Are EEG Foundation Models Worth It? Comparative Evaluation with Traditional Decoders in Diverse BCI Tasks
Authors: Liuyin Yang
Qiang Sun
Ang Li
Marc M. Van Hulle
Venue: ICLR 2026 Poster (2026)
Keywords: Foundation Model
Brain–Computer Interface
EEG
Benchmark
Primary area: applications to neuroscience & cognitive science
TL;DR: We present a comprehensive benchmark of EEG foundation models against state-of-the-art neural and non-neural decoders across diverse BCI tasks, introducing a novel six-dimensional evaluation framework supported by rigorous statistical analysis.

Abstract: Foundation models have recently emerged as a promising approach for learning generalizable EEG representations for brain–computer interfaces (BCIs). Yet, their true advantages over traditional methods—particularly classical non-neural approaches—remain unclear. In this work, we present a comprehensive benchmark of state-of-the-art EEG foundation models, evaluated across diverse datasets, decoding tasks, and six evaluation protocols, with rigorous statistical testing. We introduce spatiotemporal EEGFormer (ST-EEGFormer), a simple yet effective Vision Transformer (ViT)-based baseline, pre-trained solely with masked autoencoding (MAE) on over 8M EEG segments. Our results show that while fine-tuned foundation models perform well in data-rich, population-level settings, they often fail to significantly outperform compact neural networks or even classical non-neural decoders in data-scarce scenarios. Furthermore, linear probing remains consistently weak, and performance varies greatly across downstream tasks, with no clear scaling law observed among neural network decoders. These findings expose a substantial gap between pre-training and downstream fine-tuning, often diminishing the benefits of complex pre-training tasks. We further identify hidden architectural factors that affect performance and emphasize the need for transparent, statistically rigorous evaluation. Overall, this study calls for community-wide efforts to construct large-scale EEG datasets and for fair, reproducible benchmarks to advance EEG foundation models.

Published as a conference paper at ICLR 2026
ARE EEG FOUNDATION MODELS WORTH IT? COMPARATIVE EVALUATION WITH TRADITIONAL DECODERS IN DIVERSE BCI TASKS
Liuyin Yang"", Qiang Sun" Ang LiP, Mare M. Van Hulle
"Laboratory for Neuro-& Psychophysiology, Department of Neurosciences, KU Leuven
Shanghai Advanced Research Institute, Chinese Academy of Sciences
Code & Models: https: http:/tt.....ll.lyyiioongng//o//
ABSTRACT
Foundation models have recently emerged a promising approach for learning generalizable EEG representations for computce interfaces (BCIs). Yet, their true advantages over traditional methods pariddslaattlllarlly classical non-neural approaches-remail remain unclear. In this work, we present a comprehensive bench- mark of state-of-the-ar EEG foundation models, evaluated across diverse datasets, decoding tasks, and six evaluation protocols, with rigorous statistical testing. We introduce spatiotemporal EEGFormer (ST-EEGFormer), simple yet effective Vi- sion Transformer WVTT-bbaee baseline, pre-trained solely with masked autoen- coding (MAE) on over 8M EEG segments. Our results show that while fine-tuned foundation models perform well in data-rich, population-level settings, they often fail to significantly outperform compact neural networks or even classical non- neural decoders in data-scarce scenarios. Furthermore, lincar probing remains consistently weak, and performance varies greatly across downstream tasks, with clear scaling law observed among neural network decoders. These findings expose substantial gap between pre-training and downstream fine-tuning, often diminishing the benefits of complex pre- rre-training tasks. We further identify hidden architectural factors that affect performance and emphasize the need for transpar- ent, statistically rigorous evaluation. Overall, this study calls for community efforts to construct large-scale EEG datasets and for fair, reproducible benchmarks advance EEG foundation models.
INTRODUCTION
Eleetroenneephalography (EEG) is a non-invasive brain recording technique widely used in Brain- Computer Interface (BCI) research improve the quality of life for patients and to augment the capabilities of healthy individuals. Various EEG paradigms have been explored to advance BCI de- velopment. For example, motor imagery (MI)-the the mental simulation of physical movement-can be employed to control exoskeletons (Soekadar et al.
2016:
Choi et al., 2020), navigate real or vir-
tual environments (Choi & Cichocki] 2008 Tsui al.
2011 Yang & Van Hulle 2023), or facilitate
rehabilitation (Baniqued et al.) 2021 |iao et al. 2023). Event-related potentials (ERPs), such as the
P300 response-a positive deflection elicited by infrequent events have been leveraged for user attention decoding and smart home applications (Holzner et al. [2009] Masud et al. 2017). Visual- evoked potentials (VEPs), including steady-state visual-evoked potentials (SSVEPs), are amplitude changes in EEG elicited by visual stimuli, enabling high-speed spelling devices (Wittevronge) &
Van Hulle 2017
Nakanishi et al. 2018a Xing et al. 2018).
Despite the rapid progress of deep learning in BCI, which has led to state-of-the-ae performance across many tasks, variations in experimental paradigms, hardware setups, and limited dataset sizes often necessitate training separate models for each task (Murad & Rahimi 2025). A promising solution is the use of EEG foundation models-models pre-trained on large-scale EEG datasets to learn generalizable representations that can be adapted a variety of downstream tasks. In re- cent years, numerous EEG foundation models have reported strong performance across diverse BCI
"Equal contribution.
1 "Corrsponndence liuyin. kun.ggkkulllaa luven. & marc.vanhil 1e@kul euven. be

---

Published as a conference paper at ICLR 2026
tasks. However, many studies lack comparisons to classical EEG decoding methods and typically evaluate performance on only one or two protocols, such as leave-one-out zero-shot or population decoding often without statistical testing. While existing work demonstrates the feasibility of ap- plying foundation models to EEG decoding, the practical benefits remain uncertain, especially given the substantial computational and time costs associated with pre-training and fine-tuning.
Contributions: We present a comprehensive benchmark of EEG foundation models, introducing a six-dimensional evaluation framework that encompasses seven classification tasks and two regres- sion tasks. Our extensive experiments enable statistically rigorous comparisons across decoders and evaluation protocols. We systematically compare foundation models against both classical non- neural and neural network-basee EEG decoders across diverse paradigms. Additionally, we intro- duce ST-FEG simple, transparent Vision Transformer-based (Dosovitskiy al. 2021) foundation model pre-trained solely with MAE (He et al., 2022) on more than 8 million raw EEG segments--ervvng as a strong baseline. Our key findings are as follows:
1. Linear probing remains weak and task-dependent. Across foundation models, linear probing generally yields suboptimal results, with performance fluctuating significantly across different downstream tasks. This highlights the limited robustness and inconsistent generalization of cur- rent pre-trained representations.
2. Foundation models are not universally better. While fine-tuned foundation models perform well in data-rich, population-level settings, they often fail to significantly outperform compact neural networks or even classical non-neural methods in data-scarce or subject-specific settings. Scaling does not guarantee success. Although larger foundation models have more capacity, they do not reliably outperform smaller neural decoders, particularly complex and data- tasks such as motor imagery or inner speech.
Simple pre-training can be effective. Challenging the prevailing view from LaBraM (Jiang ct al. 2024) that MAE raw EEG is ineffective, demonstrate that direct MAE pre-traiming on raw signals can in fact produce top-performing models.
Our findings reveal critical gaps between common pre-training assumptions and empirical decod- ing capabilities, underscoring the strength of simple, ovven-overlooked classical baselines. They also highlight the urgent need for a large-scale, standardized EEG anaalott-aalogous to Ima- geNet (Deng al. 2009)-foo for both pre-training and downstream evaluation. Such resource is essential to explore scaling behavior, rather than overfitting for marginal gains on limited data. To support transparent benchmarking and future research, release all code and models
RELATED WORK
Recent years have seen a surge of BEG foundation models leveraging large-scale self-supervised pre-training to learn transferable neural representations (Kostas et al. [222 Yang et al.) 2023 Wang al.) 2024 Jiang et al., 2024] Wang al.) (025). While these approaches claim improved gen- eralization, they are typically benchmarked under limited evaluation protocols and seldom com- pared against classic nou-ne network decoders, which often remain competitive in practical BCT tasks (Chevallier al. 2024). For example, classical methods like CSP/FBCSP (Ramoser et al.) 2000 Ang et al. a 2008). Riemannian classifiers (Congedo et al.) 2017). FBCCA (Chen et al. 2015), and TRCA (Nakanishi et al. 2018b) continue to provide robust baselines for their respective tasks. often outperforming compact neural network models (Schirmmesster et al. 2017; Lawhern et al. 2018) in data-limited scenarios. However, systematic comparisons across paradigms and with rig- orous statistical testing are still lacking. To address these gaps, we provide the first comprehensive benchmark spanning foundation models, classic neural networks, and non-neural decoders across diverse classification and regression tasks. A detailed review of prior EEG foundation, neural, and non-neural methods is provided in Appendix
3 METHODS
3.1
EVALUATION PROTOCOLS
In this paper, we propose six evaluation protocols that provide comprehensive view of model generalization, transferability, and practical utility in real-world BCI settings, as demonstrated and
Pree-triined model weights and the corresponding implementation are publicly available at: r
https: / com/ http://wwww...mm..o.mmmmamaaao.a
2

---

Published as a conference paper at ICLR 2026
(a)
Population Decoding
Per-Subject Decoding
Seeve--ne-Suujecc-OOu (LOO) Decoding
Training
Testig
Training
Tasling
Trainig
Testing
Testing
&
Deig
8
8
subject
2
Pepulatbo
SI
8
&
8 8 & & Training Sunjects
& & & Testng Subjects
Protocols
(b)
Input Sgrelo
Temiporal Positional Encadinng (TPE)
Classification
Motor Imagery
Class Tokee
Averaging
SSVEP ERN Inner Speach
Downstraem Tasks
Tie tanooeee
Encosher
Libar
Channel
Regression
Aucitory Atler
Spetial Positional Entzding (SPE)
-Viglanco
Time
ST-EEGFormer
Prec Traininn
Concorentioon
Mask Tokkn
Reconstructed EEG Signals
Moshing
Tha nafomaa
CI Aumang
Enoodder
Figure 1: (a) Graphical representation of the six evaluation protocols. In population decoding, data from all subjects are pooled to train single model, which is then tested on each subject individually, yielding the (1) Population performance. In per-suhject decoding, separate model is trained for each subject and tested both on itself-giving (2) Per-Subject (Self) performance-anm on other subjects for (3) Per-Subject (Trans- fer) performance.
subjects serr--onssuujc--oour (LOO) decoding, population model is trained except the LOO subject. This model is evaluated on all subjects, with its performance on the LOO subject rep- resenting (4) LOO Zero-Shot performance. After fine-tuning the model on the LOO subject, is again tested on all subjects. Performance on the LOO subject after fine-tuning gives (5) LOO Fine-Tune performance, while changes in population subjects' performance before after fine-tuning quantify the generalization ca- pability, denoted as (6) L,00 Drop. (b) Graphical representation of the proposed SY-EEEGFormer During pre-tre training, the input EEG data are divided into segments along spatial and temporal dimensions. Each segment is tokenized through linear projection layer, with each token receiving its correspouding temporal positional encoding (TPE) and spatial positional encoding (SPE), After randomly masking 75% all tokens, the encoder processes the remaining unmasked tokens. The mask tokens, with their added temporal and spa- tial positional embeddings, are then concatenated with the encoder output to form a full set of tokens. This full set of tokens input a small decoder comprising transformer followed by linear projection layer, which reconstructs the original EEG signal. Once the model is pre-trained, only the encoder is utilized as the TT-EEGFormer model for fine-tuning on a downstream dataset.
explained in Figure (a). Population Decoding quantifies a model's ability to capture global neural patterns that generalize across individuals, valuable to study population-level features and to appli- cations where single universal model is desired. Per-Subject (Self) reflects the traditional BCT approach, assessing performance when the model is tailored for individual users (also known as subject-dependent or subject-specific models). Per-Subject (Transfer) and LOO Zero-Shot both interrogate the transfcrability of learned representations: the former asks how well a subject-specift model works on another individual, while the latter tests whether model trained on a cohort can be directly deployed to novel subject without further adaptation. LOO Fine-Tune examines how effectively population model can be adapted to a new subject through limited subject-specific train- ing. Finally, LOO Drop evaluates the extent which model fine-tuning a new subject erodes prior knowledge-ake akin to catastrophic bargetting-hhghlighhee tation and preservation of generalized population knowledge. Together, these dimensions offer comprehensive benchmark for EEG foundation models.
the balance between subject adap-
3.2 DOWNSTREAM BENCHMARK TASKS
We selected seven classification tasks and two regression tasks to comprehensively evaluate EEG foundation models across diverse EEG paradigms with varying complexity levels. The classi- fication tasks include: error-related negativity (ERN) classification task (Error-ERN) (Kueper ct al. 2024); a three-class Alzheimer's Disease classification task distinguishing Alzheimer's s, fron- totemporal dementia, and healthy subjects (Alzheimer's) (Miltiadous al. 2023): a four-class inner
3

