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REVIEW 3 major objections 4 minor 107 references

Large Language Models for EEG: A Comprehensive Survey and Taxonomy

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This survey organizes EEG-LLM research into four domains: foundation models, brain-to-language decoding, cross-modal generation, and clinical tools.

desk verdict A useful orientation survey of the LLM-EEG area whose 'systematic' and quantitative claims outrun the stated methodology; the taxonomy itself is reasonable and the tables are handy. read the letter →

arxiv 2506.06353 v1 pith:7E72LEFP submitted 2025-06-02 eess.SP cs.AIcs.ETcs.HCcs.LG

Naseem Babu , Jimson Mathew , A. P. Vinod This is my paper
classification eess.SPcs.AIcs.ETcs.HCcs.LG
keywords EEGlargelanguagemodelsbrain-computerinterfacesEEG-to-languagedecodingfoundationcross-modalgenerationclinicalapplications
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to show that the growing convergence between large language models and EEG research has produced a body of work that can be organized into four domains: LLM-inspired foundation models for EEG representation learning, EEG-to-language decoding, cross-modal generation (images and 3D objects), and clinical applications plus dataset tools. It argues that transformer-based architectures, adapted through fine-tuning, few-shot, and zero-shot learning, let EEG models perform complex tasks such as generating natural language from brain signals, semantic interpretation, and diagnostic assistance. A reader should care because the field currently lacks a shared structure, and this taxonomy gives researchers a common vocabulary for placing new work.

What carries the argument

The central object is the taxonomy itself: four categories with a hierarchical breakdown of modeling strategies. The machinery underneath is the transformer/LLM architecture family, specifically masked (BERT-style) and autoregressive (GPT-style) pretraining, plus three adaptation strategies—fine-tuning (including prefix tuning and adapters), zero-shot prompting, and few-shot in-context learning—that let text-trained models ingest EEG signals and emit text, images, or diagnostic labels.

What would settle it

A systematic replication that defines a search query, inclusion criteria, and screening count, then classifies every eligible paper, would settle the claim: if a substantial fifth category emerges or the four-domain distribution in Figure 3 shifts materially, the taxonomy misrepresents the field.

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Extended reading notes

Core claim

The paper's central claim is that the convergence of LLMs and EEG is not a scattering of isolated experiments but a field that can be organized into four functional domains: foundation models that learn transferable EEG representations through masked or autoregressive pretraining; EEG-to-language decoding that generates natural language from brain signals through decoders, semantic alignment, or instruction tuning; cross-modal generation that turns brain activity into images or 3D objects; and clinical and dataset work covering emotion recognition, mental health diagnosis, motor imagery classification, reading analysis, and data tools. The survey presents this taxonomy as a foundational resource, with tables and diagrams showing which LLMs are used, how they are adapted, and which datasets support each line of work. If the map is right, future research can be positioned, compared, and extended within a common structure.

Load-bearing premise

The taxonomy's four-domain structure assumes the studies selected for review are representative and complete enough to map the field, but the paper reports no search strategy, inclusion criteria, or screening count, so a biased sample would distort the map.

Editorial extensions

If this is right

  • New EEG-LLM work can be positioned within one of four domains, giving the field a shared reference structure for comparing methods.
  • EEG-to-language decoding is the largest surveyed cluster at 31 percent of studies (Figure 3), so brain-to-text communication is the near-term application most likely to mature.
  • Fine-tuning and few-shot or zero-shot adaptation mean existing text-trained LLMs can be repurposed for EEG tasks without large labeled EEG datasets.
  • Cross-modal generation indicates that brain activity can guide image and 3D synthesis, pointing toward richer brain-computer interfaces.
  • Clinical tools such as lightweight emotion copilots and dataset harmonization frameworks suggest LLMs can support real-time feedback and data standardization, not just offline analysis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The Figure 3 distribution implies the four domains are not equally developed, with cross-modal generation at 16 percent the thinnest cluster; that area may absorb future work as diffusion and 3D generative models improve.
  • The paper's own framing suggests a testable next step: if EEG foundation models mature into standardized pretrained backbones, researchers should rely less on training task-specific EEG models from scratch, mirroring the shift that pretrained LLMs caused in NLP.
  • Because the survey reports no search strategy or inclusion criteria, a reader cannot yet tell whether the four-domain structure would survive a formal systematic review; that is the natural next test of the taxonomy.
  • An implication the authors leave implicit is that all four domains inherit EEG's noisy-signal and inter-subject variability problems, so progress in preprocessing and artifact handling likely gates progress in each category.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper presents a survey of recent work combining large language models (LLMs) with electroencephalography (EEG) analysis. The authors propose a four-domain taxonomy — LLM-inspired foundation models, EEG-to-language decoding, cross-modal generation, and clinical applications and dataset tools — and illustrate it with tables and figures summarizing selected studies, model types, tasks, and datasets. The paper also reviews adaptation strategies (fine-tuning, zero-shot, and few-shot learning) and outlines future directions. Its stated contribution is to serve as a systematic and comprehensive foundational resource for this emerging interdisciplinary field.

