OSCR

EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography.

Overview

Authors: Youxi Qu1, Xicheng Lou2, Hongying Meng3, Zhangyong Li1, Jianlin Wang1, Kunpeng Mao1, Xinwei Li1
ORCID iDs: Xinwei Li
  1. Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, 2 Chongwen Rd, Chongqing, 400065 China
  2. School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, 2 Chongwen Rd, Chongqing, 400065 China
  3. Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, Uxbridge, UB8 3PH UK
Journal: Brain informatics, volume 13, issue 1, article 33
Dates: received 18 May 2026; accepted 2 July 2026; published online 16 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s40708-026-00321-1 · PMID 42461518 · PMCID PMC13391462 · OpenAlex W7169103807
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), methods / tools (subfield)
Methods: Connectivity, Machine learning
Keywords: Electroencephalogram (EEG), Motor imagery (MI), Brain-computer interfaces (BCIs), Neural networks
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Key Project of Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K202400602); Chongqing Graduate Student Research Innovation Project; National Natural Science Foundation of China (62576066)
Citations: not cited yet (Europe PMC); 34 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1186/s40708-026-00321-1.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 3 funders, 16 references.

Cite

This paper

Qu, Y., Lou, X., Meng, H., Li, Z., Wang, J., Mao, K., & Li, X. (2026). EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography. Brain informatics, 13(1), 33. https://doi.org/10.1186/s40708-026-00321-1

BibTeX

@article{qu2026eeg,
author = {Qu, Youxi and Lou, Xicheng and Meng, Hongying and Li, Zhangyong and Wang, Jianlin and Mao, Kunpeng and Li, Xinwei},
title = {{EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography}},
journal = {Brain informatics},
year = {2026},
month = jul,
volume = {13},
number = {1},
pages = {33},
publisher = {Springer},
issn = {2198-4018},
doi = {10.1186/s40708-026-00321-1},
url = {https://doi.org/10.1186/s40708-026-00321-1},
pmid = {42461518},
pmcid = {PMC13391462}
}

RIS

TY - JOUR
AU - Qu, Youxi
AU - Lou, Xicheng
AU - Meng, Hongying
AU - Li, Zhangyong
AU - Wang, Jianlin
AU - Mao, Kunpeng
AU - Li, Xinwei
TI - EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography
T2 - Brain informatics
J2 - Brain Inform
PY - 2026
DA - 2026/07/16
VL - 13
IS - 1
SP - 33
SN - 2198-4018
PB - Springer
DO - 10.1186/s40708-026-00321-1
UR - https://doi.org/10.1186/s40708-026-00321-1
LA - en
ER -

CSL-JSON

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"title": "EEG-DBNet: a dual-branch framework for temporal-spectral representation learning of motor imagery electroencephalography",
"container-title": "Brain informatics",
"author": [
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"given": "Youxi"
},
{
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{
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"given": "Jianlin"
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{
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"volume": "13",
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"DOI": "10.1186/s40708-026-00321-1",
"PMID": "42461518",
"PMCID": "PMC13391462",
"ISSN": "2198-4018",
"publisher": "Springer",
"URL": "https://doi.org/10.1186/s40708-026-00321-1",
"language": "en",
"issued": {
"date-parts": [
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]
}
}

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