---

Published as a conference paper at ICLR 2026
speech classification task (Inner Speech) (Nicto et al. 2022 four-class motor imagery task from the classic BCI Competition IV 2a dataset (BCI-IV-2A) (Tangermann et al. 2012); a seven-class upper limb motor execution task (Motor-Execution)
and seven-class upper limb motor imagery task (Motor-Imaginnl (mggint-maiinnnion)) both derived from the same dataset (Ofner et al. 2017); and a challeng-
ing 40-target binocular steady-state visual evoked potential (SSVEP) classification task (Binocular- SSVEP) (Yike et al., 2024). All selected datasets have been rarely explored in existing literature and were absent from the pre-training data of the cited foundation models, with the exception of BCI-IV-2A. We included this well- motor imagery benchmark specifically to serve as rec- ognizable baseline, acknowledging its extensive use in prior foundation model benchmarks and its inclusion in the pre-training of our ST-EEGFormer. Additionally, the selected tasks vary signifi- cantly complexity, ranging from the relatively simple binary ERN task to the intricate 40-target binocular SSVEP task, characterized by complex inter-modulation components arising from binoc- ular swap stimulation (Yan et al., 2011 Sun et al., 2024). For classification tasks, top-1 accuracy (Ace1), top-2 accuracy (Acc2), balanced accuracy (BAcc), area under the curve (AUC), and Cohen's 's kappa coefficient (Kappa) were used as evaluation metrics for model performance. In addition to the seven tasks described above, we also benchmarked two widely used datasets- SACssS-FFEEDDD (9-class emotion recognition) (Chen et al., 2023a) and TUEV (6-class EEG event classificationn) (Obeid & Picone (016)uunde the conventional cross-subject zero-shot evaluation protocol.
For regression, we evaluate on two datasets. The DTU auditory attention decoding dataset targets reconstruction of the attended speech envelope from EEG recordings (Fuglsang al. 2018). SEED-
VIG (Zheng & Lu, 2017) predicts vigilance level in a virtual, monotonous driving task. Model performance assessed using mean squared error (MSE) and Pearson correlation coefficient (R)..
All benchmarked datasets underwent minimal and standardized pre-processing steps. EEG signals were band-pass filtered between 0.1-128 Hz and downsampled to 256 Hz if the original sampling rate is higher. During model training. epochs were further resampled to match each foundation model's native sampling rate. For most classification datasets, we applied 5-fold cross-validation scheme within each subject. Exceptions are the BCI-IV-2A and ERN datasets, for which we strictly followed the original competition-styy the conventional cross-subject split approach, where 80% subjects were used as the training subjects, and the remaining 20% as the test subjects. For the two regression datasets, we used the first 80% of each subject's ss recording as the training set and the remaining 20% as the test set. Detailed dataset descriptions and train/test split strategies are provided in Appendix D
rrain/validation splits, For TUEV and FACED, we followed
3.3
BENCHMARK MODELS
3.3. FOUNDATION MODELS
For all benchmark datasets, we evaluated a range of ERG foundation models, including BENDR (Kostas et al., 202I). BIOT Yang et al. 2023). LaBraM (Jiang et al.] 2024). EEGPT (Wang ct al. 2024). and CBraMod (Wang et al. 2025 In addition, we pre-trained our proposed model, ST-EEGFormer, using a straightforward MAE strategy on raw EEG signals provide transpar- ent baseline (Figure [ (b)). Detailed pre-training procedures for ST-EEGFormer are described in Appendix E while additional intermediate benchmark experiments validating its effectiveness are presented in Appendix E.9 For all foundation models, we systematically evaluated both linear probing and fine-tuning, with implementation details provided in Appendix F.
3.3.2 CLASSIC NEURAL NETWORK MODELS
We benchmarked two well-established convolutional neural network (CNN)-based EEG decoders: DeepConvNet (Schirmeister et al., 2017) and EEGNet (Lawhern et al.] 2018). Additionally, we included more recent, transformer-basee architectures, EEG Conformer (Song et al. 2023) and CT- Net (Zhao et al. 2024). which have demonstrated state-of-the-art performance yet remain compu- tationally simpler compared to larger foundation models. Implementation details for these models can be found in Appendix F2
3.3.3 CLASSIC NON-NEURAL NETWORK MODELS
Depending on the specific downstream task, we benchmarked the most widely used classical models accordingly. For movement and speech classification tasks, we included CSP- (Ramoser et al. 2000) and FBCSP- (Ang et al. 2008) based pipelines (CSP-LDA, CVP-SVM, FBCSP-LDA, and FBCSP.
4

---

Published as a conference paper at ICLR 2026
(b)
Lincar Probe
Fine-Tunine
Accuracy
Accuracy
1.0
0.8
0.5
0.4
0.0
BIOT
BENDR
CiraMldd
EEGPT
LaBMM
ST-EEGF ST-EEGFemenr Fommnns
ST-EEGFomere
ST-EEGF Fammer
Per-Suhject (Seff)
Population
LOO Fine- Thme
Per-Subject (Ser)
Population
LO0 Fine- Tune
Linex Probe Tuning
(c)
(d)
1.0
Lineur Probe
Fine-Tuning
Performunce Drop
0.8
Accuracy
1.0
0.8
0.6
0.6
0.1
0.0
Per-Subjjctt (Transeer)
LOO Zero Shot
Per-Suhject (Thamster)
LO0 Zero-Shol
0.0
Fine-Tuning
Figure 2: Comparison of linear- ereaar-pobbig and fine-tuning foundation models across evaluation proto- cols. (a) Per-model performance. Classification accuracy for each foundation model, aggregated across all subjects and downstream tasks, under Population, Per-Subject (Self), and LOO Fine-Tune protocols. (b) Within-subject performance. Comparison of all foundation models (pooled) across Per-Subject (Self), Population, and LOO Fine-Tune protocols. (c) Cross-subject performance. Comparison Per-Subject (Transfer) and LOO Zero-Shot protocols. (d) Generalization performance. Accuracy drop population subjects after subject-specific fine-tuning, comparing lincar probing and fine-tuning. In panels, light grey indicates linear probing and dark gray indicates line-tuning Box plots show median (red line), mean (white dot), and interquartile range; violin plots illustrate the full data distribution. Asterisks in (b)-(d) indicate statistically significant differences (Wilcoxon signed-rank test, Bonferroni-corrcted;;; "*; p 0.001, ns: significant).
SVM). as well as Riemannian geometry- based classifiers, including Minimum Distance to Mean (MDM) (Barachant et al.) 20126). Fisher Geodesic MDM (FgMDM) (Barachant al. 2012b) and tangent space mapping (TS) with ElasticNet (TS-FllastiNeet) (Corsi et al.) 2022). For Alzheimer's diagnosis, we included four decoding pipelines used in the dataset paper of (Miltiadous al. 2023), which extract Relative Band Power (RBP) features as input for Random Forest (RBP-RF), SVM (RBP-SVM), k-Nearest Neighbors (RBP-KNN), and LightGBM (RBP-LightGBM) classifiers. For ERN detection, included XDAWN-LDA (Rivet et al., 2009), xDAWNCov-MDM (Barachant 2014). XDAWNCov-TS TS-MVM (Chevallier et al. 2018), ERPCov-MDM (Barachant Congedo 2014 and DCPM (Xiao et al.) (020). For SSVEP target recognition, included the two decoding
models FBCCA and TRCA used in the dataset paper (ike et al 2024 Implementation details for
these models can also be found in Appendix F3
RESULTS
In this section, we present findings addressing our key research questions. For detailed results per dataset, please refer to Appendix
4.1 Do FOUNDATION MODELS LEARN ROBUST REPRESENTATION AFTER PRE-TRAINING?
Figure summarizes the performance of foundation models under both linear probing and fine- tuning across all subjects and downstream classification tasks. The corresponding summary table is listed in Appendix Tables G. and and G.2 In Figure 2 (a), fine-tuning generally yields higher accuracy than linear probing for all foundation models, with the exception of EEGPT. Figure (b) compares within-subject evaluation protocols, showing that classic per-subject training results in significantly lower accuracy than both population-leve and LOO fine-tuned models, while no significant differ- ence is observed between the latter two. Figure (c) highlights transfer performance, demonstrating that population-traine Shot) than models trained individually per subject (Per-Subject (Transfer)), for both linear probing and fine-tuning Finally. Figure 2 (d) illustrates that subject-specifi. fine-tuning leads to a larger ac- curacy drop (generalization drop) the population subjects compared to linear probing, suggesting a tendency for ine-ttned models to "forget" information learned from the broader population (i.c., increased catastrophic forgetting).
models achieve substantially better cross-subject generalization (LOO Zero-
5

---

Published as a conference paper at ICLR 2026
Average
12.00
5144
15.95
12.11
10.21
1.13
10.82
1.00
Ranii
6.55
501
LO0 Drop
Shtt-Sho
Fine-Tune
Subject (Trunsfer)
20
Per Subject (Sel)
1
Population
DespComnNee
ERGNe
Confamer
CTher
BENDR (D
CBraMeed (1)
EEGPT (1)
STEEGFametss (1)
LaBraM (1)
BIOT (D)
(
BENDR (
CHraMeed ()
ST-BEOFomma-- ())
LaBraM (
ST-EEGE SrEEGFomee-bo. (
ST-ERGFamerd (0)
BIOT
S
EROPT
Figure 3: Comparison of classic neural network (NN) decoders and foundation models using aggregated ranks across six evaluation protocols. The bar plot on top shows the average rank (lower better) across metric, subject, dataset, and protocol. Green bars denote classic NN models, while purple and red indicate linear-probed (I) and fine-tuned (f) foundation models, respectively. The heatmap at the bottom shows the rank (lower in red is better) averaged across metric, subject, and dataset per protocol.
4.2 Do FOUNDATION MODELS OUTPERFORM CLASSIC NN-BASED DECODERS?
Figure 3 provides a comparison between classic neural network (NN) decoders and foundation models across six distinct evaluation protocols, using aggregated rank as the performance met- ric. Per-dataset result can be obtained in Appendix G.3 The results reveal that, while certain linear-probed foundation models perform competitively
specific evaluation contexts, classic NN models--particlaat CTNet- Const-consistently outperform all linear-probed foundation models across most evaluation protocols, except for the LOO Drop protocol. Notably, among fine-tuned foun- dation models, only the largest fully fine-tuned foundation architectures (i.e., ST-EEGFormer-J) achieve performance levels equal to or surpassing those of the best-performing classic NN mod- els. This underscores that despite their promise, foundation models do not inherently outperform well-established, classic neural network approaches.
4.3 Do FOUNDATION MODELS OUTPERFORM CLASSIC NON-NN-BASED DECODERS?
Figure compares the pest-performing decoders from each group across six evaluation protocols. A detailed per-dataset result can be found in Appendix G.4 Overall, fine-tuned foundation mod- consistently achieve the highest or compa- rable top performance. In contrast, classic neural and non-neural decoders show more variable re- sults depending on the evaluation setting. Linear- Linear-probed foundation models generally underperform across all protocols. Detailed observations are as follows:
els els-particularly the largest variant, T.--EEGFormer--consseeetttt
Population Fine-tuned foundation models and ST ST-EEGrrrrr-- achieve the highest accuracy with no significant difference between them. Both significantly outperform classical NN and non-NN decoders, and classical NN decoders in turn outperform linear-probed foundation models.
Per-Subject (Self) ST-EEGFormer- shows notable performance advantage, but with no statis- tically significant differences between the classic non-NN decoders.
Per-Subject (Transfer) ST-EEGFormer- is statistically better than every other group, whereas pairwise comparisons among the remaining models reveal no significant differences.
LOO Zero-Shot Classic non-NN decoders yield the lowest mean accuracy. Classic NN decoders, fine-tuned foundation models, and ST-BEGFormer-I perform similarly, with no significant differ- ences among them.
LOO Fine-Tune Classic NN decoders perform comparably to both fine-tuned foundation models and ST-EEGFormer-l, and all three significantly outperform linear-probed foundation models. LO0 Drop Classic NN decoders exhibit the most pronounced generalization drop after subject- specific fine-tuning, performing significantly worse than all foundation model groups
4.4 Do EEG CLASSIFICATION MODELS TRANSFER TO REGRESSION?
Table I summarizes the regression performance of all neural network betwork-based models on DTU and SEED-VIG under the 1.0 Shot protocol; full per-model results provided in Ap-

