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arxiv: 2605.00857 · v1 · submitted 2026-04-21 · 📡 eess.SP · cs.AI· cs.LG· q-bio.NC

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

Peiliang Gong , Han Zhang , Zhen Jiang , Chenyu Liu , Ziyu Jia , Xinliang Zhou , Daoqiang Zhang , Xiaoli Li This is my paper

Pith reviewed 2026-05-10 02:22 UTC · model grok-4.3

classification 📡 eess.SP cs.AIcs.LGq-bio.NC
keywords source-free domain adaptationEEG decodingfoundation modelscross-subject generalizationmotor imageryemotion recognitionSSVEPpseudo-label refinement
0
0 comments X

The pith

A foundation model and compact specialist model co-adapt to decode EEG signals from new subjects without source data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that source-free domain adaptation for EEG can be strengthened by pairing a large pretrained foundation model with a smaller specialist model that learn from each other. This dual-branch setup generates mutual pseudo-labels, filters reliable samples using the foundation model's stability, and refines labels across two stages before distilling knowledge back to the specialist. The result is better generalization across subjects in motor imagery, emotion recognition, and SSVEP tasks while keeping source data private. A sympathetic reader would care because many real-world brain-computer interfaces cannot share raw training recordings yet still need to work on new people.

Core claim

FUSED integrates an EEG foundation model and a specialist model through dual-branch co-adaptation equipped with linear and prototype views, a consensus filtering mechanism that exploits the foundation model's stability to select high-quality samples, and a two-stage pseudo-label refinement that uses cross-branch arbitration; the foundation model is then calibrated by mutual information maximization with the specialist before knowledge is distilled from foundation model to specialist.

What carries the argument

The dual-branch co-adaptation mechanism in which the foundation model and specialist model each supply pseudo-labels to the other, combined with consensus filtering and a calibrate-then-distill pipeline.

If this is right

  • Cross-subject EEG decoding becomes practical in settings where source recordings cannot be shared for privacy reasons.
  • Pseudo-label quality improves through mutual arbitration between the two model branches rather than relying on one model alone.
  • The foundation model receives boundary calibration without direct target labels, allowing its general knowledge to transfer more effectively to the specialist.
  • Performance gains appear consistently on motor imagery, emotion recognition, and SSVEP decoding tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same co-adaptation pattern could be tested on other time-series biosignals such as ECG where large foundation models are also emerging.
  • The two-stage refinement step might reduce the need for extensive hyperparameter tuning that current source-free methods often require.
  • If the consensus filter proves robust, similar stability-based selection could help other unsupervised adaptation problems outside EEG.

Load-bearing premise

The foundation model's stability can be used to reliably pick high-quality pseudo-labeled samples and avoid introducing systematic bias when no source data is available.

What would settle it

Running FUSED on a held-out EEG dataset from a new paradigm and finding that its accuracy does not exceed standard source-free methods or that its pseudo-label error rate rises sharply would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.00857 by Chenyu Liu, Daoqiang Zhang, Han Zhang, Peiliang Gong, Xiaoli Li, Xinliang Zhou, Zhen Jiang, Ziyu Jia.

