Pith. sign in

REVIEW 2 major objections 6 minor 73 references

On using AI for EEG-based BCI applications: problems, current challenges and future trends

T0 review · 2 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper argues that EEG-based brain-computer interfaces fail outside the lab because the data-generating process is causal—each new subject, device, stimulus, or environment shifts the distribution—and because EEG data is orders of…

desk verdict A useful causal roadmap for EEG-BCI generalization, but the data-scarcity numbers are internally inconsistent as written. read the letter →

arxiv 2506.16168 v1 pith:NN6HINVU submitted 2025-06-19 cs.HC cs.AI

Thomas Barbera , Jacopo Burger , Alessandro D'Amelio , Simone Zini , Simone Bianco , Raffaella Lanzarotti , Paolo Napoletano , Giuseppe Boccignone
show 1 more author
Jose Luis Contreras-Vidal
This is my paper · ORCID
classification cs.HCcs.AI
keywords EEGbrain-computerinterfacedistributionshiftcausalitydatascarcityfoundationmodelslargebraingeneralization
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 authors set out to explain why AI models for EEG-based brain-computer interfaces (BCIs) rarely survive contact with the real world. Their central thesis is that EEG data is generated by a causal process, so every change in stimulus, subject engagement, recording device, or environment shifts the data distribution and breaks the i.i.d. assumption most models are trained under. They give the field a map of BCI paradigms organized by whether the task is exogenous or endogenous and voluntary or involuntary, and they attach a specific distribution shift to each family. They also quantify a second challenge: counted as tokens, all public EEG datasets for six BCI tasks sum to between roughly 81 million and 406 million tokens per task, against 15 trillion tokens used to train a modern large language model. If this diagnosis is right, the route to deployable BCIs runs through collecting interventional data—varying one causal factor at a time—and through models that treat causality as part of the learning problem.

What carries the argument

The load-bearing object is the causal graph in Figure 1, which connects the unobserved brain activity $Z$, the measured EEG $X$, the stimulus $S$, the subject $P$, the device $D$, the engagement $I$, the environment $E$, and the decoded condition $Y$, with arrows colored by paradigm. It makes the distribution-shift argument legible by showing that a shift is not noise but an intervention: change the device, and the path from $Z$ to $X$ changes; change the stimulus, and the path from $S$ to $Z$ changes. The second piece of machinery is the token-count estimate $N_{\text{tokens}}(i) = (T_i \times C_i)/L$, with $L = 1$ second, which converts total recorded hours and average channel count into a number comparable to the token counts used in language modeling and exposes the data-scarcity claim.

What would settle it

A concrete test: take a state-of-the-art EEG foundation model pre-trained only on currently available public datasets and evaluate it on a held-out intervention—a new EEG device, a new subject population, and a new physical environment—with no fine-tuning. If it generalizes to within a small margin of same-distribution performance, the claim that data scarcity and causal distribution shifts are the binding constraints on real-world BCIs is falsified. A weaker falsifier would be to recompute the token counts using alternative EEG tokenizations, such as multi-channel patches, waveform tokens, or windows longer than one second, and show the gap to LLM corpora shrinks to within an order of magnitude.

Watch

Extended reading notes

Core claim

The paper's core claim is that the generalization problem in EEG-based BCIs is a data-generation problem, not just a modeling problem. It represents the generation of an EEG trace $X$ as the output of a causal graph whose nodes include the unobserved brain activity $Z$, the stimulus $S$, the subject $P$, the device $D$, the level of engagement $I$, and the environment $E$; the target variable $Y$ is the condition or message being decoded. Each of the four paradigm families—exogenous and voluntary, exogenous and involuntary, endogenous and voluntary, endogenous and involuntary—realizes a different causal chain, and any intervention on a node changes the distribution of the observed signal. The authors support this with a quantitative data census: using a token defined as one second of one channel, the public datasets for Brain-to-Image, Brain-to-Text, Emotion Recognition, Motor Imagery, Neurodegenerative Disease, and Seizure Prediction contain on the order of $10^8$ tokens, while LLaMA 3.1 405B used $10^{12}$ tokens. The conclusion argued is that current self-supervised foundation models for EEG are trained on too little, too narrow data, and that the field should shift toward interventional data collection and causality-aware architectures.

Load-bearing premise

The quantitative data-scarcity headline rests on the assumption that one second of one EEG channel is a token comparable to the word and subword tokens used to train large language models; if EEG models instead tokenize multi-channel patches or longer windows, the computed gap could shrink or grow, although the qualitative distribution-shift argument stands on its own.

Editorial extensions

If this is right

  • If the causal diagnosis is right, strong results on a single EEG dataset should not be read as evidence of real-world readiness; they mostly reflect fitting one fixed distribution.
  • The field's benchmarks should shift toward leave-one-intervention-out testing, where a model is evaluated after training on all conditions except one, such as a new device, subject group, or environment.
  • Data augmentation and self-supervised pre-training can enrich the training distribution but cannot replace the diversity of real interventions, so they are complements to, not substitutes for, interventional data collection.
  • Because EEG token budgets are around $10^6$ to $10^8$ rather than $10^{12}$, scaling current EEG foundation models alone will not follow the language-model trajectory; progress depends on data-efficient and causality-aware learning.
  • Real-world BCI applications, such as assistive communication, wheelchair control, and drowsiness detection, will require systems that either are invariant to acquisition and subject shifts or can adapt with minimal calibration data.