---

Published as a conference paper at ICLR 2026
(a)
Population
(b)
Per-Subject (Self)
(c)
Per-Subject (Transfer)
10
Accuracy
1.0
Accuracy
Accuracy
08-
0.4
04
0.0-
(d)
L.00 Zero-Shot
(e)
LOO Fine-Tune
LOO Drop
1.0
0.60
Ascuracy
1.0
Accuracy
0.8
Acquracy
0:45
0.8
0.6
0.6
0.30
044
0.15
Classic nom-NN decoder
Classic NN decoder
Foundation
Foundation
ST-EEGFormer-1C
Figure 4: Comparison of pest-performing models across decoder groups and evaluation pro- tocols. Violin plots show distribution accuracy scores for the best model within each decoder group-classiy non-NN decoders (bluc), classic NN decoders (green), lincar-probed foundation models (pur- ple), fine-tuned foundation models (red), and the large ST-EEGFFrmer-l
(rrage))unddr each evaluation pro- tocol: (a) Population, (b) Per-Subject (Self), (c) Per-Subject (Transfer), (d) LOO Zero-Shot, (e) LOO Fine-Tune, and (I) LOO Drop. LOO Fine- and LOO Drop are not applicable for classic non-NN decoders. Accuracy is used instead of rank to highlight meaningful performance differences. For each group, the hest-performing model may vary across datasets. Statistical significance is assessed using permuta- tion testing (Mresample 50,000) with Bonferroni correction (** p < 0.001, "*: p 0.01, p 0.05). Box plots within violins indicate the median (red line), mean (white dot), and interquartile range.
pendix 1.1.1 and Appendix G.I.IT For DTU, the task is to predict single continuous target from 1-S EEG epochs, evaluated with mean squared error (MSE) and Pearson correlation (R). For SEED VIG, the task is predict vigilance level from 5-s EEG epochs. On DTU, the best models are CTNet and the linear-probed EEGPT, both reaching R ~ 0.05. On SEED-VIG, EEGNet and DeepConvNet exceed R >> 0.45.
Figure 5 further compares model families. On DTU, classic decoders outperform fine-tuned foun- dation models, and linear-probed foundation models also outperform fine-tuned foundation models; however, the top model from each family does not differ significantly from the others. On SEED- VIG, although the average performance of classic models exceeds that of foundation variants, the difference is not statistically significant.
4.5 CAN WE OBSERVE ANY SCALING LAW IN EEG CLASSIFICATION TASKS?
Figure 6 illustrates the relationship between NN-based EEG decoder size and both classification performance and training time. Panel (a) shows that, although there is a slight upward trend in normalized accuracy with increasing model size, the poor logarithmic fit suggests that a clear scaling law does not exist for downstream EEG classification tasks. contrast, Panel (b) demonstrates that training time per EEG epoch grows exponentially with model size, achieving reasonably good fit (R2 0.60), indicating that computational cost scales much faster than accuracy improvements.
5 DISCUSSION
Tine-Tuning vs. Linear Probing Fine-tuning enables foundation models models-particularly larger variants like ST-EEGFormer- achieve strong performance in population- decoding. con- trast, linear probing consistently yields poor performance across all models and evaluation protocols (except for LOO Drop), as shown in Figure 2 and Figure 3, This suggests that current pre-training strategies do not produce EEG representations that are sufficiently generalizable and discrimina- tive across a broad range of BCI tasks. Supporting evidence is provided in Appendix G.5 where attention aatention-weight weight visualizations reveal that the regions of interest change substantially after fine- tuning, underscoring the dependence of learned representations on task- sssc-spcc adaptation. How- ever, this does not suggest that foundation models fail to learn any useful representations. For

---

Published as a conference paper at ICLR 2026
Table 1: Average LOO Zero-Shot performance across all subjects on DTU and SEED-VIG The best and second-best MSE and Pearson scores in bold, with the highest one surrounded by a box.
(a)
All Modeks
Best Model
0.15
0.2
Pearson R
0.10
0.1
Peurson
0.05
0.0
DTU
SEED-VIG
Model
MSE
Peurson
MSE
Pearson
Classic NN
Foundatio Foundation (9)
CTNer EEGPT ())
EEGPT (O)
0.999
0.039
0.055
0.452
DeepConvnet
4:0.220
10.033
20.33
+ 0.352
0.095
0.471
ERGNNet
+0.018
+0.029
00077
+ 0.296
(b)
All Models
Best Model
1.024
0.017
0.056
0.398
Conformer
+00028
+0.025
00060
0.3338
0,065
0,435
CTNet
+: 0.017
1 0.032
0.065
00052
1:0.313
0.360
Peunson R
0.5
Peanson
0.5
BIOT (I)
+00052
10.015
=00600
+0.342
BIOT (I)
+00019
1.000
+00018
=0,42
0,056
+0.283
0,440
0.0
0.0
1616
0,010
0,050
0.347
(E
10.055
10.021
0.037
030030
0,995
0,039
0,104
0.037
Classic NN Foundation (1)
Foundation (6)
U.EEGrameer 1 (1)
BEGNe
crmerr (f)
BENDR ())
10.018
+0,019
-0077
20.141
1.431
0.005
0,062
0,408
(BraMod())
10,024
00066
10336
0,996
0.039
2,951
0,232
(IraMod
10.018
10.024
-2877
1.0.662
0.994
0.043
0,051
0,413
Figure 5: LOO Zero-Shot regression perfor- mance on (a) DTU and (h) SEED-VIG. Left panels compare classic NN decoders, linear-probed foundation models, and fine-tuned foundation mod- els; right panels show the best model from each fam- ily. Group differences use the Mann-Whiney U
()GP)()
+00017
+0.032
= 0.040
+0.343
0.994
0.047
0,063
0,443
(EGPT()
100017
+ 0.033
-0005
-.0331
0.011
0,057
0.421
(1BraM()
10.05
10.013
0006
1:0.319
0,993
0,061
0,418
(aBrM
+00018
#0.021
=0.057
+0,305
1,237
0.010
0.055
0.441
test; paired top-model comparisons use the Wilcoxon
ST.FEGfomer.s
10.039
10.020
20061
10.370
signed-rank test (Bonferroni corrected, "*; p
0.993
0.016
0.064
0.372
ST-BBGfomee-ss)
0.019
+0.01
=0062
20.367
0.001).
instance, in ERN detection, all linear- probed foundation models perform relatively well, suggesting that the utility of pre-trained features may be highly task-dependent. These results imply that the effectiveness of pre-training can vary significantly across EEG paradigmss, potentially due to funda- mental differences in the underlying neural representations. Moreover, the strong performance of our simple yet oppperforming ST-EEGFormer model indicates that complex pre-training objectives may not yield substantial downstream benefits, especially when models are fine-tuned. Fine-tuning appears overwrite or adjust much of what learned during pre-training, thereby narrowing the performance gap between models with simple versus sophisticated pre-training tasks.
5.1 STRENGTHS AND LIMITATIONS OF EEG FOUNDATION MODELS
Data Sensitivity and Generalization Gaps Foundation models perform best when enough train- ing data is available, such as under population decoding. but their advantage diminishes in low-data settings like per-subject decoding. In these scenarios, classic neural networks (CTNet) remain com- petitive. Non-neural decoders can also achieve performance comparable to foundation models in certain evaluation protocols (e.g.. Per-Subject (Self), although they perform statistically worse than NN-based models in LOO Zero-Shot. Interestingly, while foundation models often achieve higher mean accuracy than classic NN models in LOO Zero-Shot and LOO Fine-Tune settings (Figure (d-e)), these differences are not statistically significant. These findings highlight the value of simpler baselines, which are frequently overlooked in current foundation model research.
On the Transferability of EEG Classification Models to Regression Our regression case study underscores limits to the transferability of EEG foundation model representations. On the con- ventional SEED- VIG dataset, fine-tuned foundation models are competitive and often rank highest overall (Figure G.12); however, under specific protocols-sute as LOO Zero-Shot (Table I clas- sie neural networks still produce the best results. In contrast, on the more challenging DTU auditory where foundation models have not previously been benchmarked clasiee decoders out-
8

---

Published as a conference paper at ICLR 2026
(a)
(b)
Log Fit (R-0.044)
Exp Fit (R--0603)
1.0
Training Time (H100)
0.0125
Normalized ACC
0.8
0.6
0.0075
0.4
0.0050
0.0025
0.2
10'
10"
10"
10"
10'
10"
10'
10'
10
10
10'
10'
Model Size (Parameters, log scale)
Model Size (Parameters, log scale)
Models
DespCoonvnet BBGNe:
Contormer CTNer
BROT BENDR
CieaMMdd
LıBraM
STEBEGRomeee
EEGPT
BEG
BBGlomer
Classic NN decoder
Foundation ())
Foundation
(a) Normalized accuracy versus total trainable parameters. For each dataset, accuracies are rescaled the dataset's best model equals 1. A pooled logarithmic fit a In(In x) + is applicd; the coefficient determination (R²) is reported above the panel. (b) Training time (S) per EEG epoch versus total trainable parameters, computed on a single NVIDIA H100 as Time per Batch/Bateed Size. An exponential fit a dr + is applied to the pooled data, with A shown above the panel. Each point denotes distinct model (unique marker); colors indicate the three model families.
Figure 6: Cross-model scaling trends in NN-based EEG decoders.
perform both fine-tuned and linear- procar foundation models. Taken together, these findings suggest that feature representations for regression not universally robust across tasks and protocols. Scaling Behavior and Task Dependence Figure 6 (a) indicates that a simple "bigger-is-bettr" rule does not generally hold across EEG decoders. Within a single architecture, however, we do observe conventional scaling: for ST-EEGFormer, the large variant outperforms the base and small variants (Figure 2p. When widen the comparison to all neural networks in the benchmark, large foundation models often deliver performance that is merely comparable to classic, smaller NN de- coders. We report this intentionally as a benchmark-level finding rather than tuning artifact: if large foundation model only matches a compact classic model, then within-family scaling gains have limited practical ealcc-esp given the substantial computational and training costs of large models. A primary factor is downstream data scarcity: most downstream BCI datasets include fewer than 50 subjects, which constrains the benefits of large foundation models. This echoes results in re- lated modalities, where an increasing number of training data leads to substantial performance gains for sEMG decoding (cf., Figure 2: over 40% error rate when training with less than 50 subjects, whereas below 10% error rate when scaling up the training subjects to more than 6,000) (Kaifosh) jct al., 2025). Task dependence further compounds the picture. Easier paradigms (e.g.. ERN) reach near-ceiling accuracy (se 99.9%) even with small models, leaving little headroom for scaling; harder paradigms (e.g., inner speech) show minimal improvement regardless model size. Collectively, these observations highlight fundamental limits in EEG decoding: foundation models can help. but they are not a universal remedy in data-scarce regimes. Progress will require not only architec- tural advances and task-aware objectives, but also substantially larger and more diverse datasets, alongside a deeper understanding of the theoretical and physiological limits of scalp EEG.
5.2 HIDDEN IMPLEMENTATION FACTORS
Foundation model performance depends not only on pre-training but also on downstream design and training choices that are often underreported.
Head Capacity under Linear Probing EEGPT and CBraMod employ multi-layer classification heads even in settings described as linear probing, effectively increasing capacity relative to ap- proaches that freeze the backbone and attach single linear layer. This likely contributes to EEGPT's strong linear-probing performance and illustrates how the term "linear probing" can conceal sub- stantial variation in head complexity. In Appendix H. show that more complex heads can significantly improve lincar-probing accuracy.
Token Fusion Strategy The way token features aggregated before the head is likewise critical. CBraMod feeds all tokens into its head: LaBraM supports either class-token or full-token fusion; ST-EEGFormer adopts ViT-style average-token fusion by default. These choices alter the effective receptive field and the information delivered to the head. As demonstrated in Appendix H..] simple fusion coupled with single linear layer typically underperforms more expressive heads (e.g., multi-