Significance. If the taxonomy and the quantitative distribution of studies are representative of the field, this survey provides a genuinely useful organizational framework for researchers at the intersection of LLMs and EEG. The paper's strengths include the breadth of the covered topics, the detailed tables (Tables 2 and 3) that summarize models, tasks, and datasets, and the clear diagrams of the taxonomy and adaptation workflows. As a survey, it makes no new technical claims or fitted predictions, so the standard circularity concerns for derivations do not apply. However, its value as a 'foundational resource' depends on the transparency and representativeness of the study selection, which the current manuscript does not establish.

major comments (3)
  1. [Section 3 and Figure 3] The abstract and Section 3 describe the paper as a 'systematic review' and present a quantitative distribution of studies (25%, 31%, 16%, 28%), but the manuscript never states the search strategy, the databases queried, the inclusion/exclusion criteria, or the number of papers screened. Without a stated methodology and a denominator for Figure 3, the percentages are not verifiable and the four-domain taxonomy cannot be assessed for representativeness. Please add a methodology subsection detailing the literature search, screening process, and exact study counts, and report counts in addition to percentages in Figure 3.
  2. [Section 5.1 and Table 2] Two distinct models named 'EEGPT' are discussed: EEGPT [62], which uses electrode-wise masked modeling, and EEGPT [68], an autoregressive 1.1B-parameter model. The text and Table 2 do not disambiguate these two works, which is highly confusing for readers and weakens confidence in the survey's carefulness. Please rename or explicitly distinguish the two models (e.g., as EEGPT-MAE and EEGPT-AR) consistently in the text, Table 2, Figure 8, and the reference list.
  3. [Section 3 taxonomy vs. Figure 8 and Table 2] The taxonomy's boundaries are internally inconsistent: Section 3 defines 'Cross-Modal EEG Generation' as translation to images, text, or 3D objects, while 'EEG-to-Language Decoding' is a separate category that also covers text generation. In Figure 8, AdaCT, an EEG-to-text method, is placed under Cross-Modal Generation rather than under EEG-to-Language Decoding, whereas other EEG-to-text systems appear in the latter category. The authors should provide explicit operational criteria for assigning studies to the four domains, or revise the category definitions to remove this overlap.
minor comments (4)
  1. [Section 2.2, reference [31]] The text cites Vaswani et al. (2017) as [31], but reference [31] is listed as Parmar et al., 'Image transformer'; this appears to be a citation error, since the same work is also cited as [19].
  2. [Table 3, ZuCo 1.0 row] The task description 'EET-to-text and reading analysis' contains a typo and should read 'EEG-to-text and reading analysis'.
  3. [Section 5.4.5] The sentence 'crucial tasks given the field’s challenges with data heterogeneity' is missing a period or em-dash before 'crucial' and would benefit from a grammatical fix to clarify that the tools address crucial tasks.
  4. [Table 2, reference [63]] The table lists only 'ClinicalBERT' for reference [63], but Section 5.4.5 states that Meditron-7B and BioMistral are also used in that work; please make the table consistent with the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a survey paper with no derived equations or fitted predictions, and it does not rely on self-citations or prior uniqueness claims to establish its taxonomy.

full rationale

The paper is a literature survey and taxonomy; it does not derive equations, fit parameters, or make quantitative predictions from first principles. Its central contribution is a four-domain organizing framework for LLM-EEG studies, which is constructed from the set of papers it reviews. This is the normal, non-circular operation of a survey rather than a derivation that reduces to its inputs. I checked for the enumerated circularity patterns: there are no self-defined quantities, no fitted inputs renamed as predictions, no load-bearing self-citation chains, no imported uniqueness theorems, and no ansatz smuggled in via citation. The two references to models both named 'EEGPT' (Ref. [62] and Ref. [68]) create an ambiguity and may reflect incomplete vetting, but they are not circularity. The absence of a stated search strategy, inclusion/exclusion criteria, or screened-paper count is a legitimate concern about representativeness and verifiability of the survey's coverage and Fig. 3 percentages, but it is a methodological limitation, not a circular argument. The taxonomy is a classification of the reviewed literature, and the classification does not presuppose the conclusions it presents. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey introduces no free parameters or invented entities. Its claims rest on the representativeness of the selected papers and on the chosen taxonomy, both of which are domain assumptions rather than derived results.

assumptions (2)
  • domain assumption The four-domain taxonomy fully captures the current space of LLM-EEG research.
    The survey partitions all surveyed studies into exactly four categories (Section 3) without arguing exhaustiveness or mutual exclusivity.
  • domain assumption Papers selected for review are representative of the wider LLM-EEG literature.
    No inclusion/exclusion criteria are stated, and the Fig 3 distribution depends on this unstated selection process.

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Cite this review

Pith. "Pith review of Large Language Models for EEG: A Comprehensive Survey and Taxonomy." pith.science (2026). https://pith.science/paper/7E72LEFP

@misc{pith2026250606353,
  author       = {Pith},
  title        = {Pith review of: Large Language Models for EEG: A Comprehensive Survey and Taxonomy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7E72LEFP}},
  note         = {Machine review of arXiv:2506.06353}
}
read the original abstract

The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding, brain-computer interfaces (BCIs), and affective computing. This survey offers a systematic review and structured taxonomy of recent advancements that utilize LLMs for EEG-based analysis and applications. We organize the literature into four domains: (1) LLM-inspired foundation models for EEG representation learning, (2) EEG-to-language decoding, (3) cross-modal generation including image and 3D object synthesis, and (4) clinical applications and dataset management tools. The survey highlights how transformer-based architectures adapted through fine-tuning, few-shot, and zero-shot learning have enabled EEG-based models to perform complex tasks such as natural language generation, semantic interpretation, and diagnostic assistance. By offering a structured overview of modeling strategies, system designs, and application areas, this work serves as a foundational resource for future work to bridge natural language processing and neural signal analysis through language models.

Figures

Figures reproduced from arXiv: 2506.06353 by the authors.

Figure 1
Figure 1. EEG signals are processed by a large language [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A taxonomy of large language model applications in EEG signal analysis, organized into four categories: foundation [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Workflow illustrating the fine-tuning of a large lan [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Illustration of a zero-shot approach using a pre [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Illustration of few-shot learning for emotion clas [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Summary of large language models (LLMs) used in EEG studies. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: A hierarchical taxonomy of LLM-based EEG analysis, structured according to primary application domains. Each [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

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Works this paper leans on

107 extracted references · 36 canonical work pages

  1. [62]

    Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,

    G. Wang, W. Liu, Y. He, C. Xu, L. Ma, and H. Li, “Eegpt: Pretrained transformer for universal and reliable representation of eeg signals,” in Advances in Neural Information Processing Systems, A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang, Eds., vol. 37. Curran Associates, Inc., 2024, pp. 39 249– 39 280. [Online]. Avail...