Figure 1
Figure 1. Figure 1: Illustration of FUSED: Motivation, Paradigm, and Results. (a) FM provides more discriminative feature spaces and reliable confidence scores than SM. (b) Architecture of the proposed FM-guided SFDA vs. conventional single-model SFDA. (c) Performance comparison on target domain for Motor Imagery and Emotion Recognition, showing the significant gain achieved by our new paradigm. inter-subject variability, cau… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the FUSED framework. Phase 1 (Pre-Adaptation): The SM is pre-trained and the FM is fine-tuned on labeled source data using [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Compare different foundation model backbones on BCIC-IV-2a dataset [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Parameter sensitivity analysis on BCIC-IV-2a and FACED datasets. The classification accuracy (%) is reported under varying (a) margin threshold [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: t-SNE visualization of extracted features on BCIC-IV-2a. Left [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA methods rely solely on the limited internal knowledge of source-pretrained models, leading to inferior cross-domain generalization and unreliable pseudo-labels. Although EEG Foundation Models (FMs) pretrained on large-scale data exhibit strong generalizability, their potential in SFDA remains largely unexplored. To this end, we propose FUSED, a Foundation-guided Source-free EEG Decoding framework that integrates a large-scale FM with a compact Specialist Model (SM) via dual-branch co-adaptation. Specifically, we introduce a Co-adaptation mechanism equipping both branches with linear and prototype views, enabling cross-branch pseudo-label generation. Additionally, we design a Consensus Filtering Mechanism that exploits the FM's inherent stability to identify high-quality samples, along with a Two-Stage Pseudo-Label Refinement scheme to suppress error accumulation through cross-branch arbitration. Finally, we calibrate the FM's decision boundaries via mutual information maximization with the SM, followed by knowledge distillation from FM to SM, forming a principled calibrate-then-distill pipeline. To our knowledge, FUSED is the first work to leverage EEG FMs within the SFDA framework for cross-subject EEG decoding. Extensive experiments across three EEG paradigms, including motor imagery, emotion recognition, and SSVEP, demonstrate consistent state-of-the-art performance, validating the effectiveness of foundation-guided synergy for robust and privacy-preserving EEG decoding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes FUSED, a Foundation-guided Source-free EEG Decoding framework that integrates a large-scale EEG Foundation Model (FM) with a compact Specialist Model (SM) via dual-branch co-adaptation for cross-subject EEG decoding without source data. It features a co-adaptation mechanism with linear and prototype views, a Consensus Filtering Mechanism to select high-quality samples using the FM's stability, a Two-Stage Pseudo-Label Refinement to reduce error accumulation, and a calibrate-then-distill pipeline. The authors claim this is the first such integration of EEG FMs in SFDA and report consistent state-of-the-art performance on motor imagery, emotion recognition, and SSVEP tasks.

Significance. If validated, this work is significant as it pioneers the use of EEG foundation models in source-free domain adaptation, potentially improving generalization and privacy in EEG decoding applications. The dual-branch approach and consensus-based refinement provide a new paradigm for handling unreliable pseudo-labels in SFDA. The consistent SOTA across multiple paradigms, if supported by rigorous experiments, could influence future research in brain-computer interfaces.

major comments (2)
  1. The paper's central innovation relies on exploiting the FM's inherent stability for consensus filtering and high-quality sample identification. However, there is no reported ablation study that isolates whether this filtering step improves pseudo-label accuracy over the raw FM outputs on the target domain under domain shift, which is critical to address the concern that residual domain shift may inject bias into the pseudo-labels.
  2. The claims of state-of-the-art performance across three EEG paradigms are presented without reference to specific tables or figures detailing the baselines, statistical significance tests, or ablation studies on the co-adaptation components. This makes it difficult to evaluate the load-bearing contributions of the proposed mechanisms.
minor comments (1)
  1. The abstract would benefit from including the specific datasets or number of subjects used in the experiments to provide immediate context for the claimed SOTA results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper to strengthen the presentation and validation of our contributions.

read point-by-point responses

  1. Referee: The paper's central innovation relies on exploiting the FM's inherent stability for consensus filtering and high-quality sample identification. However, there is no reported ablation study that isolates whether this filtering step improves pseudo-label accuracy over the raw FM outputs on the target domain under domain shift, which is critical to address the concern that residual domain shift may inject bias into the pseudo-labels.

    Authors: We agree that an explicit ablation isolating the Consensus Filtering Mechanism's impact on pseudo-label accuracy relative to raw FM outputs under domain shift would provide stronger evidence against potential bias. While our end-to-end results demonstrate the overall benefit, this specific comparison was not included. In the revised manuscript, we will add this ablation study, reporting pseudo-label accuracy metrics with and without filtering across the evaluated paradigms. revision: yes

  2. Referee: The claims of state-of-the-art performance across three EEG paradigms are presented without reference to specific tables or figures detailing the baselines, statistical significance tests, or ablation studies on the co-adaptation components. This makes it difficult to evaluate the load-bearing contributions of the proposed mechanisms.