---

Published as a conference paper at ICLR 2026
layer full-token designs), even when backbone and data are held fixed. Additional overlooked factors are discussed in Appendix C.4
Takeaway Scemingly minor implementation details, such head depth/width. fusion scheme, can induce substantial performance gaps and complicate cross pross-model comparisons. We therefore recom- mend that future foundation model studies (i) explicitly specify head architecture and fusion strategy for every setting, and (ii) adopt a reporting checklist to standardize linear-probie mnoar-probing vs. fine-tuning protocols and ensure fair, reproducible benchmarks.
5.3 NERD FOR FAIR AND REPRODUCIBLE BENCHMARKING
Current evaluation practices often rely on selective downstream tasks and evaluation protocols, en- abling overly optimistic claims. Statistical testing is frequently absent or insufficiently emphasized. To truly assess progress, foundation models must be compared against strong classic baselines across diverse tasks and protocols, with rigorous significance testing.
5.4 CALL FOR COMMUNITY- WIDE COLLABORATION
The current landscape of EEG foundation models remains fragmented: models employ different pre- training strategies, evaluation protocols, and reporting practices, making direct comparisons difficult and limiting transparency. At the same time, the small size and limited diversity of downstream EEG datasets constraín large-scale benchmarking and analyses of generalization. To advance the field meaningfully, we call for coordinated community effort to: 1) Develop and share large-scale EEG datasets suitable for both pre-training and standardized evaluation. 2) Establish common evaluation protocols and strong, consistent baselines to enable fair and reproducible comparisons. Without such shared resources and benchmarking standards, progress EEG foundation models risks being incremental, domain-specific and unlikely generalize to new subjects, tasks, or datasets.
6 CONCLUSION
This study presents a comprchensive benchmark of EEG foundation models across diverse tasks, evaluation protocols, and model types. In addition, we introduce ST-EEGFormer, simple yet effective foundation model based on the Vision Transformer architecture and pre-trained solely us- ing masked autoencoding. Our results demonstrate that while foundation models can offer clear advantages in data-rich settings, they are not universally superior- in data-scarce sce- narios such as per-subject decoding. In such cases, classic neural and non-neural models remain strong contenders and should not be underestimated. Moreover, the generally poor performance of linear probing and the sensitivity to downstream implementation details highlight the need for greater transparency and standardization in evaluation. Moving forward, progress in EEG founda- tion modeling will depend on community-widd efforts establish large-scale datasets and adopt fair, statistically rigorous benchmarking practices.
ACKNOWLEDGMENTS
*L.Y. is supported by the Research Foundation - Flanders (FWO) grant 1S65622N.
*Q.S. supported by the China Scholarship Council (no. 202206050022).
A.L. is supported by the China Scholarship Council (no. 202404910389).
M.M.V.H.. supported by research grants received from Horizon Europe's Marie Sklodowska-
Curie Action (grant agreement No. 101 1 18964), Horizon 2020 research and innovation programme under grant agreement No. 857375, the special research fund of KU Leuven (C24/18/090) the Belgian Fund for Scientific Research Flanders (GOA4321N, GOC1522N, G031426N), and the Hereules Foundation (AKUL. 043).
*The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government.
REFERENCES
Samira Abnar and Willem H. Zuidema. Quantifying attention flow in transformers. CoRR, abs/2005.00925 000908 2020. URI /ttp:::///a/vv..o/ hataxxxv.org//bb//0550.0 20000000000228. 00928
10

---

Published as a conference paper at ICLR 2026
Ralph Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian Elger. Indications of nonlinear deterministic and finie-dimensiinn structures in time series of brain electrical activity: Dependence recording region and brain state. Physical review. E, Statistical, nonlinear, and soft matter physics, 64:061907, 01 2002. 061907.
doi: 10. 10.1103/hysReeE.644
Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, and Cuntai Guan. Filter bank common spatial pattern (FBCSP) in brain-computer interface. In 2008 IEEE International Joint Conference Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, June 2008.
Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli. wav2vec 2.0: A frame- work for self-supervised learning of speech representations. CoRR, abs/2006. 11477, 2020. URL
https: org/abs 2006 11477
Paul Dominick Baniqued, Emily C Stanyer, Muhammad Awais, Ali Alazmani, Andrew E Jackson, Mark A Mon-Williams, Faisal Mushtaq, and Raymond J Holt. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J. Neuroeng, Rehabil., 18(1):15, January 2021.
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. BETT: BERT pre-training of image transform- ers, 2022. URL httl./tpss//axxx..orrra/////////
08254
Alexandre Barachant. MEG decoding using riemannian geometry and unsupervised classification. Grenoble University: Grenoble, France, pp. 1-8, 2014.
Alexandre Barachant and Marco Congedo. A plug&play P300 BCI using information geometry, 2014. (Rtpstts:://axxii
Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten. Multiclass brain- computer interface classification by riemannian geometry. IEEE Trans. Biomed. Eng., 59(4): 920-928, April 2012a.
Alexandre Barachant, Stéphane Bonnet, Marco Congedo, and Christian Jutten. Multiclass brain- computer interface classification by riemannian geometry. IEEE Trans. Biomed. Eng., 59(4): 920- 929, April 2012b.
Lies Bollens, Bernd Accou, Hugo Van hamme, and Tom Francart. SparrKULee: A speech-evoked auditory response repository of the KU leuven, containing EEG of 85 participants, 2023.
Jingjing Chen, Xiaobin Wang, Chen Huang, Xin Hu, Xinke Shen, and Dan Zhang. A large finer- grained affective computing EEG dataset. Sci. Data, 10(1):740, October 2023a.
Jingjing Chen, Xiaobin Wang, Chen Huang, Xin Hu, Xinke Shen, and Dan Zhang. A large finer- grained affective computing EEG dataset. Sci. Data, 10(1):740, October 2023b.
Xiaogang Chen, Yijun Wang. Shangkai Gao, Tzyy-Ping Jung, and Xiaorong Gao. Filter bank canon- ical correlation analysis for implementing high-speed SSVEP-based brain-computer interface. J. Neural Eng., 1244)004,, August 2015.
Sylvain Chevallier, Guillaume Bao, Mayssa Hammami, Fabienne Marlats, Louis Mayaud, Djillali Annane, Frédéric Lofaso, and Eric Azabou. Brain-machine interface for mechanical ventilation using caspiratory-related evoked potential. In Artificial Neural Networks and Machine Learning ICANN 2018, Lecture notes in computer science, pp. 662-671. Springer International Publishing, Cham, 2018.
Sylvain Chevallier, Igor Carrara, Bruno Aristimunha, Pierre Guetschel, Sara Sedlar, Bruna Lopes, Sebastien Velut, Salim Khazem, and Thomas Moreau. The largest EEG- BCI reproducbbib study for open science: the MOABB benchmark, 2024. URL. 2404 15319
Hohyun Cho, Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, and Sung Chan Jun. EEG datasets for motor imagery brain-computer interface. Gigascience, 6(7):1-8, July 2017.
11

---

Published as a conference paper at ICLR 2026
Junhyuk Choi, Keun Tae Kim, Ji Hyeok Jeong, Lachyun Kim, Song Joo Lee, and Hyungmin Kim. Developing a motor imagery-based real- time asynchronous hybrid BCI controller for a lower-limb exoskeleton. Sensors (Basel), 20(24):7309, December 2020.
Kyuwan Choi and Andrzej Cichocki. Control of wheelchair by motor imagery in real time. In Lecture Notes in Computer Science, Lecture notes in computer science, pp. 330-337. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. ELECTRA: Pre- training text encoders as discriminators rather than generators, 2020. URL https://arxiv
hxc/abbs20033 10555
Marco Congedo, Alexandre Barachant, and Rajendra Bhatia. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain Comput. Interfaces (Abingdon), 4(3); 155-174, July 2017.
Marie-Constane Corsi, Sylvain Chevallier, Fabrizio De Vico Fallani, and Florian Yger. Functional connectivity ensemble method enhance BCI performance (FUCONE). IEEE Trans. Biomed. Eng., 69(9):282--2838 September 2022.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hier- archical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, June 2009.
Yang Deng, Zhiwei Ji, Yijun Wang, and S Kevin Zhou, OS-SSVEP: one-shot SSVEP classification. Neural Networks, 180: 106734, 2024.
Yi Ding, Neethu Robinson, Chengxuan Tong, Qiuhao Zeng, and Cuntai Guan. LGGNet: Learning from local-global-graph representations for brain-computer interface, 2022. URL. https://
stxiv.org/bbs... 2215 02786
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszko- reit, and Neil Houlsby. An image worth 16x 16 words: Transformers for image recognition scale, 2021. URL https: / arxiv. org abs//ab///// 2200000000000 10010.1990 11929
Søren A Fuglsang, Daniel D E Wong, and Jens Hjortkjær. EEG and audio dataset for auditory attention decoding, 2018
Wendong Ge, Jin Jing, Sungtae An, Aline Herlopian, Marcus Ng. Aaron F Struck, Brian Ap- pavu, Emily L Johnson, Gamaleldin Osman, Hiba A Haider, Ioannis Karakis, Jennifer A Kim, Jonathan Halford, Monica B Dhakar, Rani A Sarkis, Christa B Swisher, Sarah Schmitt, Jong Woo Lee, Mohammad Tabacizadeh, Andres Rodriguez, Nicolas Gaspard, Emily Gilmore, Susan Herman, Peter W Kaplan, Jay Pathmanathan, Shenda Hong, Eric S Rosenthal, Sahar Zafar, Jimeng Sun, and M Brandon Westover. Deep active learning for interictal ictal injury continuum EEG patterns. J. Neurosci. Methods, 351(108966): March 2021.
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolutional sequence sequence learning, 2017. URL httpt: http:://axxvvvvvo wwwp://wwrgg.////o/// http///105.011133
/
Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Yee Whye Teh and Mike Titterington (eds.), Proceedings of the Thirteenth Interna- tional Conference on Artificial Intelligence and Statistics, volume 9 of Proceedings of Machine Learning Research, pp. 249 256, Chia Laguna Resort, Sardinia, Italy, 13-15 May 2010. PMLR.
Ary Goldberger, Luís Amaral, Leon Glass, Jeffrey Hausdorff, Plamen Ivanov. Roger Mark, Joseph Mietus, George Moody, Chung-Kan Peng, and H. Stanley. Physiobank, physiotoolkit, and phy- sionet: : Components
new research resource for complex physiologic signals. Circulation, 101:E215-20, 07 2000. doi: 10.1161/01.C1 C.0...IR.100 101.111.....0 23..21
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, An- drew Tulloch, Yangqing Jia, and Kaiming He. Accurate large minibatch SGD: Training ImageNet in 1 hour, 2018. httpstss::: http:///wwxwv..o.//aa/////////s/
12