    2024

  2. [68]

    Eegpt: Unleashing the potential of eeg generalist foundation model by autoregressive pre- training,

    T. Yue, S. Xue, X. Gao, Y. Tang, L. Guo, J. Jiang, and J. Liu, “Eegpt: Unleashing the potential of eeg generalist foundation model by autoregressive pre- training,”arXiv preprint arXiv:2410.19779, 2024

    arXiv 2024

  3. [1]

    Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey,

    B. Ghojogh and A. Ghodsi, “Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey,” Dec. 2020, working paper or preprint. [Online]. Available: https://hal.science/hal-04637647

    2020

  4. [2]

    Bert: a review of applications in natural language processing and understanding,

    M. V . Koroteev, “Bert: a review of applications in natural language processing and understanding,” arXiv preprint arXiv:2103.11943, 2021

    arXiv 2021

  5. [3]

    What does BERT learn about the structure of language?

    G. Jawahar, B. Sagot, and D. Seddah, “What does BERT learn about the structure of language?” inProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, A. Korhonen, D. Traum, and L. M `arquez, Eds. Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 3651–3657. [Online]. Available: https://aclanthology...

    2019

  6. [4]

    Brain com- puter interfaces, a review,

    L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain com- puter interfaces, a review,”Sensors, vol. 12, no. 2, pp. 1211–1279, Jan 2012. 15

    2012

  7. [5]

    Brain- computer interface technology: a review of the first international meeting,

    J. Wolpaw, N. Birbaumer, W. Heetderks, D. McFar- land, P . Peckham, G. Schalk, E. Donchin, L. Qua- trano, C. Robinson, and T. Vaughan, “Brain- computer interface technology: a review of the first international meeting,”IEEE Transactions on Rehabil- itation Engineering, vol. 8, no. 2, pp. 164–173, 2000

    2000

  8. [6]

    Eeg signal analysis: a survey,

    D. P . Subha, P . K. Joseph, U. R. Acharya, and C. M. Lim, “Eeg signal analysis: a survey,”Journal of Medi- cal Systems, vol. 34, no. 2, pp. 195–212, Apr 2010

    2010

Show all 107 references
  1. [7]

    Analysis of electroencephalography (eeg) signals and its categorization–a study,

    J. S. Kumar and P . Bhuvaneswari, “Analysis of electroencephalography (eeg) signals and its categorization–a study,”Procedia Engineering, vol. 38, pp. 2525–2536, 2012, iNTERNA- TIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING. [Online]. Available: https://www.scienced...

    2012

  2. [8]

    Eeg signal analysis: a survey,

    D. P . Subha, P . K. Joseph, R. Acharya U, and C. M. Lim, “Eeg signal analysis: a survey,”Journal of medi- cal systems, vol. 34, pp. 195–212, 2010

    2010

  3. [9]

    Thought2text: Text generation from eeg signal using large language models (llms),

    A. Mishra, S. Shukla, J. Torres, J. Gwizdka, and S. Roychowdhury, “Thought2text: Text generation from eeg signal using large language models (llms),” arXiv preprint arXiv:2410.07507, 2024

    2024

  4. [10]

    Hidden states in llms improve eeg representation learning and visual decoding,

    A. Liu, H. Jing, Y. Liu, Y. Ma, and N. Zheng, “Hidden states in llms improve eeg representation learning and visual decoding,” vol. 392, pp. 2130–2137, 2024

    2024

  5. [11]

    Mind2matter: Creating 3d models from eeg signals,

    X. Deng, S. Chen, J. Zhou, and L. Li, “Mind2matter: Creating 3d models from eeg signals,”arXiv preprint arXiv:2504.11936, 2025

    2025 arXiv

  6. [12]

    Exploring large-scale language models to evaluate eeg-based multimodal data for mental health,

    Y. Hu, S. Zhang, T. Dang, H. Jia, F. D. Salim, W. Hu, and A. J. Quigley, “Exploring large-scale language models to evaluate eeg-based multimodal data for mental health,” inCompanion of the 2024 on ACM In- ternational Joint Conference on Pervasive and Ubiquitous Computing, 2024...

    2024

  7. [13]

    Eeg artifact removal—state-of-the-art and guidelines,

    J. A. Urig ¨uen and B. Garcia-Zapirain, “Eeg artifact removal—state-of-the-art and guidelines,” Journal of Neural Engineering, vol. 12, no. 3, p. 031001, apr 2015. [Online]. Available: https: //dx.doi.org/10.1088/1741-2560/12/3/031001

    2015 doi

  8. [14]

    Identification and removal of physiological artifacts from electroencephalogram signals: A review,

    M. M. N. Mannan, M. A. Kamran, and M. Y. Jeong, “Identification and removal of physiological artifacts from electroencephalogram signals: A review,”IEEE Access, vol. 6, pp. 30 630–30 652, 2018

    2018

  9. [15]

    Removal of artifacts from eeg signals: A review,

    X. Jiang, G.-B. Bian, and Z. Tian, “Removal of artifacts from eeg signals: A review,”Sensors, vol. 19, no. 5, 2019. [Online]. Available: https: //www.mdpi.com/1424-8220/19/5/987

    2019

  10. [16]

    Discrepancy between inter- and intra-subject variability in eeg-based motor imagery brain-computer interface: Evidence from multiple perspectives,

    G. Huang, Z. Zhao, S. Zhang, Z. Hu, J. Fan, M. Fu, J. Chen, Y. Xiao, J. Wang, and G. Dan, “Discrepancy between inter- and intra-subject variability in eeg-based motor imagery brain-computer interface: Evidence from multiple perspectives,”Frontiers in Neuroscience, vol. Volume ...