Reading between the lines

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

  • A direct extension is that the token-scarcity numbers likely understate the usable information: EEG channels are highly correlated, so raw sample tokens over-count redundant data, and the effective unique information per second is even smaller than the already-small token totals.
  • The causal-graph view predicts that domain-invariant representation learning, meaning features that remain unchanged across devices and subjects, is a more direct target than brute-force scaling, and it could be tested with synthetic data before collecting expensive interventional EEG.
  • A testable extension for the community is to build a standardized intervention benchmark, for example a common set of subjects recorded with three electrode types in both lab and home settings, and to report leave-one-setting-out accuracy as a routine metric.
  • If the authors are right that reliability is condition-specific, then the ethical discussion should include a duty to state the operating envelope of a BCI; a system validated in a lab is not equally reliable when used in an unvalidated setting.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. This review/position paper argues that EEG-based BCI systems fail to generalize in real-world settings because the data-generating process is causal: stimulus, subject engagement, device, environment, and neuroplasticity jointly induce distribution shifts that violate the i.i.d. assumption underlying standard deep learning. The authors organize BCI paradigms along two axes (exogenous/endogenous and voluntary/involuntary), present a causal graph, and identify two grand challenges: distribution shifts and data scarcity. They formalize data scarcity by estimating the number of EEG tokens per task, compare these estimates with LLaMA 3.1's 15-trillion-token training corpus, and argue that datasets must include interventional data to support robust benchmarking. The paper closes with technological, methodological, and ethical recommendations, including spiking neural networks, continuous-time reasoning, and causality-aware large brain models.

Significance. The causal framing is a useful and relatively novel organizing principle for a scattered literature, and the proposed two-axis taxonomy gives practitioners a clear way to anticipate which distribution shifts matter for a given BCI paradigm. The explicit token-count comparison, although currently flawed in presentation, is a valuable attempt to make the data-scarcity argument concrete and falsifiable. The call for leave-one-intervention-out benchmarking is actionable and aligns with recent trends in causally motivated evaluation. The authors also share dataset metadata publicly via a GitHub repository, which supports reproducibility. If the quantitative inconsistency is fixed and the token-equivalence assumption is justified, the paper could serve as a useful roadmap for the field.

major comments (2)
  1. [Section 4.1, Table 1 and following paragraph] The table reports 81M, 108M, 406M, 179M, 161M, and 193M tokens for the six tasks, i.e., values in the 10^7 to 10^8 range, yet the text immediately after the table states that LBMs are trained on datasets containing 'at most millions of tokens (10^6)' if all data across tasks are considered. This is internally inconsistent by two orders of magnitude. Because the comparison with LLaMA 3.1's 15-trillion-token corpus is the headline evidence for the second grand challenge, the statement must be corrected or the table recomputed before publication. If the intended claim is 'hundreds of millions,' the gap to 15 trillion is still large, but the current wording is factually wrong.
  2. [Section 4.1, Eq. for N_tokens] The token definition N_tokens = (T_i * C_i) / L with L = 1 s assumes that one second of one EEG channel is directly comparable to an NLP subword token. This assumption is not justified in the text; current EEG foundation models such as LaBraM use multi-channel time windows and continuous or patch-based representations rather than per-channel-per-second tokenization. Depending on the tokenizer, the estimated token counts could change by more than an order of magnitude. The authors should either justify the comparability with specific references to how LBMs tokenize EEG, or reframe the comparison as an order-of-magnitude illustration rather than a precise accounting. The qualitative distribution-shift argument does not depend on this assumption, but the quantitative data-scarcity claim does.
minor comments (6)
  1. [Section 2.1.3 and Section 2.1.4] The acronym ERP is used both for Event-Related Potentials (Section 2.1.3) and Error-Related Potentials (Section 2.1.4), which is confusing; please disambiguate, for example by writing 'ErrP' for the error-related potential.
  2. [Section 4.1] The exponents in '10^6' and '1012' are missing superscript formatting; '15 trillion (1012) tokens' should read '10^12'.
  3. [Section 2] The phrase 'V oluntarily engaged' contains an erroneous space and should read 'Voluntarily engaged'.
  4. [Section 4.1 and GitHub link] The GitHub links are rendered as '/githubhttps://github.com/...' and '1/github...', which appears to be a typesetting artifact; these should be cleaned up in the final version.
  5. [Figure 2a] The caption describes inner and outer circles for recording hours per task and per dataset, but the figure as rendered is hard to interpret; please add a clearer legend or report the underlying totals in the text or table.
  6. [Section 5.2.1] The sentence introducing Adaptive Resonance Theory as a biologically inspired learning approach lacks a citation specific to its use in EEG or BCI contexts; the Grossberg reference alone does not establish that connection.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the data-scarcity estimate is self-contained and the causal framework is grounded in external references; the Table 1 inconsistency is a correctness issue, not circularity.