---

Published as a conference paper at ICLR 2026
Osman Berke Guney, Muhtasham Oblokulov, and Huseyin Ozkan. A deep neural network for SSVEP-based brain-computer interfaces. IEEE Trans. Biomed. Eng. 69(2):932-944 February 2022.
Daeun Gwon, Kyungho Won, Minseok Song, Chang S Nam, Sung Chan Jun, and Minkyu Ahn. Review of public motor imagery and execution datasets in brain-computer interfaces. Front. Hum. Neurosci, 17:1 134869, March 2023.
Amir Harati, M Golmohammadi, Sebas Lopez, Obeid, and Joseph Picone. Improved EEG event classification using differential energy. In 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1-4. IEEE, December 2015.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2022.
Yongtian He, Trieu Phat Luu, Kevin Nathan, Sho Nakagome, and Jose Contreras- Vidaal.. mobile brain-body imaging dataset recorded during treadmill walking with brain-computer interface. Sci. Data, 5(I): 180074, April 2018.
Holzner, C Guger, C Grönegress, G Edlinger, and M Slater. Using P300 brain computer interface for smart home control. In IFMBE Proceedings, IFMBE proceedings, pp. 174-177. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger. Deep networks with stochas- tic depth, 2016. URL pnttps://aaziiv.orrr/bbbbs/ 09382
Ji-Hoon Jeong, Jeong-Hyun Cho, Young-Eun Lee, Seo-Hyun Lee, Gi-Hwan Shin, Young-Seok Kweon, José Del R Millán, Klaus-Robert Müller, and Gsong-Whan Lee. 2020 international brain- computer interface competition: A review. Front. Hum. Neurosci., 16:898300, July 2022.
Wei-Bang Jiang, Li-Ming Zhao, and Bao-Liang Lu. Large brain model for learning generic rep- resentations with tremendous EEG data in BCI, 2024. URL https::/arxiv..rg/ abs//
2405. 18765
Patrick Kaifosh, Thomas Reardon, and CTRL-labs at Reality Labs. A generic non-invasive neu- romotor interface for human-computer interaction. Nature, July 2025.
Murat Kaya, Mustafa Kemal Binli, Erkan Ozbay, Hilmi Yanar, and Yuriy Mishchenko. A large electroencephallgraahii motor imagery dataset for electroencephalograppic brain computer in- terfaces, October 2018.
Bob Kemp, Aeilko Zwinderman, Bert Tuk, Hilbert Kamphuisen, and Josefien Oberyé. Analysis of sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the eeg. Biomedical Engineeringg IEEE Transactions on, 47:1 185 1194, 10 2000. doi: 10.1 109/10.867928.
Sirvan Khalighi, Teresa Sousa, José Moutinho Santos, and Urbano Nunes. ISRUC-Sleep: A compre- hensive public dataset for sleep researchers. Comput. Methods Programs Biomed., 124:180-19 February 2016.
Dominik Klepl, Min Wu, and Fei He. Graph neural network-based EEG classification: A survey, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32:493-500 503, 2024.
Demetres Kostas, Stephane Minca-Ouellette, and Frank Rudzicz. Bendr: using transformers and contrastive self-supervised learning task to learn from massive amounts of eeg data, 2021. URL
https://arrxvv.rrtt www.:/rgg//aoo http://www....b///....00000 12037
Niklas Kueper, Kartik Chari, Judith Bütefür, Julia Habenicht, Tobias Rossol, Su Kyoung Kim, Marc Tabie, Frank Kirchner, and Elsa Andrea Kirchner. EEG and EMG dataset for the detection of errors introduced by active orthosis device. Front. Hum. Neurosci., 18:1304311, January 2024.
Vernon J Lawhern, Amelia Solon. Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance. EEGNet: a compact convolutional neural network for based brain- computer interfaces. J. Neural Eng., 15(5)0056013, October 2018.
13

---

Published as a conference paper at ICLR 2026
Yao Li, Jiayi Xiang, and Thenkurussi Kesavadas. Convolutional correlation analysis for enhanc- ing the performance of SSVEP-based brain-computer interface. JEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(12)22681228 1-990, 2020.
Wenzhe Liao, Jiahao Li, Xuesong Zhang, and Chen Li. Motor imagery brain-computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke pa- tients: A clinical study. Front. Hum. Neurosci,, 17:1 1 17670, March 2023.
Bingchuan Liu, Xiaoshan Huang, Yijun Wang, Xiaogang Chen, and Xiaorong Gao. BETA: A large BEnchmark database toward SSVEP-BCI application. Front. Neurosci, 14:627, June 2020.
Wei Liu, Jie-Lin Qiu, Wei-Longg Zheng, and Bao-Liang Lu. Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition. IEEE Trans. Cogn. Dev. Syst. 14(2):715-729, June 2022.
Sebas Lopez, G Suarez, D Jungreis, Obeid, and Joseph Picone. Automated identification of abnor- mal adult eegs. In 2015 IEEE signal processing in medicine and biology symposium (SPMB), pp- 1-5. IEEE, 2015.
Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts, 2017.
URL httts:://aaxiiii org/ abs/1608 03983
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. URL https:
Jun Ma, Banghua Yang, Wenzheng Qiu, Yunzhe Li, Shouwei Gao, and Xinxing Xia. A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface. Sci. Data, 9(1):531, September 2022.
Perrin Margaux, Maby Emmanuel, Daligault Sébastien, Bertrand Olivier, and Mattout Jérémie. Ob- jective and subjective evaluation of online error correction during P300-based spelling. Adv. Hum.-Comput Interact., 2012:1-13, 2012.
Usman Masud, Muhammad Iram Baig. Faraz Akram, and Tae-Seong Kim. P300 brain computer interface based intelligent home control system using random forest classifier. In 20/7 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, November 2017.
Andreas Miltiadous, Katerina D Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Nikolaos Grigoriadis, Dimitrios Tsalikakis, Pantelis Angelidis, Markos Tsipouras, Euripidis Glavas, Nikolaos Giannakeas, and Alexandros T Tzallas. A dataset of scalp EEG recordings of alzheimer's disease, frontotemporal dementia and healthy subjects from routine EEG. Data (Basel), 8(6):95, May 2023.
Jianliang Min, Ping Wang, and Jianfeng Hu. Driver fatigue detection through multiple entropy fusion analysis in EEG-based system. PLoS One, 12(12):00188756 December 2017.
Wajid Mumtaz. MDD patients and healthy controls EEG data (new), November 2016.
Saydul Akbar Murad and Nick Rahimi. Unveiling thoughts: A review of advancements in EEG brain signal decoding into text. IEEE Trans. Cogn. Dew. Syst., 17(1):61-76, February 2025.
Masaki Nakanishi, Yijun Wang, Xiaogang Chen, Yu-Te Wang, Xiaorong Gao, and Tzyy-Ping Jung. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component anal- January 2018a.
ysis. IEEE Trans. Biomed. Eng., 65(1):104-113
Masaki Nakanishi, Yijun Wang, Xiaogang Chen, Yu- Te Wang, Xiaorong Gao, and Tzyy-Ping Jung. Enhancing detection of SSVEPs for high-speed brain speller using task-related component anal- ysis. JEEE Trans. Biomed. Eng., 65(1):104-113 January 2018b.
Nicolás Nieto, Victoria Peterson, Hugo Leonardo Rufiner, Juan Esteban Kamienkowski, and Ruben Spies. Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition. Sci. Data, 9(1):52 February 2022.
14

---

Published as a conference paper at ICLR 2026
Iyad Obeid and Joseph Picone. The temple university hospital EEG data corpus. Front. Neurosci., 10:196, May 2016.
Patrick Ofner, Andreas Schwarz, Joana Pereira, and Gernot R Müller-Putz. Upper limb movements can be decoded from the time-domain of fow-frequency EEG. PLoS One, 12(8);e0182577, August 2017.
Herbert Ramoser, Johannes Müller-Gerking, and Gert Pfurtscheller. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng., 8(4):441-446, December 2000.
Bertrand Rivet, Antoine Souloumiac, Virginie Attina, and Guillaume Gibert. xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Trans. Biomed. Eng., 2668):2035-2043, August 2009.
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, and Tonio Ball. Deep learning with convolutional neural networks for EEG decoding and visualiza- November 2017.
tion. Hum. Brain Mapp., 38311):(333)))) 38(11::3311-5420
S R Sockadar, M Witkowski, C Gómez, E Opisso. J Medina, M Cortese, M Cempini, M C Carrozza, L. Cohen, N Birbaumer, and N Vitiello. Hybrid BEG//OG-basee brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia. Sci. Robot., 1(1 (e))aaag3299, December 2016.
Yonghao Song, Qingging Zheng, Bingchuan Liu, and Xiaorong Gao. EEG Conformer: Convolu- tional transformer for EEG decoding and visualization. IEEE Trans. Neural Syst. Rehabil. Eng., 31:710-719, February 2023.
James R Stieger, Stephen A Engel, and Bin He. Continuous sensorimotor rhythm based brain com- puter interface learning in large population. Sci. Data, 8(1):98, April 2021.
Yike Sun, Liyan Liang, Yuhan Li, Xiaogang Chen, and Xiaorong Gao. Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface. Gigascience, 13, January 2024.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Re- thinking the inception architecture for computer vision, 2015. URL https://arxiv..rrt
abs/15 00567
Michael Tangermann, Klaus-Robers Müller, Ad Aertsen, Niels Birbaumer, Christoph Braun, Clemens Brunner, Robert Leeb, Carsten Mehring, Kai Miller, Gernot R Müller-Putz, Guido Nolte, Gert Pfurtscheller, Hubert Preissl, Gerwin Schalk, Alois Schlögl, Carmen Vidaurre, Stephan Waldert, and Benjamin Blankertz. Review of the BCI competition IV. Front. Neurosci., 6:55, July 2012.
Ly V Tran, Hieu M Tran, Tuan M Le, Tri T M Huynh, Hung T Tran, and Son V Dao. Application of machine learning in epileptic seizure detection. Diagnostics (Basel), 12(11):287) November 2022.
Chun Sing Louis Tsui, John Q Gan, and Huosheng Hu. A self-paced motor imagery based brain- computer interface for robotic wheelchair control. Clin. EEG Neurosci., 42(():225-22 22,,,, October 2011.
Guagnyu Wang, Wenchao Liu, Yuhong He, Cong Xu, Lin Ma, and Haifeng Li. EEGPT: Pre- trained transformer for universal and reliable representation of EEG signals. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, Tomezak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 19249-39280 Curran Associates, Inc., 2024. URL. httpps://rocceeeeeeeeee ngs neuri ps ec htwaaperrfflleepppp///0/000 Ei le 454 0d2 0ee44402222eecccceeebbbbbb
880883000333333 ort.a/00000003-pppe-----eeeeeee pd
Jiquan Wang, Sha Zhao, Zhiling Luo, Yangxuan Zhou, Haiteng Jiang, Shijian Li. Tao Li, and Gang Pan. CBraMod: A criss-cross brain foundation model for EEG decoding, 2025. URL hetps:
15

---

Published as a conference paper at ICLR 2026
Benjamin Wittevrongel and Mare M Van Hulle. Spatiotemporal beamforming: A transparent and unified decoding approach to synchronous visual brain-computer interfacing. Frontiers in neuro- science, 11:630, 2017.
Kyungho Won, Moonyoung Kwon, Minkyu Ahn, and Sung Chan Jun. EEG dataset for RSVP and P300 speller brain-computer interfaces. Sci. Data, 9(1):388, July 2022.
Xiaolin Xiao, Minpeng Xu, Jing Jin, Yijun Wang, Tzyy-Ping Jung, and Dong Ming. Discrimina- tive canonical pattern matching for single-trial classification of ERP components. IEEE Trans. Biomed. Eng., 22666222662 August 2020.
Xiao Xing, Yijun Wang. Weihua Pei, Xuhong Guo, Zhiduo Liu, Fei Wang, Gege Ming, Hongze Zhao, Qiang Gui, and Hongda Chen. A high-speed SSVEP-based BCI using dry EEG electrodes. Sci. Rep., 8(1): 14708, October 2018.
Zheng Yan, Xiaorong Gao, and Shangkai Gao. Right-and-left visual field stimulation: frequency and space mixed coding method for SSVEP based brain-computer interface. Sci. China Inf. Sci., 54(11):2492- 2498, December 2011.
Chaoqi Yang. M. Brandon Westover, and Jimeng Sun. BIOT: Cross-data biosignal learning in the wild, 2023. URLpttts:: http:://aaxvvvvvo batttp:://aaadvoorrrbb//////// 2305 10351
Liuyin Yang and Mare M Van Hulle. Real-time navigation in google street view@ using a motor imagery-based BCI. Sensors (Basel), 23(3), February 2023.
Liuyin Yang, Bob Van Dyck, and Marc M Van Hulle. Sea-wave: Speech envelope reconstruction from auditory EEG with an adapted WaveNet. IEEE Open J. Signal Process., 5:686 699666999660 2024.
Sun Yike, Liang Liyan, Li Yuhan, Chen Xiaogang, and Gao Xiaorong. Supporting data for "dual- alpha: A large EEG study for dual-frrquency SSVEP brain-computer interface", 2024..
Guo-Qiang Zhang, Licong Cui, Remo Mueller, Shiqiang Tao, Matthew Kim, Michael Rueschman, Sara Mariani, Daniel Mobley, and Susan Redline. The national sleep research resource: towards sleep data commons. J. Am. Med. Inform. Assoc,, 25(10))1351-1358 October 2018
Xinyi Zhang, Shuang Qiu, Yukun Zhang, Kangning Wang, Yijun Wang, and Huiguang He. Bidi- rectional siamese correlation analysis method for enhancing the detection of SSVEPs. Journal of Neural Engineering. 1944:0440277, 2022.
Wei Zhao, Xiaolu Jiang, Baocan Zhang, Shixiao Xiao, and Sujun Weng. CTNe:: a convolutional transformer network for EEG- motor imagery classification. Sci. Rep., 14(1):20237, August 2024.
Wei-Long Zheng and Bao-Liang Lu. A multimodal approach to estimating vigilance using EEG and forehead EOG. Journal of Neural Engineering, 14(2):026017, 2017. URL http::/staccks 0mgpor111 2-1-2225/5222/2/222
Peixiang Zhong, Di Wang, and Chunyan Miao. BEG-based emotion recognition using regularized graph neural networks. IEEE Transactions on Affective Computing. 13(3):1290-133 2020.
Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Maria Chernykh, and Oleksii Shpenkov. Electroencephalograms during mental arithmetic task performance. Data (Basel), (1):14, January 2019.
16