    2023

  11. [17]

    Intra- and inter-subject variability in eeg-based sensorimotor brain computer interface: A review,

    S. Saha and M. Baumert, “Intra- and inter-subject variability in eeg-based sensorimotor brain computer interface: A review,”Frontiers in Computational Neuroscience, vol. Volume 13 - 2019,

    2019

  12. [18]

    A survey on evaluation of large language models,

    Y. Chang, X. Wang, J. Wang, Y. Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y. Wang, W. Ye, Y. Zhang, Y. Chang, P . S. Yu, Q. Yang, and X. Xie, “A survey on evaluation of large language models,”ACM Trans. Intell. Syst. Technol., vol. 15, no. 3, Mar. 2024. [Online]. Available:...

    2024 doi

  13. [19]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” p. 6000–6010, 2017

    2017

  14. [20]

    Large lan- guage model alignment: A survey,

    T. Shen, R. Jin, Y. Huang, C. Liu, W. Dong, Z. Guo, X. Wu, Y. Liu, and D. Xiong, “Large lan- guage model alignment: A survey,”arXiv preprint arXiv:2309.15025, 2023

    2023 arXiv

  15. [21]

    Aligning large lan- guage models with human: A survey,

    Y. Wang, W. Zhong, L. Li, F. Mi, X. Zeng, W. Huang, L. Shang, X. Jiang, and Q. Liu, “Aligning large lan- guage models with human: A survey,”arXiv preprint arXiv:2307.12966, 2023

    2023 arXiv

  16. [22]

    Eegformer: A transformer–based brain activity classification method using eeg signal,

    Z. Wan, M. Li, S. Liu, J. Huang, H. Tan, and W. Duan, “Eegformer: A transformer–based brain activity classification method using eeg signal,”Frontiers in Neuroscience, vol. Volume 17 - 2023, 2023. [Online]. Available: https://www.frontiersin.org/journals/ neuroscience/articles...

    2023

  17. [23]

    Transformers for eeg-based emotion recognition: A hierarchical spatial information learning model,

    Z. Wang, Y. Wang, C. Hu, Z. Yin, and Y. Song, “Transformers for eeg-based emotion recognition: A hierarchical spatial information learning model,” IEEE Sensors Journal, vol. 22, no. 5, pp. 4359–4368, 2022

    2022

  18. [24]

    Eeg2text: Open vocabulary eeg-to-text de- coding with eeg pre-training and multi-view trans- former,

    H. Liu, D. Hajialigol, B. Antony, A. Han, and X. Wang, “Eeg2text: Open vocabulary eeg-to-text de- coding with eeg pre-training and multi-view trans- former,”arXiv preprint arXiv:2405.02165, 2024

    2024 arXiv

  19. [25]

    Brain–computer interfaces for com- munication and rehabilitation,

    U. Chaudhary, N. Birbaumer, and A. Ramos- Murguialday, “Brain–computer interfaces for com- munication and rehabilitation,”Nature Reviews Neu- rology, vol. 12, no. 9, pp. 513–525, 2016

    2016

  20. [26]

    Electroen- cephalographic motor imagery brain connectivity analysis for bci: A review,

    M. Hamedi, S.-H. Salleh, and A. M. Noor, “Electroen- cephalographic motor imagery brain connectivity analysis for bci: A review,”Neural Computation, vol. 28, no. 6, pp. 999–1041, 06 2016. [Online]. Available: https://doi.org/10.1162/NECO a 00838

    2016 doi

  21. [27]

    Performance variation in motor imagery brain–computer interface: A brief review,

    M. Ahn and S. C. Jun, “Performance variation in motor imagery brain–computer interface: A brief review,”Journal of Neuroscience Methods, vol. 243, pp. 103–110, 2015. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0165027015000400

    2015

  22. [28]

    Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives,

    F.-B. Vialatte, M. Maurice, J. Dauwels, and A. Cichocki, “Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives,”Progress in Neurobiology, vol. 90, no. 4, pp. 418–438, 2010. [Online]. Available: https://www.sciencedirect.com/science/ art...

    2010

  23. [29]

    Steady-state visual evoked potential (ssvep)-based communication: impact of harmonic frequency components,

    G. R. M ¨uller-Putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, “Steady-state visual evoked potential (ssvep)-based communication: impact of harmonic frequency components,” 16 Journal of Neural Engineering, vol. 2, no. 4, p. 123, oct 2005. [Online]. Available: https://dx.d...

    2005 doi

  24. [30]

    A survey of multimodel large language models,

    Z. Liang, Y. Xu, Y. Hong, P . Shang, Q. Wang, Q. Fu, and K. Liu, “A survey of multimodel large language models,” inProceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering, ser. CAICE ’24. New York, NY, USA: Association for C...

    2024

  25. [31]

    Image transformer,

    N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, and A. Ku, “Image transformer,” CoRR, vol. abs/1802.05751, 2018. [Online]. Available: http://arxiv.org/abs/1802.05751

    2018 arXiv

  26. [32]

    Pre-trained models: Past, present and future,

    X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, Y. Yao, A. Zhang, L. Zhang, W. Han, M. Huang, Q. Jin, Y. Lan, Y. Liu, Z. Liu, Z. Lu, X. Qiu, R. Song, J. Tang, J.-R. Wen, J. Yuan, W. X. Zhao, and J. Zhu, “Pre-trained models: Past, present and future,”AI Open, vol. 2, ...