full rationale

This is a review and position paper rather than a derivation. The central claims are (i) that EEG-based BCI generalization is hampered by distribution shifts induced by a causal data-generating process, and (ii) that available EEG data are scarce relative to LLM training corpora. Claim (i) is explicitly attributed to external work (Barmpas et al., 2024; Schölkopf et al., 2021) and is not justified by a self-citation chain. Claim (ii) rests on a transparent formula, N_tokens(i) = (T_i * C_i) / L, with L = 1 s cited to Jiang et al., 2024; the token counts in Table 1 follow directly from the collected metadata. No fitted parameter is renamed as a prediction, no equation is defined in terms of its own output, and no uniqueness theorem is imported from the authors' prior work. The paper does contain an internal inconsistency: Table 1 lists 81M to 406M tokens per task while the text states that LBMs are trained on 'at most millions of tokens (10^6)'. That is a factual/arithmetic error affecting the quantitative comparison, but it is not circular reasoning. The self-citations present (Craik et al., 2019; Pacheco-Ramírez et al., 2024; Theofanopoulou et al., 2024) are used for background and context, not as load-bearing evidence for the paper's central conclusions. The paper even honestly notes the lack of strong empirical evidence for LBM generalization claims. No circular step is exhibited, so the circularity score is 0.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper is a perspective/review, so it introduces no new empirical entities. Its quantitative claim rests on a hand-chosen token duration, on the completeness and representativeness of the dataset metadata, and on the causal graph adopted from prior work.

free parameters (1)
  • EEG token duration L = 1 second
    The paper sets L = 1 s in the formula N_tokens = (T_i * C_i) / L, following LaBraM (Jiang et al., 2024). The resulting token counts and the comparison to LLM training tokens depend directly on this hand-chosen value.
assumptions (4)
  • domain assumption The causal graph in Figure 1 correctly represents the EEG data generating process, with variables E, P, I, Y, Z, S, D, X and their directed edges.
    Invoked in Section 2; the entire distribution-shift taxonomy follows from this graph, which is adopted from Barmpas et al. 2024 and Scholkopf et al. 2021.
  • ad hoc to paper One second of one EEG channel is a valid token unit comparable to an NLP token for large model pretraining.
    Used in Section 4.1 via the formula N_tokens = (T_i * C_i) / L. No evidence is provided that channel-second tokens behave like language tokens or that the comparison to LLM token counts is meaningful.
  • domain assumption The six selected tasks (B2I, B2T, ER, MI, ND, SP) and the listed public datasets are representative of the entire EEG-BCI field for the data-volume comparison.
    Section 4.1 presents Figure 2 and Table 1 as covering datasets from 2004 to the present, but no search strategy or inclusion criteria are given, so completeness and representativeness cannot be verified.
  • domain assumption Any intervention on variables in the causal graph violates the i.i.d. assumption and thereby degrades generalization of data-driven models.
    Section 3 relies on this link between causality and distribution shift, citing Scholkopf et al. 2021; the paper's main argument depends on it.

how reviews work

0 comments
Cite this review

Pith. "Pith review of On using AI for EEG-based BCI applications: problems, current challenges and future trends." pith.science (2026). https://pith.science/paper/NN6HINVU

@misc{pith2026250616168,
  author       = {Pith},
  title        = {Pith review of: On using AI for EEG-based BCI applications: problems, current challenges and future trends},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NN6HINVU}},
  note         = {Machine review of arXiv:2506.16168}
}
read the original abstract

Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling exciting progress in decoding brain signals from scalp electroencephalography (EEG). Prima facie, this opens the door to revolutionary brain-computer interfaces (BCIs) designed for real life, moving beyond traditional uses to envision Brain-to-Speech, Brain-to-Image, and even a Brain-to-Internet of Things (BCIoT). However, the journey is not as straightforward as it was for Computer Vision (CV) and Natural Language Processing (NLP). Applying AI to real-world EEG-based BCIs, particularly in building powerful foundational models, presents unique and intricate hurdles that could affect their reliability. Here, we unfold a guided exploration of this dynamic and rapidly evolving research area. Rather than barely outlining a map of current endeavors and results, the goal is to provide a principled navigation of this hot and cutting-edge research landscape. We consider the basic paradigms that emerge from a causal perspective and the attendant challenges presented to AI-based models. Looking ahead, we then discuss promising research avenues that could overcome today's technological, methodological, and ethical limitations. Our aim is to lay out a clear roadmap for creating truly practical and effective EEG-based BCI solutions that can thrive in everyday environments.

Figures

Figures reproduced from arXiv: 2506.16168 by the authors.