---

Published as a conference paper at ICLR 2026
APPENDIX
A CLARIFICATION ON THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, Large Language Models (LLMs) (GPT-4o and GPT-5) were used as auxiliary tools to support writing and implementation. For manuscript preparation, LLMs were used for grammar checking and text refinement only. A typical prompt example is: "Refine the following paragraph to improve clarity and conciseness while preserving technical meaning."
For experiments, LLMs were occasionally consulted to assist in coding tasks such as generating specific function using Python. Importantly, all code produced with LLM assistance was carefully reviewed, validated, and tested by the authors to ensure correctness. Thus, the scientific contribu- tions, experimental design, and data analyses presented in this work are entirely the responsibility of the authors.
B REPRODUCIBILITY
We've open-sourced the project on GitHub, with detailed documentation and usage examples to help the community adopt The release includes:
Pre-training framework: source code and pre-trained model weights for the proposed ST-EEGFormer.
Downstream benchmarks: training and evaluation code for both foundation models and classic neural networks.
implementations of non-neural methods (e.g., FBCSP, TRCA, CCA, Riemannian classifiers) used in this benchmark.
Classical baselines:
Utilities: example SLURM scripts for job submission, reflecting our HPC-based experi- mental setup, to help others reproduce large-scale training efficiently.
C RELATED WORK
C. EEG FOUNDATION MODELS
EEG foundation models have attracted significant attention as they provide generalizable EEG rep- resentations transferable across various tasks and datasets. BENDR (Kostas et (202) among the earliest to apply masked pre-training and contrastive learning to EEG data, utilizing a transformer backbone to learn from large-scale unlabeled datasets. Building upon this approach, BIOT (Yang et al. (023) and LaBraM (Jiang et al. 2024) adopted similar transformer architectures combined with extensive masked pre-training strategies. Specifically, BIOT introduced flexible channel kenization to enhance cross-dataset transferability, while LaBraM proposed "neural tokens" and argued that straightforward masked autoencoding on raw EEG data failed to converge effectively, thereby encouraging further exploration into more sophisticated pre-training objectives instead of a simple raw-signal reconstruction task. More recently, CBraMod (Wang et al. 2025) advanced LaBraM's paradigm by employing criss-cross transformer architecture alongside local and global masked reconstruction losses, significantly enhancing the transferability of EEG feature represen- tations. Similarly, EEGPT (Wang et al. 2024) adds spatio-temporal representation alignment, structing a self-supervised task on EEG representations with high SNR and rich semantic infor- mation instead of raw signals. A detailed comparison of these foundation models is presented in Appendix Table C.I
Despite methodological differences. most existing EEG foundation models share the underlying assumption that large-scale, self-supervised pre-training leads to better generalization in downstream tasks. However, these models are rarely evaluated against traditional non-neural baselines and are typically tested using limited evaluation protocols- -ucc as population decoding leave-one-out
Pre-trained model weights and the corresponding implementation are publicly available at: https://githul com/ wiw.liyyynnnnnn
17

---

Published as a conference paper at ICLR 2026
zero-shot decoding. As result, prior studies often report isolated figh-perfomming numbers without statistical verification, leaving the true advantages of foundation models largely unsubstantiated.
C.2 CLASSIC NEURAL NETWORK MODELS
Parallel to the advancements in foundation models, the BCI community has actively developed compact neural architectures specifically tailored for EEG decoding. Early deep-leariigg models such as DeepConvNNet (Schirrmeister et al., 2017) and EEGNet (Lawhern et al.) 2018) demon- strated that convolutional architectures relatively low complexity. More recently, hybrid designs like EEG Conformer (Song et al., (203)) and CTNet (Zhao et al., -hich combine convolutional modules with Transformer-basee at- tention-bave achieved state-of-the-art performance on diverse tasks while maintaining significantly lower computational demands than foundation models. Due to the inherent coupling EEG signals with the specific tasks performed by participants, many existing neural network architectures are tailored to incorporate explicit task-specific priors (or knowledge). For instance, tasks involv- ing emotion, fatigue, or Alzheimer's disease etc., where cross-brain-region information transfer is prevalent, graph-based architectures have proven effective in modeling this underlying functional connectivity (Zhong et al. 2020; Ding et al. 20222 Klepl et al. 2024). Conversely, for SSVEP detection, where the brain response to the stimulation is relatively well-defined, stimulation priors incorporated into the neural network architecture design, leading improved decoding perfor- mance (Li et al., 20200] Zhang et al., [202]] Deng et al., (024). These developments highlight that well-designed compact models capture rich EEG representations across a variety of BCI tasks, often matching or exceeding the performance of larger models while remaining more efficient and
effectively capture spatio-temporal EEG features with
interpretable.
C.3 CLASSIC NON- NON-NEURAL NETWORK MODELS
Classic machine learning models- not based on neural networks-renet the most widely used for EEG decoding, though they are often overlooked in recent foundation model research. Ac- cording recent comprehensive study on EEG decoder reproducibility, classic approaches often exhibit superior performance, even outperforming neural networks on various BCI tasks (Cheval- lier et al., 2024). For instance, Common Spatial Patterns (CSP) (Ramoser et al., 2000), filter bank CSP (FBCSP) (Ang et al., 2008), and Riemannian geometry- boaser classifiers (Congedo et al.) 2017 continue to demonstrate competitiveness various motor imagery BCI applications. In the context of SSVEP decoding, traditional methods such as filter bank Canonical Correlation Analy- sis (FBCCA) (Chen et al. 2015). Task-Related Component Analysis (TRCA) (Nakanishi et al.) 2018b), and Spatiotemporal Beamforming (Wittevrongel & Van Hulle 2017) remain dominant due to their robustness and high efficiency with limited training data. For event-related potential (ERP) classification, xDAWN is widely used pre-processing step for improving signal-to-noise ratio (Rivet et al. 2009). Additionally, classic machine learning techniques employing carefully designed EEG features and explainable classifiers such Linear Discriminant Analysis (LDA), Random For- est (RF), and Support Vector Machines (SVM) are still popular in scenarios characterized by limited data availability and a preference for interpretabilliyy such as Alzheimer's diagnosis (Miltiadous et al. 2023).
C.4 METHODOLOGICAL GAPS AND IMPLEMENTATION ISSUES IN PRIOR EEG FOUNDATION MODELS
While Table outlines the key methodological differences across EEG foundation models, sev- eral important implementation details have been largely overlooked in prior work. For instance, as discussed in Section (522, the choice of token fusion strategy can substantially affect downstream performance, yet it is rarely examined systematically. During our benchmarking, careful review of publicly available code revealed additional overlooked factors and inconsistencies in implemen- tation, which may partly explain discrepancies in reported results. We summarize and discuss these issues below to highlight the importance of transparent and reproducible evaluation practices.
Beyond the differences summarized in Table C.I several methodological details warrant emphasis. EEGPT employs an encoder that restricts attention to tokens within the same time step, treating tokens from different time steps independent. This design prevents the model from explicitly
18

---

Published as a conference paper at ICLR 2026
capturing temporal dependencies. Furthermore, EEGPT discards all patch tokens after the encoder, relying solely on the [CLS] token for both pre-training and downstream tasks. In downstream ap- plications, an additional convolutional layer is inserted before the encoder to remap input channels, which may inadvertently diminish the role of channel embeddings.
For CBraMod, positional information is introduced through ACPE embeddings, constructed via two-dimensional convolutions over the spatio-temporal neighborhood of each patch. However, the model was pre-trained exclusively on fixed 19-channel dataset. Consequently, when applied to datasets with different channel configurations, it requires fine-tuning to learn new channel embed- dings and representations. This limitation reflected in their reported results; as shown Table of the original paper, the downstream performance differences between the pre-trained model and the model trained directly on downstream tasks without pre-training are very marginal, suggesting limited transferability of the learned representations.
BIOT, in contrast, adopts bipolar montage during pre-training rather than standard single-channel montage. As result, downstream datasets must either be remapped into consistent bipolar mon- tage or supplemented with a convolutional layer before the encoder to automatically learn this map- ping.
Finally, we also identified potential implementation issues in the official EEGPT codebase that were not documented in the paper. Specifically, the authors employed the torcheeg library extract EEG segments from datasets such as TSU, M3CV, and SEED. By default, this library generates I-second epochs, which were subsequently interpolated to match the longer durations reported in the paper (e.g., 4 for TSU and 10 for SEED).
C.5 GAPS AND MOTIVATION
Despite recent advances, current EEG foundation model studies remain limited in scope. Most fo- cus narrowly on tasks like leave-one-ous zero-shot decoding, often overlooking real-world scenarios such as per-subject evaluation and omitting comparisons with classical non-neural methods. Sta- tistical testing is rarely performed, and regression tasks are largely unexplored. To address these gaps, we present a comprehensive, statistically rigorous benchmark across diverse classification and regression tasks, comparing foundation models against both classical and compact neural decoders. Notably, we show that top performance can achieved with a simple MAE-pre -ae--re--rained trained model, challenging the need for overly complex pre-training strategies.
19

---

Published as a conference paper at ICLR 2026
Table C.I: Comparison of large-scale EEG foundation models. Columns detail: pre-training datasets, pre- trairriinng method, total model size, downstream datasets, task type, downstream optimization strategy, evaluation protocol coverage (using our six ex--imension taxonomy), and baseline/bette hte//bnnchaarkr comparison models.
Comparion Models
mod.
olk. HENDR BROT, LaBraM
Classic NN models BJOT, LaBraM
Classic NN models
BIOT
Population decoding, 0,0 zeno- soot
Population decoding zaro stat
Popelation deswiing, zono-alott
Population decodingg, LOO zero zero-slort slot
Exalration protoods
Opt-
pooking cnioderry,
small imns former
Dovnstream
Fine tiinng. probing
Fine- Fine-tunine tuniug
miration
Linar
diazen
for
Fine-
Laak
(UAP
sleep
Cettuut, ahuar-
mal EBG. emocse))
Abuot-
mal EEG. emnotoon
Classiictionn (eaoo
ton. ML event
ERP. setcure
Downstreum
Clusiliction
Class
Carreiiatioo
Chosiiiatii
abncenal.
Regresion ditectiio)
Type
stuge)
mal
Loper 0t a 2013
2015
[Earan CI 2015
Lis et d. 202
2013
1-ye 3 31
[Haral B0rS BCICAY
301 ERN (Magaa at
201 PiveeNer (Coll
barget di aL ZHIM Stepp EDRT 12 diseet EAC
Chhail
Ct all 20230 SEED
SHL N021
al
Imaanno
speet Tomn of mL 20022
600 SEED-VIO Min
W. 2010
201
Dowastream Daisets
BUTEERE
Trun
Foat et aL.
CHE-MIY
TUEV
Loper
ManIeCA 3016
2007
TUEY Farat
CHR- MTT
PlymoONN
MI NA
2002
TUAB
TUEV
I'UE
SUID
Molil
2013
CE al
ISRUC
HEAB
OF
(base (hase/urge hurge/uggggu
Modi Saze
7M/25M
(log (hoy//agg Barge)
4M
dra--
pralition
beiwern original and
(haurir spectrmm pred.)
maaked token token pradicion (Tran sermee))
Dudl selfouperiiall gatie-kampeaa alggenai mask coomstraction
Patch- bach-b masted re- construction: mis-enes wanforme sopporal (ppatial
branch (iimlloeo) or
pre tre-traiiig Method
son contattiv
(war (oar (imppii)
pottrbed bokens
Tokentation
coder: ACME
nalipach
contastive
pre trainingg Datuxet Stze
Ubeid Pieme
subject.
EEG BFG seggmnns
propectrry EBI cameeds -2.53
Dbei & P. (oone 2010 119967 sab-
et (0US) M rottigg (PREST))
~ 1.31 al ras EBG
puhlic triale (eggeessss)
22,002
slopp
< seep
Tates
TURAI
CBrMMed (2005)
BITT (2023)
BENDR
Model
20