    2021

  27. [33]

    BERT: pre-training of deep bidirectional transformers for language understanding,

    J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,”CoRR, vol. abs/1810.04805, 2018. [Online]. Available: http://arxiv.org/abs/1810.04805

    2018 arXiv

  28. [34]

    Flamingo: a visual lan- guage model for few-shot learning,

    J.-B. Alayracet al., “Flamingo: a visual lan- guage model for few-shot learning,”arXiv preprint arXiv:2204.14198, 2022

    2022 arXiv

  29. [35]

    Visual instruction tuning,

    H. Liu, C. Zhang, Z. Hu, Y. Wang, Z. Yang, J. Wang, W. Chenet al., “Visual instruction tuning,”arXiv preprint arXiv:2304.08485, 2023

    2023 arXiv

  30. [36]

    Fine- tuned language models are zero-shot learners,

    J. Wei, M. Bosma, V . Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V . Le, “Fine- tuned language models are zero-shot learners,”arXiv preprint arXiv:2109.01652, 2021

    2021 arXiv

  31. [37]

    Instruction tuning for large language models: A survey,

    S. Zhang, L. Dong, X. Li, S. Zhang, X. Sun, S. Wang, J. Li, R. Hu, T. Zhang, F. Wuet al., “Instruction tuning for large language models: A survey,”arXiv preprint arXiv:2308.10792, 2023

    2023

  32. [38]

    Large transformers are better eeg learn- ers,

    B. Wang, X. Fu, Y. Lan, L. Zhang, W. Zheng, and Y. Xiang, “Large transformers are better eeg learn- ers,”arXiv preprint arXiv:2308.11654, 2023

    2023 arXiv

  33. [39]

    Belt-2: Bootstrapping eeg-to-language repre- sentation alignment for multi-task brain decoding,

    J. Zhou, Y. Duan, F. Chang, T. Do, Y.-K. Wang, and C.- T. Lin, “Belt-2: Bootstrapping eeg-to-language repre- sentation alignment for multi-task brain decoding,” arXiv preprint arXiv:2409.00121, 2024

    2024 arXiv

  34. [40]

    A survey of zero-shot learning: Settings, methods, and applications,

    W. Wang, V . W. Zheng, H. Yu, and C. Miao, “A survey of zero-shot learning: Settings, methods, and applications,”ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, Jan. 2019. [Online]. Available: https://doi.org/10.1145/3293318

    2019 doi

  35. [41]

    A review of generalized zero-shot learning methods,

    F. Pourpanah, M. Abdar, Y. Luo, X. Zhou, R. Wang, C. P . Lim, X.-Z. Wang, and Q. M. J. Wu, “A review of generalized zero-shot learning methods,”IEEE Transactions on Pattern Analysis and Machine Intelli- gence, vol. 45, no. 4, pp. 4051–4070, 2023

    2023

  36. [42]

    Eeg- clip : Learning eeg representations from natural language descriptions,

    T. C. N’dir and R. T. Schirrmeister, “Eeg- clip : Learning eeg representations from natural language descriptions,” 2025. [Online]. Available: https://arxiv.org/abs/2503.16531

    2025 arXiv

  37. [43]

    Hypergraph multi-modal large language model: Exploiting eeg and eye-tracking modalities to evaluate heterogeneous responses for video understanding,

    M. Wu, C. Zhao, A. Su, D. Di, T. Fu, D. An, M. He, Y. Gao, M. Ma, K. Yan, and P . Wang, “Hypergraph multi-modal large language model: Exploiting eeg and eye-tracking modalities to evaluate heterogeneous responses for video understanding,” 2024. [Online]. Available: https://arx...

    2024 arXiv

  38. [44]

    Generalizing from a few examples: A survey on few-shot learning,

    Y. Wang, Q. Yao, J. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” 2020. [Online]. Available: https://arxiv.org/abs/1904.05046

    2020 arXiv

  39. [45]

    A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,

    Y. Song, T. Wang, S. K. Mondal, and J. P . Sahoo, “A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities,” 2022. [Online]. Available: https: //arxiv.org/abs/2205.06743

    2022 arXiv

  40. [46]

    Eeg emotion copilot: Optimizing lightweight llms for emotional eeg interpretation with assisted medical record generation,

    H. Chen, W. Zeng, C. Chen, L. Cai, F. Wang, Y. Shi, L. Wang, W. Zhang, Y. Li, H. Yan, W. T. Siok, and N. Wang, “Eeg emotion copilot: Optimizing lightweight llms for emotional eeg interpretation with assisted medical record generation,” 2025. [Online]. Available: https://arxiv....

    2025 arXiv

  41. [47]

    Advancing semi-supervised eeg emotion recognition through feature extraction with mixup and large language models,

    S. Yao, L. Liu, J. Lu, D. Wu, and Y. Li, “Advancing semi-supervised eeg emotion recognition through feature extraction with mixup and large language models,” in2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024, pp. 2772– 2779

    2024

  42. [48]

    Classification of non-invasive eeg signals during motor imagery tasks using a large language model,

    D.-H. Lim, M.-J. Cho, and H.-H. Kim, “Classification of non-invasive eeg signals during motor imagery tasks using a large language model,” in2024 Interna- tional Conference on Cyberworlds (CW), 2024, pp. 378– 379

    2024

  43. [49]

    Deep representation learning for open vocabulary electroencephalography-to-text decod- ing,

    H. Amrani, D. Micucci, and P . Napole- tano, “Deep representation learning for open vocabulary electroencephalography-to-text decod- ing,”IEEE Journal of Biomedical and Health Informatics, p. 1–12, 2024. [Online]. Available: http://dx.doi.org/10.1109/JBHI.2024.3416066

    2024

  44. [50]

    Dewave: Discrete eeg waves encoding for brain dynamics to text translation,

    Y. Duan, J. Zhou, Z. Wang, Y.-K. Wang, and C.- T. Lin, “Dewave: Discrete eeg waves encoding for brain dynamics to text translation,”arXiv preprint arXiv:2309.14030, 2023