Figure 1
Figure 1. Causal graph describing the EEG-based BCI data generating process. Different colored [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Datasets for EEG-BCI tasks. Metadata collected for six different tasks: Brain-to-Image (B2I), [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

73 extracted references · 70 canonical work pages

  1. [1]

    , Keutayeva, A

    abibullaev2023deep APACrefauthors Abibullaev, B. , Keutayeva, A. \ Zollanvari, A. APACrefauthors \ 2023 . Deep learning in EEG-based BCIs: a comprehensive review of transformer models, advantages, challenges, and applications Deep learning in eeg-based bcis: a comprehensive review of transformer models, advantages, challenges, and applications . IEEE Access

    work page 2023

  2. [2]

    , Dawwd, S A

    al2021deep APACrefauthors Al-Saegh, A. , Dawwd, S A. \ Abdul-Jabbar, J M. APACrefauthors \ 2021 . Deep learning for motor imagery EEG-based classification: A review Deep learning for motor imagery eeg-based classification: A review . Biomedical Signal Processing and Control 63 102172

    work page 2021

  3. [3]

    , Micucci, D

    amrani2024deep APACrefauthors Amrani, H. , Micucci, D. \ Napoletano, P. APACrefauthors \ 2024 . Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding Deep representation learning for open vocabulary electroencephalography-to-text decoding . IEEE Journal of Biomedical and Health Informatics

    work page 2024

  4. [4]

    , Falc \'o n, L E

    antelis2020spiking APACrefauthors Antelis, J M. , Falc \'o n, L E. \ . APACrefauthors \ 2020 . Spiking neural networks applied to the classification of motor tasks in EEG signals Spiking neural networks applied to the classification of motor tasks in eeg signals . Neural networks 122 130--143

    work page 2020

  5. [5]

    , Carrara, I

    Aristimunha_Mother_of_all_2023 APACrefauthors Aristimunha, B. , Carrara, I. , Guetschel, P. , Sedlar, S. , Rodrigues, P. , Sosulski, J. Chevallier, S. APACrefauthors \ 2025 . Mother of all BCI Benchmarks. Mother of all BCI benchmarks. Zenodo . APACrefURL https://github.com/NeuroTechX/moabb APACrefURL APACrefDOI doi:10.5281/zenodo.10034223 APACrefDOI

    doi:10.5281/zenodo.10034223 2025

  6. [6]

    , Ward, T

    awais2024lab APACrefauthors Awais, M A. , Ward, T. , Redmond, P. \ Healy, G. APACrefauthors \ 2024 . From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual...

    work page 2024

  7. [7]

    , Atapour-Abarghouei, A

    aznan2019simulating APACrefauthors Aznan, N K N. , Atapour-Abarghouei, A. , Bonner, S. , Connolly, J D. , Al Moubayed, N. \ Breckon, T P. APACrefauthors \ 2019 . Simulating brain signals: Creating synthetic eeg data via neural-based generative models for improved ssvep classification Simulating brain signals: Creating synthetic eeg data via neural-based g...

    work page 2019

  8. [8]

    , Tariq, U

    badr2024review APACrefauthors Badr, Y. , Tariq, U. , Al-Shargie, F. , Babiloni, F. , Al Mughairbi, F. \ Al-Nashash, H. APACrefauthors \ 2024 . A review on evaluating mental stress by deep learning using EEG signals A review on evaluating mental stress by deep learning using eeg signals . Neural Computing and Applications 1--26

    work page 2024

Show all 73 references
  1. [9]

    , Panagakis, Y

    barmpas2024lbm APACrefauthors Barmpas, K. , Panagakis, Y. , Adamos, D. , Laskaris, N. \ Zafeiriou, S. APACrefauthors \ 2024 . A Causal Perspective in Brainwave Foundation Models A causal perspective in brainwave foundation models . Causality and Large Models@ NeurIPS 2024. Cau...

    2024

  2. [10]

    , Panagakis, Y

    barmpas2023improving APACrefauthors Barmpas, K. , Panagakis, Y. , Bakas, S. , Adamos, D A. , Laskaris, N. \ Zafeiriou, S. APACrefauthors \ 2023 . Improving generalization of CNN-based motor-imagery EEG decoders via dynamic convolutions Improving generalization of cnn-based mot...

    2023

  3. [11]

    , Panagakis, Y

    barmpas2024causal APACrefauthors Barmpas, K. , Panagakis, Y. , Zoumpourlis, G. , Adamos, D A. , Laskaris, N. \ Zafeiriou, S. APACrefauthors \ 2024 . A causal perspective on brainwave modeling for brain--computer interfaces A causal perspective on brainwave modeling for brain--...

    2024

  4. [12]

    , Yozbatiran, N

    Bhagat2020EEGBCI APACrefauthors Bhagat, N A. , Yozbatiran, N. , Sullivan, J L. , Paranjape, R. , Losey, C. , Hernandez, Z. others APACrefauthors \ 2020 . Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation Neural ...

    2020

  5. [13]

    , Pritchard, M

    bird2021synthetic APACrefauthors Bird, J J. , Pritchard, M. , Fratini, A. , Ek \'a rt, A. \ Faria, D R. APACrefauthors \ 2021 . Synthetic biological signals machine-generated by GPT-2 improve the classification of EEG and EMG through data augmentation Synthetic biological sign...

    2021

  6. [14]

    , Astolfi, L

    borghini2014measuring APACrefauthors Borghini, G. , Astolfi, L. , Vecchiato, G. , Mattia, D. \ Babiloni, F. APACrefauthors \ 2014 . Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness Measuring n...