    Authors: We thank the referee for highlighting this clarity issue. The manuscript reports baseline comparisons, statistical significance, and component ablations in Tables 1-3, Figures 4-7, and Section 4.3. However, we will revise the text to include more explicit cross-references to these elements and expand the discussion to better highlight the contributions of the dual-branch co-adaptation and related mechanisms. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper introduces FUSED as a new framework combining an EEG foundation model with a specialist model via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement for source-free domain adaptation. The abstract and description present these as novel mechanisms supported by cross-paradigm experiments, without any equations or steps that reduce predictions to fitted inputs by construction, self-definitional loops, or load-bearing self-citations. The 'first work' claim is a novelty assertion, not a derivation that collapses to prior inputs. The method remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

Review based on abstract only; detailed assumptions and parameters cannot be audited without full text.

free parameters (1)
  • hyperparameters for co-adaptation, filtering thresholds, and distillation
    Standard in such ML frameworks but unspecified in abstract.
axioms (1)
  • domain assumption EEG foundation models possess inherent stability that can identify high-quality pseudo-labeled samples
    Directly invoked for the Consensus Filtering Mechanism.
invented entities (1)
  • FUSED dual-branch co-adaptation framework no independent evidence
    purpose: To enable foundation-guided source-free EEG decoding
    The proposed architecture itself.

pith-pipeline@v0.9.0 · 5602 in / 1243 out tokens · 36732 ms · 2026-05-10T02:22:39.194629+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

53 extracted references · 4 canonical work pages · 1 internal anchor

  1. [1]

    Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks,

    W.-L. Zheng and B.-L. Lu, “Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks,” IEEE Transactions on autonomous mental development, vol. 7, no. 3, pp. 162–175, 2015

    2015

  2. [2]

    Deep learning techniques for classification of electroencephalogram (eeg) motor imagery (mi) signals: A review,

    H. Altaheri, G. Muhammad, M. Alsulaiman, S. U. Amin, G. A. Altuwai- jri, W. Abdul, M. A. Bencherif, and M. Faisal, “Deep learning techniques for classification of electroencephalogram (eeg) motor imagery (mi) signals: A review,”Neural computing and applications, vol. 35, no. 20, pp. 14 681–14 722, 2023

    2023

  3. [3]

    Fa- cilitating applications of ssvep-bci by effective cross-subject knowledge transfer,

    H. Li, G. Xu, C. Du, Z. Li, C. Han, P. Tian, B. Li, and S. Zhang, “Fa- cilitating applications of ssvep-bci by effective cross-subject knowledge transfer,”Expert Systems with Applications, vol. 249, p. 123492, 2024

    2024

  4. [4]

    Tahag: Two-stage domain adaptation with hybrid adaptive graph learning for eeg emotion recognition,

    P. Gong, Y . Zhou, S. Huang, P. Wang, and D. Zhang, “Tahag: Two-stage domain adaptation with hybrid adaptive graph learning for eeg emotion recognition,”IEEE Transactions on Affective Computing, 2025

    2025

  5. [5]

    Toward open-world electroencephalogram decoding via deep learning: A comprehensive survey,

    X. Chen, C. Li, A. Liu, M. J. McKeown, R. Qian, and Z. J. Wang, “Toward open-world electroencephalogram decoding via deep learning: A comprehensive survey,”IEEE Signal Processing Magazine, vol. 39, no. 2, pp. 117–134, 2022

    2022

  6. [6]

    Custom domain adaptation: A new method for cross-subject, eeg-based cognitive load recognition,