    2023 arXiv

  45. [51]

    Eeg-gpt: exploring capabilities of large language models for eeg classification and interpretation,

    J. W. Kim, A. Alaa, and D. Bernardo, “Eeg-gpt: exploring capabilities of large language models for eeg classification and interpretation,”arXiv preprint arXiv:2401.18006, 2024

    2024 arXiv

  46. [52]

    Eeg-language mod- eling for pathology detection,

    S. Gijsen and K. Ritter, “Eeg-language mod- eling for pathology detection,”arXiv preprint arXiv:2409.07480, 2024

    2024 arXiv

  47. [53]

    Eegunity: Open-source tool in facil- itating unified eeg datasets toward large-scale eeg model,

    C. Qin, R. Yang, W. You, Z. Chen, L. Zhu, M. Huang, and Z. Wang, “Eegunity: Open-source tool in facil- itating unified eeg datasets toward large-scale eeg model,”IEEE Transactions on Neural Systems and Re- habilitation Engineering, vol. 33, pp. 1653–1663, 2025

    2025

  48. [54]

    Enhancing eeg-to-text decoding through transferable representations from pre-trained con- 17 trastive eeg-text masked autoencoder,

    J. Wang, Z. Song, Z. Ma, X. Qiu, M. Zhang, and Z. Zhang, “Enhancing eeg-to-text decoding through transferable representations from pre-trained con- 17 trastive eeg-text masked autoencoder,”arXiv preprint arXiv:2402.17433, 2024

    2024 arXiv

  49. [55]

    Enhancing neural decoding with large language models: A gpt-based approach,

    D. H. Lee and C. K. Chung, “Enhancing neural decoding with large language models: A gpt-based approach,” in2024 12th International Winter Confer- ence on Brain-Computer Interface (BCI), 2024, pp. 1–4

    2024

  50. [56]

    Explo- ration of llms, eeg, and behavioral data to mea- sure and support attention and sleep,

    A. Sano, J. Amores, and M. Czerwinski, “Explo- ration of llms, eeg, and behavioral data to mea- sure and support attention and sleep,”arXiv preprint arXiv:2408.07822, 2024

    2024 arXiv

  51. [57]

    Exploring the diagnostic potential of llms in schizophrenia detection through eeg analy- sis,

    M. Guerra, R. Milanese, M. Deodato, M. G. Ciobanu, and F. Fasano, “Exploring the diagnostic potential of llms in schizophrenia detection through eeg analy- sis,” in2024 IEEE International Conference on Bioinfor- matics and Biomedicine (BIBM), 2024, pp. 6812–6819

    2024

  52. [58]

    From word embedding to reading embedding us- ing large language model, eeg and eye-tracking,

    Y. Zhang, S. Yang, G. Cauwenberghs, and T.-P . Jung, “From word embedding to reading embedding us- ing large language model, eeg and eye-tracking,” in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024, pp. 1–4

    2024

  53. [59]

    Integrating llm, eeg, and eye-tracking biomarker analysis for word-level neural state classification in semantic inference reading comprehension,

    Y. Zhang, Q. Li, S. Nahata, T. Jamal, S.-k. Cheng, G. Cauwenberghs, and T.-P . Jung, “Integrating llm, eeg, and eye-tracking biomarker analysis for word-level neural state classification in semantic inference reading comprehension,”arXiv preprint arXiv:2309.15714, 2023

    2023 arXiv

  54. [60]

    Large brain model for learning generic representations with tremendous eeg data in bci,

    W.-B. Jiang, L.-M. Zhao, and B.-L. Lu, “Large brain model for learning generic representations with tremendous eeg data in bci,”arXiv preprint arXiv:2405.18765, 2024

    2024 arXiv

  55. [61]

    Large cognition model: Towards pretrained eeg foundation model,

    C.-S. Chen, Y.-J. Chen, and A. H.-W. Tsai, “Large cognition model: Towards pretrained eeg foundation model,”arXiv preprint arXiv:2502.17464, 2025

    2025 arXiv

  56. [63]

    Leveraging large language models and fuzzy clus- tering for eeg report analysis,

    C. Zhao, Z. Cook, L. Murray, J. Kesan, N. Belacel, S. Doesburg, G. Medvedev, V . Vakorin, and P . Xi, “Leveraging large language models and fuzzy clus- tering for eeg report analysis,” in2024 IEEE SEN- SORS, 2024, pp. 1–4

    2024

  57. [64]

    Neuro-gpt: Devel- oping a foundation model for eeg,

    W. Cui, W. Jeong, P . Th ¨olke, T. Medani, K. Jerbi, A. A. Joshi, and R. M. Leahy, “Neuro-gpt: Devel- oping a foundation model for eeg,”arXiv preprint arXiv:2311.03764, vol. 107, 2023

    2023 arXiv

  58. [65]

    See: Semantically aligned eeg-to-text translation,

    Y. Tao, Y. Liang, L. Wang, Y. Li, Q. Yang, and H. Zhang, “See: Semantically aligned eeg-to-text translation,” inICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025, pp. 1–5

    2025

  59. [66]

    Eegformer: Towards transferable and interpretable large-scale eeg foundation model,

    Y. Chen, K. Ren, K. Song, Y. Wang, Y. Wang, D. Li, and L. Qiu, “Eegformer: Towards transferable and interpretable large-scale eeg foundation model,” arXiv preprint arXiv:2401.10278, 2024

    2024 arXiv

  60. [67]

    EEGTrans: Transformer- driven generative models for EEG synthesis,

    J.-H. Lim and P .-C. Kuo, “EEGTrans: Transformer- driven generative models for EEG synthesis,” 2025. [Online]. Available: https://openreview.net/forum? id=ydw2l8zgUB

    2025

  61. [69]

    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 Trans- actions on Autonomous Mental Development, vol. 7, no. 3, pp. 162–175, 2015

    2015

  62. [70]

    Emotionmeter: A multimodal framework for recognizing human emotions,

    W.-L. Zheng, W. Liu, Y. Lu, B.-L. Lu, and A. Ci- chocki, “Emotionmeter: A multimodal framework for recognizing human emotions,”IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 1110–1122, 2019

    2019

  63. [71]

    ZuCo 2.0: A dataset of physiological recordings during natural reading and annotation,

    N. Hollenstein, M. Troendle, C. Zhang, and N. Langer, “ZuCo 2.0: A dataset of physiological recordings during natural reading and annotation,” inProceedings of the Twelfth Language Resources and Evaluation Conference, N. Calzolari, F. B ´echet, P . Blache, K. Choukri, C. Cieri...