    2014

  7. [15]

    , Sample, M

    burwell2017ethical APACrefauthors Burwell, S. , Sample, M. \ Racine, E. APACrefauthors \ 2017 . Ethical aspects of brain computer interfaces: a scoping review Ethical aspects of brain computer interfaces: a scoping review . BMC medical ethics 18 1--11

    2017

  8. [16]

    , Hollenbenders, Y

    carrle2023generation APACrefauthors Carrle, F P. , Hollenbenders, Y. \ Reichenbach, A. APACrefauthors \ 2023 . Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder Generation of synthetic eeg data for training algorithm...

    2023

  9. [17]

    craik2019deep APACrefauthors Craik, A. , He, Y. \ Contreras-Vidal, J L. APACrefauthors \ 2019 . Deep learning for electroencephalogram (EEG) classification tasks: a review Deep learning for electroencephalogram (eeg) classification tasks: a review . Journal of neural engineeri...

    2019

  10. [18]

    , Jeong, W

    cui2024neuro APACrefauthors Cui, W. , Jeong, W. , Th \"o lke, P. , Medani, T. , Jerbi, K. , Joshi, A A. \ Leahy, R M. APACrefauthors \ 2024 . Neuro-gpt: Towards a foundation model for eeg Neuro-gpt: Towards a foundation model for eeg . 2024 IEEE International Symposium on Biom...

    2024

  11. [19]

    \ Kasabov, N

    dora2021spiking APACrefauthors Dora, S. \ Kasabov, N. APACrefauthors \ 2021 . Spiking neural networks for computational intelligence: an overview Spiking neural networks for computational intelligence: an overview . Big Data and Cognitive Computing 5 4 67

    2021

  12. [20]

    , Zhang, S

    Edelman2024BCI APACrefauthors Edelman, B J. , Zhang, S. , Schalk, G. , Brunner, P. , M \"u ller-Putz, G. , Guan, C. \ He, B. APACrefauthors \ 2024 . Non-invasive brain-computer interfaces: state of the art and trends Non-invasive brain-computer interfaces: state of the art and...

    2024

  13. [21]

    , Dessouky, M M

    ein2023eeg APACrefauthors Ein Shoka, A A. , Dessouky, M M. , El-Sayed, A. \ Hemdan, E E D. APACrefauthors \ 2023 . EEG seizure detection: concepts, techniques, challenges, and future trends Eeg seizure detection: concepts, techniques, challenges, and future trends . Multimedia...

    2023

  14. [22]

    , Wang, Y

    fu2021deep APACrefauthors Fu, M. , Wang, Y. , Chen, Z. , Li, J. , Xu, F. , Liu, X. \ Hou, F. APACrefauthors \ 2021 . Deep learning in automatic sleep staging with a single channel electroencephalography Deep learning in automatic sleep staging with a single channel electroence...

    2021

  15. [23]

    , Wang, Y

    gao2021interface APACrefauthors Gao, X. , Wang, Y. , Chen, X. \ Gao, S. APACrefauthors \ 2021 . Interface, interaction, and intelligence in generalized brain--computer interfaces Interface, interaction, and intelligence in generalized brain--computer interfaces . Trends in cog...

    2021

  16. [24]

    , Banville, H

    gramfort2021learning APACrefauthors Gramfort, A. , Banville, H. , Chehab, O. , Hyv \"a rinen, A. \ Engemann, D. APACrefauthors \ 2021 . Learning with self-supervision on EEG data Learning with self-supervision on eeg data . 2021 9th International Winter Conference on Brain-Com...

    2021

  17. [25]

    APACrefauthors \ 2018

    Grossberg2018ART APACrefauthors Grossberg, S. APACrefauthors \ 2018 . Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices Desirabi...

    2018

  18. [26]

    , Azab, A M

    habashi2023generative APACrefauthors Habashi, A G. , Azab, A M. , Eldawlatly, S. \ Aly, G M. APACrefauthors \ 2023 . Generative adversarial networks in EEG analysis: an overview Generative adversarial networks in eeg analysis: an overview . Journal of NeuroEngineering and Reha...

    2023

  19. [27]

    , Sukhbaatar, S

    hao2024training APACrefauthors Hao, S. , Sukhbaatar, S. , Su, D. , Li, X. , Hu, Z. , Weston, J. \ Tian, Y. APACrefauthors \ 2024 . Training large language models to reason in a continuous latent space Training large language models to reason in a continuous latent space . arXi...

    2024 arXiv

  20. [28]

    , Liu, J

    he2021data APACrefauthors He, C. , Liu, J. , Zhu, Y. \ Du, W. APACrefauthors \ 2021 . Data augmentation for deep neural networks model in EEG classification task: a review Data augmentation for deep neural networks model in eeg classification task: a review . Frontiers in Huma...

    2021

  21. [29]

    he2019transfer APACrefauthors He, H. \ Wu, D. APACrefauthors \ 2019 . Transfer learning for brain--computer interfaces: A Euclidean space data alignment approach Transfer learning for brain--computer interfaces: A euclidean space data alignment approach . IEEE Transactions on ...

