A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding.

Overview

Authors: Zhehan Wang¹, Yuliang Ma^1,2, Yicheng Du¹, Qingshan She^1,2

ORCID iDs: Qingshan She

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.W.); (Y.D.); (Q.S.)
Zhejiang Provincial Key Laboratory of Brain–Computer Collaborative Intelligence Technology and Applications, Hangzhou 310018, China

Institutions: Hangzhou Dianzi University (China)

Journal: Brain sciences, volume 16, issue 4, article 363

Dates: received 27 February 2026; accepted 24 March 2026; published online 27 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/brainsci16040363 · PMID 42041774 · PMCID PMC13115380 · OpenAlex W7141139825

Open access: gold, a free copy (OpenAlex)

Status: dead link (how the code is found and verified)

Categories: EEG (modality)

Methods: Statistics, Spectral & time-frequency, Machine learning

Keywords: brain–computer interface, motor imagery, feature extraction, frequency band, Riemannian manifold, domain shift

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Zhejiang Provincial Natural Science Foundation (LZ26F010007); Central Government-Guided Local Science and Technology Development Fund (2025ZY01045); Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications (2025E10015); National Natural Science Foundation of China (62371172)

Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Background: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain–computer interfaces (BCIs). Methods: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network’s feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies. Results: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability. Conclusions: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

zhwangx/BARN-DA

License: none: the authors keep all their rights

State: the link is dead, verified on 30 September 2026

Evidence: found in the paper

Software Heritage: not archived

Found in: “Data Availability Statement”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 30 September 2026: the link is dead

30 September 2026: the link is dead

At the source: github.com/zhwangx/BARN-DA

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

bbci.de/competition/iii, at bbci.de; found in “Data Availability Statement”
bbci.de/competition/iv, at bbci.de; found in the references
bnci-horizon-2020.eu/database/data-sets, at bnci-horizon-2020.eu; found in “Data Availability Statement”

Data Availability Statement

The BCI Competition IV 2a, 2b, BCI Competition III 4a, and SHU datasets utilized in this study are publicly accessible. They can be downloaded from the following websites: https://bnci-horizon-2020.eu/database/data-sets (accessed on 23 March 2026); https://www.bbci.de/competition/iii/ (accessed on 23 March 2026); https://figshare.com/articles/software/shu_dataset/19228725/1 (accessed on 23 March 2026). And the code is available at https://github.com/zhwangx/BARN-DA (accessed on 23 March 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 4 funders, 52 references.

Cite

This paper

Wang, Z., Ma, Y., Du, Y., & She, Q. (2026). A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding. Brain sciences, 16(4), 363. https://doi.org/10.3390/brainsci16040363

BibTeX

@article{wang2026band, author = {Wang, Zhehan and Ma, Yuliang and Du, Yicheng and She, Qingshan}, title = {{A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding}}, journal = {Brain sciences}, year = {2026}, month = mar, volume = {16}, number = {4}, pages = {363}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {2076-3425}, doi = {10.3390/brainsci16040363}, url = {https://doi.org/10.3390/brainsci16040363}, pmid = {42041774}, pmcid = {PMC13115380} }

RIS

TY - JOUR AU - Wang, Zhehan AU - Ma, Yuliang AU - Du, Yicheng AU - She, Qingshan TI - A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding T2 - Brain sciences J2 - Brain Sci PY - 2026 DA - 2026/03/27 VL - 16 IS - 4 SP - 363 SN - 2076-3425 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/brainsci16040363 UR - https://doi.org/10.3390/brainsci16040363 LA - en ER -

CSL-JSON

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