    2020

  64. [72]

    Deep learning human mind for automated visual classification,

    C. Spampinato, S. Palazzo, I. Kavasidis, D. Giordano, N. Souly, and M. Shah, “Deep learning human mind for automated visual classification,” in2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4503–4511

    2017

  65. [73]

    Decoding brain represen- tations by multimodal learning of neural activity and visual features,

    S. Palazzo, C. Spampinato, I. Kavasidis, D. Giordano, J. Schmidt, and M. Shah, “Decoding brain represen- tations by multimodal learning of neural activity and visual features,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 11, pp. 3833– 3849, 2021

    2021

  66. [74]

    Analysis of eeg structural synchrony in adolescents with schizophrenic disorders,

    S. V . Borisov, A. Y. Kaplan, N. L. Gorbachevskaya, and I. A. Kozlova, “Analysis of eeg structural synchrony in adolescents with schizophrenic disorders,”Human Physiology, vol. 31, no. 3, pp. 255–261, 2005. [Online]. Available: https://doi.org/10.1007/s10747-005-0042-z

    2005 doi

  67. [75]

    Analysis of a sleep-dependent neu- ronal feedback loop: the slow-wave microcontinuity of the eeg,

    B. Kemp, A. Zwinderman, B. Tuk, H. Kamphuisen, and J. Oberye, “Analysis of a sleep-dependent neu- ronal feedback loop: the slow-wave microcontinuity of the eeg,”IEEE Transactions on Biomedical Engineer- ing, vol. 47, no. 9, pp. 1185–1194, 2000

    2000

  68. [76]

    The bci competition iii: validating alternative approaches to actual bci problems,

    B. Blankertz, K.-R. Muller, D. Krusienski, G. Schalk, J. Wolpaw, A. Schlogl, G. Pfurtscheller, J. Millan, M. Schroder, and N. Birbaumer, “The bci competition iii: validating alternative approaches to actual bci problems,”IEEE Transactions on Neural Systems and Rehabilitation E...

    2006

  69. [77]

    The temple university hospital eeg data corpus,

    I. Obeid and J. Picone, “The temple university hospital eeg data corpus,”Frontiers in Neuroscience, 18 vol. Volume 10 - 2016, 2016. [Online]. Available: https://www.frontiersin.org/journals/ neuroscience/articles/10.3389/fnins.2016.00196

    2016

  70. [78]

    Automated identification of abnormal adult eegs,

    S. L ´opez, G. Suarez, D. Jungreis, I. Obeid, and J. Picone, “Automated identification of abnormal adult eegs,” in2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2015, p. 10.1109/SPMB.2015.7405423

    2015

  71. [79]

    The nmt scalp eeg dataset: An open-source annotated dataset of healthy and pathological eeg recordings for predictive modeling,

    H. A. Khan, R. Ul Ain, A. M. Kamboh, H. T. Butt, S. Shafait, W. Alamgir, D. Stricker, and F. Shafait, “The nmt scalp eeg dataset: An open-source annotated dataset of healthy and pathological eeg recordings for predictive modeling,”Frontiers in Neuroscience, vol. Volume 15 - 20...

    2021

  72. [80]

    The temple university hospital seizure detec- tion corpus,

    V . Shah, E. von Weltin, S. Lopez, J. R. McHugh, L. Veloso, M. Golmohammadi, I. Obeid, and J. Pi- cone, “The temple university hospital seizure detec- tion corpus,”Frontiers in Neuroinformatics, vol. 12, p. 83, Nov 2018

    2018

  73. [81]

    Zuco, a simultaneous eeg and eye-tracking resource for natural sentence reading,

    N. Hollenstein, J. Rotsztejn, M. Troendle, A. Pedroni, C. Zhang, and N. Langer, “Zuco, a simultaneous eeg and eye-tracking resource for natural sentence reading,”Scientific Data, vol. 5, p. 180291, Dec 2018

    2018

  74. [82]

    Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,

    A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P . C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” Circulation, vo...

    2000

  75. [83]

    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, Oct 2017, epub 2016 Nov 10

    2017

  76. [84]

    Review of the bci competition iv,

    M. Tangermann, K.-R. M ¨uller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. J. Miller, G. Mueller-Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schl¨ogl, C. Vidaurre, S. Waldert, and B. Blankertz, “Review of the bci competition iv,”Fron...

    2012 arXiv

  77. [85]

    Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier,

    D. Steyrl, R. Scherer, J. Faller, and G. R. M ¨uller-Putz, “Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier,” Biomedical Engineering / Biomedizinische Technik, vol. 61, no. 1, pp. 77–86, 2016. [Onli...