    2019

  22. [30]

    , Zhao, L

    jianglarge APACrefauthors Jiang, W. , Zhao, L. \ Lu, B l. APACrefauthors \ 2024 . Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI Large brain model for learning generic representations with tremendous eeg data in bci . The Twelfth Interna...

    2024

  23. [31]

    , Laskaris, N A

    kalaganis2020data APACrefauthors Kalaganis, F P. , Laskaris, N A. , Chatzilari, E. , Nikolopoulos, S. \ Kompatsiaris, I. APACrefauthors \ 2020 . A data augmentation scheme for geometric deep learning in personalized brain--computer interfaces A data augmentation scheme for geo...

    2020

  24. [32]

    , Deb, N

    kalita2024aneeg APACrefauthors Kalita, B. , Deb, N. \ Das, D. APACrefauthors \ 2024 . AnEEG: leveraging deep learning for effective artifact removal in EEG data Aneeg: leveraging deep learning for effective artifact removal in eeg data . Scientific Reports 14 1 24234

    2024

  25. [33]

    , Cochrane, T

    kellmeyer2016effects APACrefauthors Kellmeyer, P. , Cochrane, T. , M \"u ller, O. , Mitchell, C. , Ball, T. , Fins, J J. \ Biller-Andorno, N. APACrefauthors \ 2016 . The effects of closed-loop medical devices on the autonomy and accountability of persons and systems The effect...

    2016

  26. [34]

    , Wang, Y

    kim2024noir APACrefauthors Kim, T. , Wang, Y. , Cho, H. \ Hodges, A. APACrefauthors \ . NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities Noir 2.0: Neural signal operated intelligent robots for everyday activities . CoRL 2024 Workshop CoRoboLearn: Adv...

    2024

  27. [35]

    \ Moshfeghi, Y

    kingphai2024mental APACrefauthors Kingphai, K. \ Moshfeghi, Y. APACrefauthors \ 2024 . Mental workload assessment using deep learning models from EEG signals: a systematic review Mental workload assessment using deep learning models from eeg signals: a systematic review . IEEE...

    2024

  28. [36]

    , Aroca-Ouellette, S

    kostas2021bendr APACrefauthors Kostas, D. , Aroca-Ouellette, S. \ Rudzicz, F. APACrefauthors \ 2021 . BENDR: Using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data Bendr: Using transformers and a contrastive self-supervised...

    2021

  29. [37]

    APACrefauthors \ 2024

    kritika2024comprehensive APACrefauthors Kritika, M. APACrefauthors \ 2024 . A comprehensive study on navigating neuroethics in Cyberspace A comprehensive study on navigating neuroethics in cyberspace . AI and Ethics 1--8

    2024

  30. [38]

    , Tang, G

    kumar2022decoding APACrefauthors Kumar, N. , Tang, G. , Yoo, R. \ Michmizos, K P. APACrefauthors \ 2022 . Decoding eeg with spiking neural networks on neuromorphic hardware Decoding eeg with spiking neural networks on neuromorphic hardware . Transactions on Machine Learning Research

    2022

  31. [39]

    , Liang, D

    lashgari2020data APACrefauthors Lashgari, E. , Liang, D. \ Maoz, U. APACrefauthors \ 2020 . Data augmentation for deep-learning-based electroencephalography Data augmentation for deep-learning-based electroencephalography . Journal of Neuroscience Methods 346 108885

    2020

  32. [40]

    , Zhu, Z

    li2024spiking APACrefauthors Li, W. , Zhu, Z. , Shao, S. , Lu, Y. \ Song, A. APACrefauthors \ 2024 . Spiking Spatio-Temporal Neural Architecture Search for EEG-Based Emotion Recognition Spiking spatio-temporal neural architecture search for eeg-based emotion recognition . IEEE...

    2024

  33. [41]

    , Ienca, M

    ligthart2023minding APACrefauthors Ligthart, S. , Ienca, M. , Meynen, G. , Molnar-Gabor, F. , Andorno, R. , Bublitz, C. others APACrefauthors \ 2023 . Minding rights: Mapping ethical and legal foundations of ‘neurorights’ Minding rights: Mapping ethical and legal foundations o...

    2023

  34. [42]

    , Lemoine, L L

    lloyd2024stress APACrefauthors Lloyd, C. , Lemoine, L L. , Al-Shaikh, R. , Ly, K T. , Kayan, H. , Perera, C. \ Pham, N. APACrefauthors \ 2024 . Stress-GPT: Stress detection with an EEG-based foundation model Stress-gpt: Stress detection with an eeg-based foundation model . Pro...

    2024

  35. [43]

    , Chouhan, T

    mane2020bci APACrefauthors Mane, R. , Chouhan, T. \ Guan, C. APACrefauthors \ 2020 . BCI for stroke rehabilitation: motor and beyond Bci for stroke rehabilitation: motor and beyond . Journal of neural engineering 17 4 041001

    2020

  36. [44]

    \ Bhattacharya, S

    naser2023towards APACrefauthors Naser, M Y. \ Bhattacharya, S. APACrefauthors \ 2023 . Towards practical BCI-driven wheelchairs: A systematic review study Towards practical bci-driven wheelchairs: A systematic review study . IEEE Transactions on Neural Systems and Rehabilitati...