---

Published as a conference paper at ICLR 2026
D BENCHMARK DATASETS
In this section, we introduce all downstream benchmark datasets used in this study and their pre- processing and data-split strategies.
D.1 BENCHMARKED DOWNSTREAM DATASETS
D.1.1 BCI COMPETITION IV-2A (4-CLASS MOTOR IMAGERY)
The BCT Competition IV-2a dataset [Tangermann et al. 2012) is a widely used benchmark for motor imagery (MI) classification. It contains EEG recordings from 9 subjects performing four distinct imagined movements: left hand, right hand, both feet, and tongue. Each subject participated two sessions on separate days, each consisting of 288 trials (576 trials in total per subject). EEG was recorded from 22 electrodes (international 10-20 system) at 250 Hz, with each trial comprising a 4-second MI period following a visual cue. In the original competition design, the first session designated the training set and the second as the test set. This dataset's well-controlled protocol and multi-class setting classes) have made a canonical benchmark for MI decoding. our study, we strictly follow the original competition split: models are trained on the first session and evaluated on the second.
1.1.2 UPPER-LIME MOTOR EXECUTION/IMAGERYY DATASET (7-CLASS MOTOR EXECUTION/MAAERY)
The Upper-Limb Motor Execution/Imagery dataset (Ofner 2017) contains EEG recordings from 15 healthy subjects performing both executed and imagined upper-limb movements. Each subject completed two sessions on separate days: one with actual motor execution (ME) and one with motor imagery (MI) of the same tasks. In both conditions, subjects performed six distinct sustained movements of the right arm (elbow flexion/extension, forearm supination/pronation and hand open/close), plus rest condition, yielding seven classes in total. Tasks were visually cued, and subjects either executed per trial. Each session comprised 10 runs of 42 trials, resulting in 60 trials per class (420 trials per session). EEG was recorded from 61 electrodes (motor coverage) at 512 Hz. This dataset enables multi-class decoding across overt and imagined movements, while also supporting analysis of execution-imagen differences. In this study, we apply a 5-fold cross-validation strategy within each subject and modality.
movement (ME) or vividly imagined it (MI) for several seconds
D.1.3 INNER SPEECH EEG DATASET (4-CLASS INNER SPEECH)
The Thinking Out Loud dataset (Nieto al.) 2022) is an open-access benchmark for inner speech classification. It contains EEG recordings from 10 native Spanish speakers instructed to silently imagine saying four command words ("arriba, abajo, izquierda, derecha" meaning up, down, left, right). For comparison, the same participants also performed overt speech (speaking the words aloud) and a visual imagery control task, though here we focus only on the inner speech condition. Each subject completed multiple runs across three sessions, yielding about 200 trials per word for in- ner speech. EEG was acquired using a 136-channel system (128 scalp electrodes plus 8 EOG/EMG channels) at 1024 Hz. During inner speech trials, participants were asked to repeatedly imagine pronouncing the target word in their own voice while avoiding overt articulation. This dataset pro- vides a four-class classification challenge in the inner speech paradigm, offering benchmark for developing BCIs aimed at natural, speech-based communication. In this study, we adopt a 5-fold cross-validation strategy within subjects to train and evaluate models.
1.1.4 BRROR-RELATED EEG DATASET (2-CLASS ERN CLASSIFICATION)
The Error-Related EEG Dataset (Kueper et al., 2024) captures brain responses to unexpected movement errors during human-robot interaction. Eight subjects wore active robotic orthosis on the right arm, which guided elbow flexion/extension movements. In ~20% of trials, the orthosis briefly (250 ms) moved in the opposite direction before returning to the correct trajectory. inducing a detectable error. Subjects remained passive but reported errors by squeezing a ball with the left hand. Each subject performed 10 runs of 30 trials (15 flexion, 15 extension), with 6 error trials
21

---

Published as a conference paper at ICLR 2026
per run, resulting in ~300 total trials and 60 enror events per subject. EEG was recorded from 64 scalp channels (extended 10-20 montage, Brain Products LiveAmp) at 500 Hz, along with EMG channels arm muscles. Impedances were kept below 5 kΩ2 The dataset is designed to study Error-Related Potentials (ErrPs), providing a binary classification task (error vs. correct movement). In this study, we use 1-s EEG epochs following the error onset and adopt the same train-test split strategy as the original work for evaluation.
D.1.5 BINOCULAR DUAL- FREUUEREE SSVEP DATASET (40-CLASS BINOCULAR SSVEP)
The Binocular SSVEP Dataset Yike et al. 2024) introduces a novel paradigm where distinct flick- ering stimuli are presented separately to the left and right eyes using a polarized light system. The benchmarked subset corresponds to the binocular-swer swap experiment, involving 35 healthy subjects. A total of 40 visual targets were defined by different binocular frequency combinations. In each trial, participants fixated single target for 2 s, with each target repeated times. EEG was recorded with a 64-channel Neuroscan Quik-Cap, following the international 10-20 electrode placement sys- tem. This dataset provides large-scale multi-class SSVEP benchmark under binocular stimulation. In this study, we adopt 5-fold cross- voss-validation strategy, ensuring that held-out test trials are never used during training avoid data leakage.
1.1.6 ALZHEIMER'S ' DIAGNOSIS BEG DATASET (3-CLASS CLASSIFICATION]
The Alzheimer's EEG Dataset (Miltiadous et al. 2023) provides resting-state EEG recordings for studying Alzheimer's disease (AD) and Frontotemporal dementia (FTD). It includes 88 elderly subjects: 36 with probable AD, 23 with FTD, and 29 cognitively healthy age- matched controls. EEGs were collected during routine clinical assessments, with each recording consisting of 12-14 minutes of eyes-closed resting-state activity. Signals were acquired from 19 scalp electrodes (10-20 system) 500 Hz and are shared in BIDS format with preprocessing and accompanying metadata, including Mini-Mental State Exam (MMSE) scores. This dataset supports both binary (c.g., AD vs. Control) and multi-class classification (AD vs. FTD vs. Control), offering a valuable resource for developing machine learning models for early dementia diagnosis from non-invasive EEG. In this study, we adopt a saav--one-subeecte split, reflecting the clinical goal of diagnosing an unseen patient.
D.1.7 FACED (9-CLASS EMOTION RECOGNITION)
The Finer-grained Affective Computing EEG Dataset (FACED) (Chen al. 2023a large- scale benchmark for multi-class EEG-based emotion recognition. It contains EEG recordings from 123 healthy subjects who watched 28 emotion-eliciting video clips spanning nine discrete emotion categories:
amusement, inspiration, joy, tenderness; anger, fear, disgust, sadness; and neutral. After each clip, participants provided self-reported ratings on eight target emotions plus valence, arousal, liking, and familiarity. EEG was recorded from 32 electrodes (10-20 system) at 250 or 1000 Hz and subsequently standardized to 250 Hz; for each trial, the last 30 s of the video were retained and pre- processed, and both raw and feature-level (DE/PSD) representations are released. In this study, we treat FACED as a 9-class emotion recognition benchmark and adopt conventional cross-subject zero-shot protocol the processed 250H 2 data, where models trained on 80% of subjects and evaluated on the remaining 20% test subjects.
D.1.8 TUH BEG EVENTS CORPUS (TUEV, 6-CLASS EEG EVENTS)
The TUH EEG Events Corpus (TUEV) (Obeid & Picone 2016) is a clinically collected bench- mark derived from the Temple University Hospital EEG (TUEG) database. consists of short EEG segments extracted from routine clinical recordings and labeled as one of six event types: spike and sharp wave (SPSW), generalized periodic epileptiform discharges (GPED), periodic lat- eralized epileptiform discharges (PLED), eye movement (EYEM), artifact (ARTF), and background (BCKG). In common benchmark settings, THE EEG signals are recorded at 250H with 23 chan- nels. The corpus has become standard dataset for automatic detection of epileptic discharges and general EEG event classification in clinical environments. In this study, we formulate TUEV as a 6-class EEG event classification task and follow the conventional cross-subject protocol provided
22

---

Published as a conference paper at ICLR 2026
Table D.I: :: Summary of benchmarked EEG datasets used this study.
Datanel
Chowne/ Torpe
REG Ch.
(Bbx)
Trial Duc
Trtals Treals/Sut Subj.
Taok
cleee haad for wmgge)
576
Moier trugery
Upper MEMM
dkasss mpweeees r000
Mouse Exec-linagery
3004
~220heerd
Speech
Immestated (fiek)
2.cloaae (ome comet)
905
spech
100
Finor Mariisting
Biraster SeVEP
40 segeo, (hirustbbr tnog comboo)
64
250
2a
200 sooiic
SSVEP
Alahimer TTTICI
IIC)
900
min
Dagnods
conoiens)
23
Brosion Recogattion
TUEV (Evcats)
classes (SPSW. GPED. PLED,
ARTE, BCKG)
250
vuriillee
BBG Detctioon mudd (n-30 mim) -730
bru Cudcaal Paaty
Begrosim oopach execlope)
64
512
500
Andney Alteztionn
SEEDS VIG
23
Regiooss (Niglance lavel)
17
200
5
Vigiance
by the dataset, training on the official training partition and evaluating on the held-out evaluation partition without subject overlap.
D.1.9 AUDITORY ATTENTION (DTU "COCKTAIL PARTY" DATASET, REGRESSION)
The DTU "Cocktail Party" Dataset (Fuglsang et al. 2018) is benchmark for auditory attention decoding (AAD), formulated here as regression task. It contains EEG recordings from 18 subjects listening to continuous speech in dual-speaker setting, In each ~50 trial, two concurrent speech streams (one male, one female, presented from different spatial locations) were played, and subjects were instructed to attend to one speaker while ignoring the other. Some baseline trials featured only a single speaker. EEG was recorded at 512 Hz with a 64-channel BioSemi system (plus EOG), and the speech waveforms of both speakers were simultaneously recorded and temporally aligned with the EEG. Each subject contributed ~30 min of data across six trials with varying attention conditions.
Unlike categorical BCI datasets, this dataset provides a continuous regression target: the temporal envelope of the attended speech. Models are evaluated by reconstructing the attended envelope from EEG and comparing it against the true attended vs. unattended audio streams. This paradigm captures realistic neural tracking of continuous stimuli and assesses a model's ability to decode selective attention in naturalistic listening environments. In this study, we regress EEG to the attended auditory envelope, using the first 80% of each subject's recording for training and the remaining 20% for testing.
D.1.10 SEED-VIG (VIGILANCE ESTIMATION)
The SEED-VIG dataset (Zheng & Lu 2017 multimodal benchmark for driver vigilance esti- mation. It was collected from 23 participants performing ~~2 h sustained simulated driving task on a monotonous four-lane highway designed to induce fatigue. EEG and forehead EOG were recorded with a Neuroscan system; for EEG, 17 scalp electrodes were placed according to the international 10-20 system, and signals were downsampled to 200 Hz. Continuous vigilance labels in the range [0, were derived from PERCLOS (percentage of eyelid closure). SEED-VES has become stan- dard benchmark for EEG-based drowsiness and vigilance estimation in automotive safety research. In this study, use SEED-VIG regression benchmark.
To ensure a comprehensive evaluation of EEG decoders, we benchmark across datasets spanning broad range of paradigms, including motor imagery/execution, inner speech, error monitoring, visual (SSVEP), auditory attention, and clinical diagnosis. This diversity captures both laboratory and real-world BCI scenarios, testing models under varying cognitive tasks, electrode montages, and recording conditions. A summary of all benchmarked datasets is provided in Table D.I D.I
D.2 DATA PRE-PRRCESSING
We apply minimal and standardized pre-processing across all benchmarked datasets to ensure com- parability while preserving raw signal characteristics. EEG signals are band-pass filtered between 0.1-128 Hz and notch filtered at the power-line frequency using the mne. filter module. All datasets are downsampled to baseline rate of 256 Hz, chosen to align with EEGPT (which natively operates at 256 Hz). For foundation models requiring lower sampling rates, additional resampling is applied at data-fetch time using mne. res amppe, followed by their A summary of preprocessing steps is provided in Table [.2] Note that for the Binocular SSVEP dataset, recordings originally sampled at 250 Hz. are kept 250 Hz, only being upsampled to 256 Hz.
normalization schemes.
23