    M. Jim ´enez-Guarneros and P. G ´omez-Gil, “Custom domain adaptation: A new method for cross-subject, eeg-based cognitive load recognition,” IEEE Signal Processing Letters, vol. 27, pp. 750–754, 2020

    2020

  7. [7]

    Label-based alignment multi- source domain adaptation for cross-subject eeg fatigue mental state evaluation,

    Y . Zhao, G. Dai, G. Borghini, J. Zhang, X. Li, Z. Zhang, P. Aric `o, G. Di Flumeri, F. Babiloni, and H. Zeng, “Label-based alignment multi- source domain adaptation for cross-subject eeg fatigue mental state evaluation,”Frontiers in Human Neuroscience, vol. 15, p. 706270, 2021

    2021

  8. [8]

    Ua-daan: An uncertainty-aware dynamic adversarial adaptation network for eeg-based depression recognition,

    J. Shen, L. You, Y . Ma, Z. Zhao, H. Liang, Y . Zhang, and B. Hu, “Ua-daan: An uncertainty-aware dynamic adversarial adaptation network for eeg-based depression recognition,”IEEE Transactions on Affective Computing, 2025. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 9

    2025

  9. [9]

    Privacy-preserving brain–computer interfaces: A systematic review,

    K. Xia, W. Duch, Y . Sun, K. Xu, W. Fang, H. Luo, Y . Zhang, D. Sang, X. Xu, F.-Y . Wanget al., “Privacy-preserving brain–computer interfaces: A systematic review,”IEEE Transactions on Computational Social Systems, vol. 10, no. 5, pp. 2312–2324, 2022

    2022

  10. [10]

    Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,

    J. Liang, D. Hu, and J. Feng, “Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,” inInternational conference on machine learning. PMLR, 2020, pp. 6028–6039

    2020

  11. [11]

    A comprehensive survey on source-free domain adaptation,

    J. Li, Z. Yu, Z. Du, L. Zhu, and H. T. Shen, “A comprehensive survey on source-free domain adaptation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 8, pp. 5743–5762, 2024

    2024

  12. [12]

    Source-free domain adaptation for semantic segmentation,

    Y . Liu, W. Zhang, and J. Wang, “Source-free domain adaptation for semantic segmentation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 1215–1224

    2021

  13. [13]

    Attracting and dispersing: A simple approach for source-free domain adaptation,

    S. Yang, S. Jui, J. Van De Weijeret al., “Attracting and dispersing: A simple approach for source-free domain adaptation,”Advances in Neural Information Processing Systems, vol. 35, pp. 5802–5815, 2022

    2022

  14. [14]

    Source-free domain adaptation (sfda) for privacy-preserving seizure subtype classification,

    C. Zhao, R. Peng, and D. Wu, “Source-free domain adaptation (sfda) for privacy-preserving seizure subtype classification,”IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2315–2325, 2023

    2023

  15. [15]

    Towards practical emotion recognition: An unsupervised source-free approach for EEG domain adaptation,

    M. N. Imtiaz and N. M. Khan, “Towards practical emotion recognition: An unsupervised source-free approach for EEG domain adaptation,” CoRR, vol. abs/2504.03707, 2025

    work page arXiv 2025

  16. [16]

    Source-free domain adaptation with frozen multimodal foundation model,

    S. Tang, W. Su, M. Ye, and X. Zhu, “Source-free domain adaptation with frozen multimodal foundation model,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 23 711–23 720

    2024

  17. [17]

    Proxy denoising for source-free domain adaptation,

    S. Tang, W. Su, Y . Gan, M. Ye, J. Zhang, and X. Zhu, “Proxy denoising for source-free domain adaptation,” inThe Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025

    2025

  18. [18]

    Are large brainwave foundation models capable yet ? insights from fine-tuning,

    N. Lee, K. Barmpas, Y . Panagakis, D. A. Adamos, N. A. Laskaris, and S. Zafeiriou, “Are large brainwave foundation models capable yet ? insights from fine-tuning,” inForty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13- 19, 2025, ser. Proceedings of Machine Learning Research. PMLR / OpenReview.net, 2025

