issn	2168-2194
dc.identifier.issn	2168-2208
dc.identifier.uri	http://hdl.handle.net/10453/184480
dc.description.abstract	High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Journal of Biomedical and Health Informatics
dc.relation.isbasedon	10.1109/JBHI.2024.3498916
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification
dc.type	Journal Article
utslib.citation.volume	PP
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-28T08:50:02Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.

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http://hdl.handle.net/10453/184480