    2016 doi

  78. [86]

    Deep learning with convolutional neural networks for eeg decoding and visualization,

    R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, F. Hutter, W. Burgard, and T. Ball, “Deep learning with convolutional neural networks for eeg decoding and visualization,” Human Brain Mapping, vol. 38, no. 11, pp. 5391–5420,

  79. [87]

    M3cv:a multi-subject, multi-session, and multi-task database for eeg- based biometrics challenge,

    G. Huang, Z. Hu, W. Chen, Z. Liang, L. Li, L. Zhang, and Z. Zhang, “M3cv:a multi-subject, multi-session, and multi-task database for eeg- based biometrics challenge,”bioRxiv, 2022. [Online]. Available: https://www.biorxiv.org/content/early/ 2022/07/03/2022.06.28.497624

    2022

  80. [88]

    Objective and subjective evaluation of online error correction during p300-based spelling,

    P . Margaux, M. Emmanuel, D. S ´ebastien, B. Olivier, and M. J ´er´emie, “Objective and subjective evaluation of online error correction during p300-based spelling,”Advances in Human- Computer Interaction, vol. 2012, no. 1, p. 578295,

    2012

  81. [89]

    Physiobank, physiotoolkit, and physionet,

    A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P . C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000. [Online]. Available: https://www.a...

    doi

  82. [90]

    Deap: A database for emotion analysis ;using physiological signals,

    S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yaz- dani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Pa- tras, “Deap: A database for emotion analysis ;using physiological signals,”IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18–31, 2012

    2012

  83. [91]

    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. [Online]. Available: https: //doi.org/10.1038/s41597-023-02650-w

    2023 doi

  84. [92]

    Com- paring recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition,

    W. Liu, J.-L. Qiu, W.-L. Zheng, and B.-L. Lu, “Com- paring recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition,”IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 2, pp. 715–729, 2022

    2022

  85. [93]

    Eeg datasets for motor imagery brain–computer interface,

    H. Cho, M. Ahn, S. Ahn, M. Kwon, and S. C. Jun, “Eeg datasets for motor imagery brain–computer interface,”GigaScience, vol. 6, no. 7, p. gix034, 05 2017. [Online]. Available: https://doi.org/10.1093/gigascience/gix034

    2017 doi

  86. [94]

    Electroencephalo- grams during mental arithmetic task performance,

    I. Zyma, S. Tukaev, I. Seleznov, K. Kiyono, A. Popov, M. Chernykh, and O. Shpenkov, “Electroencephalo- grams during mental arithmetic task performance,” Data, vol. 4, no. 1, 2019. [Online]. Available: https://www.mdpi.com/2306-5729/4/1/14

    2019

  87. [95]

    Stew: Simul- taneous task eeg workload data set,

    W. L. Lim, O. Sourina, and L. P . Wang, “Stew: Simul- taneous task eeg workload data set,”IEEE Transac- tions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 11, pp. 2106–2114, 2018

    2018

  88. [96]

    Haaglan- den medisch centrum sleep staging database (ver- sion 1.1),

    D. Alvarez-Estevez and R. Rijsman, “Haaglan- den medisch centrum sleep staging database (ver- sion 1.1),” https://doi.org/10.13026/t79q-fr32, 2022, physioNet

    2022 doi

  89. [97]

    Inferring imagined speech using eeg signals: a new approach using riemannian manifold features,

    C. H. Nguyen, G. K. Karavas, and P . Artemiadis, “Inferring imagined speech using eeg signals: a new approach using riemannian manifold features,”Journal of Neural Engineering, vol. 15, no. 1, p. 016002, dec 2017. [Online]. Available: https://dx.doi.org/10.1088/1741-2552/aa8235

    2017 doi

  90. [98]

    Generative adversarial networks: An overview,

    A. Creswell, T. White, V . Dumoulin, K. Arulku- maran, B. Sengupta, and A. A. Bharath, 19 “Generative adversarial networks: An overview,” IEEE Signal Processing Magazine, vol. 35, no. 1, p. 53–65, Jan. 2018. [Online]. Available: http://dx.doi.org/10.1109/MSP .2017.2765202

    2018

  91. [99]

    Generative adversarial networks,

    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,”

  92. [100]

    Recent progress on generative adversarial networks (gans): A survey,

    Z. Pan, W. Yu, X. Yi, A. Khan, F. Yuan, and Y. Zheng, “Recent progress on generative adversarial networks (gans): A survey,”IEEE Access, vol. 7, pp. 36 322– 36 333, 2019

    2019

  93. [101]

    Generative adversarial networks in eeg analysis: An overview,

    A. G. Habashi, A. M. Azab, S. Eldawlatly, G. M. Aly, and A. Fahmy, “Generative adversarial networks in eeg analysis: An overview,”Journal of NeuroEngineering and Rehabilitation, vol. 20, p. 40,

  94. [102]

    Eeg-gan: Generative adversarial networks for elec- troencephalograhic (eeg) brain signals,

    K. G. Hartmann, R. T. Schirrmeister, and T. Ball, “Eeg-gan: Generative adversarial networks for elec- troencephalograhic (eeg) brain signals,” 2018. [On- line]. Available: https://arxiv.org/abs/1806.01875

    2018 arXiv

  95. [2012]

    Available: https://onlinelibrary

    [Online]. Available: https://onlinelibrary. wiley.com/doi/abs/10.1155/2012/578295

    2012 doi

  96. [2014]

    Available: https://arxiv.org/abs/ 1406.2661

    [Online]. Available: https://arxiv.org/abs/ 1406.2661

    arXiv

  97. [2017]

    Available: https://onlinelibrary

    [Online]. Available: https://onlinelibrary. wiley.com/doi/abs/10.1002/hbm.23730

    doi

  98. [2020]

    Available: https://www.frontiersin

    [Online]. Available: https://www.frontiersin. org/journals/computational-neuroscience/articles/ 10.3389/fncom.2019.00087

    2019

  99. [2023]

    Available: https://doi.org/10.1186/ s12984-023-01169-w

    [Online]. Available: https://doi.org/10.1186/ s12984-023-01169-w

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.