    2025

  19. [19]

    2601.17883 , archivePrefix=

    D. Liu, Y . Chen, Z. Chen, Z. Cui, Y . Wen, J. An, J. Luo, and D. Wu, “Eeg foundation models: Progresses, benchmarking, and open problems,” arXiv preprint arXiv:2601.17883, 2026

    work page arXiv 2026

  20. [20]

    Are eeg foundation models worth it? comparative evaluation with traditional decoders in diverse bci tasks,

    L. Yang, Q. Sun, A. Li, and M. M. Van Hulle, “Are eeg foundation models worth it? comparative evaluation with traditional decoders in diverse bci tasks,” inThe Fourteenth International Conference on Learning Representations, 2026

    2026

  21. [21]

    Source- free domain adaptation via avatar prototype generation and adaptation,

    Z. Qiu, Y . Zhang, H. Lin, S. Niu, Y . Liu, Q. Du, and M. Tan, “Source- free domain adaptation via avatar prototype generation and adaptation,” inProceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Z.-H. Zhou, Ed. International Joint Conferences on Artificial Intelligence Organization, 8 2021, pp. 2921– 2927, ...

    2021

  22. [22]

    Vdm-da: Virtual domain modeling for source data-free domain adaptation,

    J. Tian, J. Zhang, W. Li, and D. Xu, “Vdm-da: Virtual domain modeling for source data-free domain adaptation,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 6, pp. 3749–3760, 2022

    2022

  23. [23]

    Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation,

    Y . Du, H. Yang, M. Chen, H. Luo, J. Jiang, Y . Xin, and C. Wang, “Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation,”Mach. Learn., vol. 113, no. 6, pp. 3611–3631, 2024

    2024

  24. [24]

    Domain adaptation without source data,

    Y . Kim, D. Cho, K. Han, P. Panda, and S. Hong, “Domain adaptation without source data,”IEEE Transactions on Artificial Intelligence, vol. 2, no. 6, pp. 508–518, 2021

    2021

  25. [25]

    Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation,

    Y . Ding, L. Sheng, J. Liang, A. Zheng, and R. He, “Proxymix: Proxy-based mixup training with label refinery for source-free domain adaptation,”Neural Networks, vol. 167, pp. 92–103, 2023

    2023

  26. [26]

    Source free graph unsupervised domain adaptation,

    H. Mao, L. Du, Y . Zheng, Q. Fu, Z. Li, X. Chen, S. Han, and D. Zhang, “Source free graph unsupervised domain adaptation,” inProceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024, pp. 520–528

    2024

  27. [27]

    Unsupervised multi-source domain adaptation without access to source data,

    S. M. Ahmed, D. S. Raychaudhuri, S. Paul, S. Oymak, and A. K. Roy- Chowdhury, “Unsupervised multi-source domain adaptation without access to source data,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 103–10 112

    2021

  28. [28]

    Temporal restoration and spatial rewiring for source-free multivariate time series domain adaptation,

    P. Gong, Y . Wang, M. Wu, Z. Chen, X. Li, and D. Zhang, “Temporal restoration and spatial rewiring for source-free multivariate time series domain adaptation,” inProceedings of the 31st ACM SIGKDD Confer- ence on Knowledge Discovery and Data Mining V . 2, 2025, pp. 685–696

    2025

  29. [29]

    Evidentially calibrated source-free time-series domain adaptation with temporal imputation,

    M. Ragab, P. Gong, E. Eldele, W. Zhang, M. Wu, C.-S. Foo, D. Zhang, X. Li, and Z. Chen, “Evidentially calibrated source-free time-series domain adaptation with temporal imputation,”IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  30. [30]

    Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data,

    J. Huang, D. Guan, A. Xiao, and S. Lu, “Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data,”Advances in neural information processing systems, vol. 34, pp. 3635–3649, 2021