    2023

  37. [45]

    APACrefauthors \ 2025

    ng2025exploring APACrefauthors Ng, J Y. APACrefauthors \ 2025 . Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrat...

    2025

  38. [46]

    , Neergaard Zahid, A

    norskov2023cslp APACrefauthors N rskov, A. , Neergaard Zahid, A. \ M rup, M. APACrefauthors \ 2023 . CSLP-AE: A contrastive split-latent permutation autoencoder framework for zero-shot electroencephalography signal conversion Cslp-ae: A contrastive split-latent permutation aut...

    2023

  39. [47]

    , Wang, Y

    ozdenizci2020learning APACrefauthors \"O zdenizci, O. , Wang, Y. , Koike-Akino, T. \ Erdo g mu s , D. APACrefauthors \ 2020 . Learning invariant representations from EEG via adversarial inference Learning invariant representations from eeg via adversarial inference . IEEE acce...

    2020

  40. [48]

    , Ram \' rez-Moreno, M A

    pacheco2024dance APACrefauthors Pacheco-Ram \' rez, M A. , Ram \' rez-Moreno, M A. , Kukkar, K. , Rao, N. , Huber, D. , Brandt, A K. Contreras-Vidal, J L. APACrefauthors \ 2024 . Neural Dynamics of Creative Movements During the Rehearsal and Performance of “LiveWire Neural dyn...

    2024

  41. [49]

    , Luo, Z

    pei2021data APACrefauthors Pei, Y. , Luo, Z. , Yan, Y. , Yan, H. , Jiang, J. , Li, W. Yin, E. APACrefauthors \ 2021 . Data augmentation: Using channel-level recombination to improve classification performance for motor imagery EEG Data augmentation: Using channel-level recombi...

    2021

  42. [50]

    ren2021reconstructing APACrefauthors Ren, Z. , Li, J. , Xue, X. , Li, X. , Yang, F. , Jiao, Z. \ Gao, X. APACrefauthors \ 2021 . Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning Reconstructing seen image from br...

    2021

  43. [51]

    , Jaiswal, A

    roy2019towards APACrefauthors Roy, K. , Jaiswal, A. \ Panda, P. APACrefauthors \ 2019 . Towards spike-based machine intelligence with neuromorphic computing Towards spike-based machine intelligence with neuromorphic computing . Nature 575 7784 607--617

    2019

  44. [52]

    , Locatello, F

    scholkopf2021toward APACrefauthors Sch \"o lkopf, B. , Locatello, F. , Bauer, S. , Ke, N R. , Kalchbrenner, N. , Goyal, A. \ Bengio, Y. APACrefauthors \ 2021 . Toward causal representation learning Toward causal representation learning . Proceedings of the IEEE 109 5 612--634

    2021

  45. [53]

    , Sirbu, R

    schroder2025cyber APACrefauthors Schroder, T. , Sirbu, R. , Park, S. , Morley, J. , Street, S. \ Floridi, L. APACrefauthors \ 2025 . Cyber Risks to Next-Gen Brain-Computer Interfaces: Analysis and Recommendations Cyber risks to next-gen brain-computer interfaces: Analysis and ...

    2025

  46. [54]

    , She, Q

    song2025domain APACrefauthors Song, H. , She, Q. , Fang, F. , Liu, S. , Chen, Y. \ Zhang, Y. APACrefauthors \ 2025 . Domain generalization through latent distribution exploration for motor imagery EEG classification Domain generalization through latent distribution exploration...

    2025

  47. [55]

    , Biancofiore, G M

    sorino2024ariel APACrefauthors Sorino, P. , Biancofiore, G M. , Lof \`u , D. , Colafiglio, T. , Lombardi, A. , Narducci, F. \ Di Noia, T. APACrefauthors \ 2024 . Ariel: Brain-computer interfaces meet large language models for emotional support conversation Ariel: Brain-compute...

    2024

  48. [56]

    , Yamada, E

    tanaka2024neurophysiological APACrefauthors Tanaka, M. , Yamada, E. \ Mori, F. APACrefauthors \ 2024 . Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia Neurophysiological markers o...

    2024

  49. [57]

    , Ghodrati, M

    tavanaei2019deep APACrefauthors Tavanaei, A. , Ghodrati, M. , Kheradpisheh, S R. , Masquelier, T. \ Maida, A. APACrefauthors \ 2019 . Deep learning in spiking neural networks Deep learning in spiking neural networks . Neural networks 111 47--63

    2019

  50. [58]

    , Paez, S

    Theofanopoulou2024dance APACrefauthors Theofanopoulou, C. , Paez, S. , Huber, D. , Todd, E. , Ram \' rez-Moreno, M A. , Khaleghian, B. Contreras-Vidal, J L. APACrefauthors \ 2024 . Mobile brain imaging in butoh dancers: from rehearsals to public performance Mobile brain imagin...