    2021

  31. [31]

    Divide and contrast: Source-free domain adaptation via adaptive contrastive learning,

    Z. Zhang, W. Chen, H. Cheng, Z. Li, S. Li, L. Lin, and G. Li, “Divide and contrast: Source-free domain adaptation via adaptive contrastive learning,”Advances in Neural Information Processing Systems, vol. 35, pp. 5137–5149, 2022

    2022

  32. [32]

    Beyond boundaries: Leveraging vision foundation models for source-free object detection,

    H. Yao, S. Zhao, P. Li, Y . Cui, S. Lu, W. Guo, Y . Lu, Y . Xu, and H. Xiong, “Beyond boundaries: Leveraging vision foundation models for source-free object detection,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 14, 2026, pp. 11 866–11 874

    2026

  33. [33]

    Duet: Dual-perspective pseudo labeling and uncertainty-aware exploration & exploitation training for source-free domain adaptation,

    J. Y . Lee, J. H. Park, G. Lee, B. Kim, M. H. Cha, H. Nam, J. H. Jeon, H. Lee, and S. I. Cho, “Duet: Dual-perspective pseudo labeling and uncertainty-aware exploration & exploitation training for source-free domain adaptation,” inThe Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025

    2025

  34. [34]

    Real-time eeg-based emotion monitoring using stable features,

    Z. Lan, O. Sourina, L. Wang, and Y . Liu, “Real-time eeg-based emotion monitoring using stable features,”The Visual Computer, vol. 32, no. 3, pp. 347–358, 2016

    2016

  35. [35]

    Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,

    V . J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, “Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,”Journal of neural engineering, vol. 15, no. 5, p. 056013, 2018

    2018

  36. [36]

    Tsception: Capturing temporal dynamics and spatial asymmetry from eeg for emo- tion recognition,

    Y . Ding, N. Robinson, S. Zhang, Q. Zeng, and C. Guan, “Tsception: Capturing temporal dynamics and spatial asymmetry from eeg for emo- tion recognition,”IEEE Transactions on affective computing, vol. 14, no. 3, pp. 2238–2250, 2022

    2022

  37. [37]

    Eeg emotion recognition using dynamical graph convolutional neural networks,

    T. Song, W. Zheng, P. Song, and Z. Cui, “Eeg emotion recognition using dynamical graph convolutional neural networks,”IEEE Transactions on Affective Computing, vol. 11, no. 3, pp. 532–541, 2018

    2018

  38. [38]

    A deep learning based model using rnn-lstm for the detection of schizophrenia from eeg data,

    R. Supakar, P. Satvaya, and P. Chakrabarti, “A deep learning based model using rnn-lstm for the detection of schizophrenia from eeg data,” Computers in Biology and Medicine, vol. 151, p. 106225, 2022

    2022

  39. [39]

    Eeg conformer: Convolutional transformer for eeg decoding and visualization,

    Y . Song, Q. Zheng, B. Liu, and X. Gao, “Eeg conformer: Convolutional transformer for eeg decoding and visualization,”IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 710–719, 2022

    2022

  40. [40]

    Emt: A novel transformer for generalized cross-subject eeg emotion recognition,

    Y . Ding, C. Tong, S. Zhang, M. Jiang, Y . Li, K. J. Lim, and C. Guan, “Emt: A novel transformer for generalized cross-subject eeg emotion recognition,”IEEE Transactions on Neural Networks and Learning Systems, 2025

    2025

  41. [41]

    Reducing the subject variability of eeg signals with adversarial domain generalization,

    B.-Q. Ma, H. Li, W.-L. Zheng, and B.-L. Lu, “Reducing the subject variability of eeg signals with adversarial domain generalization,” in International Conference on Neural Information Processing. Springer, 2019, pp. 30–42

    2019

  42. [42]

    Contrastive learning of subject-invariant eeg representations for cross-subject emotion recog- nition,

    X. Shen, X. Liu, X. Hu, D. Zhang, and S. Song, “Contrastive learning of subject-invariant eeg representations for cross-subject emotion recog- nition,”IEEE Transactions on Affective Computing, vol. 14, no. 3, pp. 2496–2511, 2022

    2022

  43. [43]

    Brainuicl: An unsupervised individual continual learning framework for eeg applications,

    Y . Zhou, S. Zhao, J. Wang, H. Jiang, S. Li, T. Li, and G. Pan, “Brainuicl: An unsupervised individual continual learning framework for eeg applications,” inThe Thirteenth International Conference on Learning Representations, 2025

    2025

  44. [44]

    Cbramod: A criss-cross brain foundation model for EEG decoding,

    J. Wang, S. Zhao, Z. Luo, Y . Zhou, H. Jiang, S. Li, T. Li, and G. Pan, “Cbramod: A criss-cross brain foundation model for EEG decoding,” in The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net, 2025

    2025

  45. [45]

    Large brain model for learning generic representations with tremendous EEG data in BCI,

    W. Jiang, L. Zhao, and B. Lu, “Large brain model for learning generic representations with tremendous EEG data in BCI,” inThe Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024

    2024

  46. [46]

    Biot: Biosignal transformer for cross-data learning in the wild,

    C. Yang, M. Westover, and J. Sun, “Biot: Biosignal transformer for cross-data learning in the wild,”Advances in Neural Information Pro- cessing Systems, vol. 36, pp. 78 240–78 260, 2023

    2023

  47. [47]

    CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model

    J. Ma, F. Wu, Q. Lin, Y . Xing, C. Liu, Z. Jia, and M. Feng, “Codebrain: Towards decoupled interpretability and multi-scale architecture for eeg foundation model,”arXiv preprint arXiv:2506.09110, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  48. [48]

    Exploiting the intrinsic neighborhood structure for source-free domain adaptation,

    S. Yang, J. Van de Weijer, L. Herranz, S. Juiet al., “Exploiting the intrinsic neighborhood structure for source-free domain adaptation,” Advances in neural information processing systems, vol. 34, pp. 29 393– 29 405, 2021. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 10

    2021

  49. [49]

    Source- free domain adaptation with temporal imputation for time series data,

    M. Ragab, E. Eldele, M. Wu, C.-S. Foo, X. Li, and Z. Chen, “Source- free domain adaptation with temporal imputation for time series data,” inProceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, 2023, pp. 1989–1998

    2023

  50. [50]

    SF(DA)2: source-free domain adaptation through the lens of data augmentation,

    U. Hwang, J. Lee, J. Shin, and S. Yoon, “SF(DA)2: source-free domain adaptation through the lens of data augmentation,” inThe Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024

    2024

  51. [51]

    Bci competition 2008–graz data set a,

    C. Brunner, R. Leeb, G. M ¨uller-Putz, A. Schl ¨ogl, and G. Pfurtscheller, “Bci competition 2008–graz data set a,”Institute for knowledge dis- covery (laboratory of brain-computer interfaces), Graz University of Technology, vol. 16, no. 1-6, p. 1, 2008

    2008

  52. [52]

    A large finer-grained affective computing eeg dataset,

    J. Chen, X. Wang, C. Huang, X. Hu, X. Shen, and D. Zhang, “A large finer-grained affective computing eeg dataset,”Scientific Data, vol. 10, no. 1, p. 740, 2023

    2023

  53. [53]

    A benchmark dataset for ssvep- based brain–computer interfaces,

    Y . Wang, X. Chen, X. Gao, and S. Gao, “A benchmark dataset for ssvep- based brain–computer interfaces,”IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1746–1752, 2016. APPENDIX A. BCIC-IV-2a Dataset We use the well-known BCI Competition 2a dataset [51] for motor imagery classification. It contains EEG recordings f...

    work page arXiv 2016