    2024

  51. [59]

    , Yang, J

    tian2021new APACrefauthors Tian, F. , Yang, J. , Zhao, S. \ Sawan, M. APACrefauthors \ 2021 . A new neuromorphic computing approach for epileptic seizure prediction A new neuromorphic computing approach for epileptic seizure prediction . 2021 IEEE International Symposium on Ci...

    2021

  52. [60]

    , Torres, E A

    torres2020eeg APACrefauthors Torres, E P. , Torres, E A. , Hern \'a ndez- \'A lvarez, M. \ Yoo, S G. APACrefauthors \ 2020 . EEG-based BCI emotion recognition: A survey Eeg-based bci emotion recognition: A survey . Sensors 20 18 5083

    2020

  53. [61]

    , Muhammad, N

    varbu2022past APACrefauthors V \"a rbu, K. , Muhammad, N. \ Muhammad, Y. APACrefauthors \ 2022 . Past, present, and future of EEG-based BCI applications Past, present, and future of eeg-based bci applications . Sensors 22 9 3331

    2022

  54. [62]

    , Convertino, L

    vigliocco2024ecological APACrefauthors Vigliocco, G. , Convertino, L. , De Felice, S. , Gregorians, L. , Kewenig, V. , Mueller, M A. others APACrefauthors \ 2024 . Ecological brain: reframing the study of human behaviour and cognition Ecological brain: reframing the study of h...

    2024

  55. [63]

    , Yang, R

    wan2021review APACrefauthors Wan, Z. , Yang, R. , Huang, M. , Zeng, N. \ Liu, X. APACrefauthors \ 2021 . A review on transfer learning in EEG signal analysis A review on transfer learning in eeg signal analysis . Neurocomputing 421 1--14

    2021

  56. [64]

    , Liu, W

    wang2024eegpt APACrefauthors Wang, G. , Liu, W. , He, Y. , Xu, C. , Ma, L. \ Li, H. APACrefauthors \ 2024 . EEGPT : Pretrained Transformer for Universal and Reliable Representation of EEG Signals EEGPT : Pretrained transformer for universal and reliable representation of EEG s...

    2024

  57. [65]

    , Lin, X

    wang2020supervised APACrefauthors Wang, X. , Lin, X. \ Dang, X. APACrefauthors \ 2020 . Supervised learning in spiking neural networks: A review of algorithms and evaluations Supervised learning in spiking neural networks: A review of algorithms and evaluations . Neural Networ...

    2020

  58. [66]

    \ Peters, J

    weichwald2021causality APACrefauthors Weichwald, S. \ Peters, J. APACrefauthors \ 2021 . Causality in cognitive neuroscience: concepts, challenges, and distributional robustness Causality in cognitive neuroscience: concepts, challenges, and distributional robustness . Journal ...

    2021

  59. [67]

    , Kwon, M

    won2019p300 APACrefauthors Won, K. , Kwon, M. , Jang, S. , Ahn, M. \ Jun, S C. APACrefauthors \ 2019 . P300 speller performance predictor based on RSVP multi-feature P300 speller performance predictor based on rsvp multi-feature . Frontiers in human neuroscience 13 261

    2019

  60. [68]

    , Kuang, K

    wu2024causality APACrefauthors Wu, A. , Kuang, K. , Zhu, M. , Wang, Y. , Zheng, Y. , Han, K. Zhang, K. APACrefauthors \ 2024 . Causality for large language models Causality for large language models . arXiv preprint arXiv:2410.15319

    2024 arXiv

  61. [69]

    , Klaes, C

    xavier2022error APACrefauthors Xavier Fid \^e ncio, A. , Klaes, C. \ Iossifidis, I. APACrefauthors \ 2022 . Error-related potentials in reinforcement learning-based brain-machine interfaces Error-related potentials in reinforcement learning-based brain-machine interfaces . Fro...

    2022

  62. [70]

    , Westover, M

    yang2024biot APACrefauthors Yang, C. , Westover, M. \ Sun, J. APACrefauthors \ 2024 . Biot: Biosignal transformer for cross-data learning in the wild Biot: Biosignal transformer for cross-data learning in the wild . Advances in Neural Information Processing Systems 36

    2024

  63. [71]

    , Zhang, D

    yuan2024brant APACrefauthors Yuan, Z. , Zhang, D. , Chen, J. , Gu, G. \ Yang, Y. APACrefauthors \ 2024 . Brant-2: Foundation model for brain signals Brant-2: Foundation model for brain signals . CoRR

    2024

  64. [72]

    , Willig, M

    zevcevic2023causal APACrefauthors Ze c evi \'c , M. , Willig, M. , Dhami, D S. \ Kersting, K. APACrefauthors \ 2023 . Causal parrots: Large language models may talk causality but are not causal Causal parrots: Large language models may talk causality but are not causal . Trans...

    2023

  65. [73]

    , Liu, Y K

    zhao2024eeg APACrefauthors Zhao, Y D. , Liu, Y K. , Zheng, W L. \ Lu, B L. APACrefauthors \ 2024 . EEG Data Augmentation for Emotion Recognition Using Diffusion Model Eeg data augmentation for emotion recognition using diffusion model . 2024 46th Annual International Conferenc...

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.