---

Published as a conference paper at ICLR 2026
Table D.2: Default preprocessing pipeline applied across all datasets.
Step
Description
Band-pass filtering
0.1-128 Hz using mne filter
Notch filtering
Power-line frequency (50/60 Hz) using mne. filter 256 Hz (baseline rate)
Downsampling Resampling
Mlode--ppciffic rate via mne resample
Table E.1: Details ST-EEG-MAE variants, all with an EEG segment (patch) size of samples and mask ratio of 0.75.
Encoder layers
Encoder embed size
Encoder ML.P size
Encoder heads
Decoder layers
Decoder embed size
Decoder ML. size
Decoder heads
Model
Params
small
512
2048
384
1536
32.7M
base
12
768
3072
12
8
512
2048
16
110.9M
large
24
1024
4096
16
8
512
2048
16
328.4M
when training EEGPT. Additionally, the Binocular SSVEP and DTU datasets undergo task-specific segmentation, described below.
To increase the number of training examples and to evaluate asynchronous classification, we segment each 2-s trial into overlapping windows. sliding window of s with 0.1 step size is used, yielding 11 segments each fold, 1760 samples are used for training and 440
Binocular SSVEP
trial. With 40 targets and repetitions each, this
results in 11 40 x = 2200 samples. for testing. The first segment of each trial (stimulus onset) is considered synchronous trial (40 in total), while the remaining 400 segments serve as asynchronous trials.
DTU (Auditory Attention) For regression benchmarking, adopt simple formulation: given a 1-s EEG segment, the model predicts one sample of the attended speech envelope. Recordings segmented into 3-s windows with a 0.I-s step size. Within each window, the model is trained to reconstruct the envelope using the abovementioned 1-s sliding window approach. The training objective combines mean squared error (MSE) and Pearson correlation loss between the predicted and true envelopes.
E ST-EEGFOMER
In this section, we introduce the proposed ST-EEGFormer, including its model architecture, pre- training task, implementation details, and results.
E. MODEL ARCHITECTURE
The proposed ST-EEGFomer is based the ViT architecture (Dosovitskiy al., (021) 1, pre-trained using MAE He et al.) 2022). The pre-training task involves reconstructing the original EEG inputs from masked tokens, as illustrated in Figure (b). During pre-training. raw EEG signals are divided into spatial and temporal segments, which are tokenized through a linear projection layer. Each token augmented with temporal and spatial positional embeddings preserve structural information. A random masking strategy is applied, with 75% of tokens hidden, and the encoder processes only the remaining visible tokens. To enable reconstruction, the masked tokens tokens-together together with their positional embeddings are concatenated with the encoder output to form the full token sequence, which then passed through lightweight decoder to reconstruct the original EEG signal. After pre-training, only the encoder is retained and used as the ST-EEGFormer backbone for fine-tuning on downstream tasks. Three different ST-EEGFormers (small, base, and large models) were pre- trained in this study. The base and large models have the same architecture the base, large models proposed in the ViT implementation, while the corresponding decoders have the same architecture in the MAE implementation. Details about the benchmarked model can be found in table F.1
24

---

Published as a conference paper at ICLR 2026
E.2 PRE-TRAINING DATASETS
The MAE reconstruction task conducted on 11 public datasets. These datasets include: 1) EEG-MI-BCI (Cho el al. 2017): This dataset contains 52 subjects performing 2-class imagined left and right-hand movements, recorded with 64 EEG channels using a Biosemi ActiveTwwo system. It includes approximately 5,000 trials of 3-second motor imagery (MI) data per class.
2) HGD (Schirrmeister et al. 2017): The High Gamma Dataset comprises 20 subjects performing second trials of executed movements with four classes (left hand, right hand, both feet, and rest). The data were recorded with 128 high-density EEG caps (WaveGuard Original, ANT, Enschede, kHz using a NeurOne amplifier (Mega Electronics Ltd, Kuopio, FI).
NL) and sampled
includes roughly 3,000 trials per class.
3) BCI-Comp-IV2a (Tangermann al. y012; This dataset includes nine subjects performing four-second trials of four classes (imagined left hand, right hand, feet, and tongue movements), recorded with 22-electrode EEG caps. It contains training set and test set from two separate sessions, each with roughly 600 trials per class.
4) BCI-Comp-IV2b [Tangermann el al. 2012): This dataset consists nine subjects performing 2-class imagined left and right-hand movements, recorded with three EEG channels. It contains training set of approximately 1,800 trials per class and separate test set of approximately 1400 trials per class.
5) Large-MI-Claste (Kaya et af., 2018): This dataset comprises subjects performing 1-second trials of six classes (imagined left hand, right hand, left foot, right foot, tongue, and rest). The data were recorded with 19-channel EEG caps plus 2 ground lead channels (Flectro-Cap International, USA) and were mostly sampled at 200 Hz, with some recordings sampled at 1000 Hz using the EEG-1200 system. total, includes approximately 50,000 trials (different classes have an unequal number of trials).
6) Large-MI-5F (Kaya et al. 2018): From the same study as 5) but different experiments, this dataset comprises 13 subjects performing 1-second trials of five classes of finger movements (imagined thumb, index, middle, ring, pinkie). In total, it includes around 18000 trials.
7) P300 Won al. 2022): This dataset consists of 55 participants performing P300 speller experiment and 50 participants viewing a rapid serial visual representation (RSVP). In total, includes 99000 training P300 trials and 277200 test trials.
8) SSVEP (Liu et al. 2020): This dataset consists of 70 participants performing cue-guided SSVEP target-selecting experiments, comprising 40 flickering stimuli ranging between 8 Hz to 15.8 Hz with interval of 0.2 Hz. For each target, it contains 20 trials of 5-s stimulation data.
9) Online MI BCI Classification (tteeerr et al. 2027): This dataset contains 600 hours of 62-channel EEG recordings, sampled at 1000 Hz. collected during online and continuous BCI control from 62 healthy adults, spanning multiple sessions across different days. The BCI paradigm involves imagining left, right, and both hand movements (opening and closing), as well as a resting state condition, control virtual cursor. The provided data consists of epoched trials of varying lengths, structured with 2-second inter-trial interval, followed by 2-second target presentation. The task imagination phase varies in length, with maximum duration of up to 6.04 seconds, followed by I-second post-trial interval.
10) KUL Auditory Decoding Dataset (Bllens al. 2023 This dataset consists of 64-channel EEG recordings from 85 young participants, each exposed to 90-150 minutes continuous natural speech. Data were acquired with a BioSemi 64-channel system at sampling rate of 1024 Hz,
25

---

Published as a conference paper at ICLR 2026
providing large-scale resource for auditory attention and speech decoding research.
11) SEED-V Emotion EEG Dataset (Liu et al. 2022): This dataset provides multimodal EEG and eye- eye-tracking data for emotion recognition. It includes recordings from 16 subjects who participated in three sessions, each watching 15 movie clips spanning five emotional categories: happy, sad, fear, disgust, and neutral (3 clips per emotion per session, totaling 45 trials per subject). EEG was acquired using a 62-channel NeuroScan system (10-20 layout) 1000 Hz (typically downsampled to 200 Hz), while eye movement was captured with SMI tracking glasses. Each trial includes stim- ulus presentation followed by
period. SEED-V V stands out as a rich resource for emotion decoding from EEG-ey eyG multimodal data, offering both raw signals and precomputed differential entropy features across standard frequency bands.
The dataset selection followed two main criteria: (i) size and quality, as highlighted in recent review (Gwon al. 2003). and (ii) benchmark relevance. Specifically, the MI datasets were chosen for their robustness and widespread use, with Datasets 3 and 4 serving as classic benchmarks in motor imagery research. The P300 dataset was included as it represents one the largest publicly available collections for event-related potential decoding, Similarly, the SSVEP dataset was selected due to its established role as a standard benchmark in visual BCI studies. Finally, the auditory decoding dataset was incorporated given its scale and its unique position the largest EEG resource in the auditory domain. In addition, we integrated an in-house EEG dataset covering multiple paradigms (e.g., MI and SSVEP) with diverse channel configurations. This enriched the model's exposure to a wide range of electrode montages, ultimately enabling it to learn from 142 unique EEG channels and improving its adaptability future datasets.
E.3 DATA PREPROCESSING
All datasets underwent minimal preprocessing to ensure comparability while preserving raw signal characteristics. Specifically, power-line noise was removed when present using the mne filter. notch_ () function (Python 3.8.19. MNE 1...1). Next, a band-pass filter between 0.1-64 Hz was applied to all channels via mne. filter. filter.data () with a win- dowed FIR design (fir design= 'firwin'), ). The signals were then downsampled to 128 Hz using mne. fi filter. resample. e () and finally standardized zero mean and unit variance per channel.
E.4 DATA SEGMENTATIONS FOR TRA-INNG
The benchmark datasets differ in whether they provide continuous EEG recordings or only task- related epochs. To unify pre-training data construction, dataset-specific sliding-windt window strategies were applied:
1) For continuous datasets (e.g., EEG-MI-BOL (Cho et al. 2017), HGD (Schirrmesster et al. 2017), BCI-Com BCI-Comp-IV22 (Tangermann et al. 2012), BCi-Comp- IV (Tangermann al. 2012), P300 (Won et al. 2022). SEED-V (Liu et al. 2022), and KUI. Auditory (Bollens et al.) 2023)), 6-s windows with 0.5-s hops were used.
2) For large MI datasets (Large-MIl MI-Classic (Kaya et al., 2018), Large-MI-5F (Kaya et al. 2018), Online MI BCI (Stieger et al.) 2021 a 6-s window with a 2.5-s hop was applied to reduce redun- dancy.
3) For the SSVEP dataset (Liu et al. 2020). which consists only of 5-s stimulation epochs, 2-s windows with 0.125-s hops were extracted.
4) We also included EEG data recorded our own lab to enrich electrode coverage. By combining heterogeneous datasets, the encoder was exposed to 142 unique EEG channels, improving robust- ness for transfer to unseen datasets.
A validation split was retained for each dataset: for BCI-Comp-IV' IV2a, the official test used, while for others, 20% of the data was held out. Overall, this yielded more than 8 million overlap- ping FEG segments.
26

---

Published as a conference paper at ICLR 2026
E.5 MAE PRE-TRAINING METHODOLOGY
The pre-training procedure follows the original MAE framework. EEG data are first divided into apatial-tempore segments, which are linearly projected into embeddings with added spatial (SPE) and temporal positional encoding (TPE). For the spatial positional embeddings. a learned embedding per channel was used, similar to the learned positional emhedding in (Gehring et al. 2017). while for the temporal positional embeddings, a sine-cosine positional embedding approach was used, as shown in Eq EEI A fixed ratio of 75% of tokens is randomly masked, and the remaining tokens are passed through a ViT-based encoder. Mask tokens with their positional embeddings are then concatenated with the encoder output and processed by a lightweight decoder. The decoder recon- structs the original EEG, and the objective is the mean squared error (MSE) between reconstructed and original signals over the masked segments.
pt = f(t) = sin(wk t), if = 2h:
where Wk
(E.1)
cos(wr (), if = 2k +
10000 26
E.6 MAE PRE-TRAINING SETTINGS
The model is initialized with xavi e .uuunniieee form (Glorot & Bengio 20TO). Optimization follows Adam W (Loshchilov & Hutter 2019) with a base learning rate of 3e-4, weight decay of 0.05, batch size of 256, cosine learning rate decay (Loshchilov Hutter |utter]2017] 2017]. and a 10-epoch warmup (Goyal
et al. 2018). The learning rate scales linearly with batch size according to Eq. E.2
Ir base_rr batch size
(E.2)
256
E.7 PRACTICAL CONSIDERATION FOR MAE PRE-TRAININ
We conducted pre-training on a regfooerformccc computing (HPC) cluster equipped with NVIDIA A100 GPUs (80 GB). Before the production run, benchmark experiments were performed to evaluate scaling efficiency and estimate wall-time under different job sizes. Each benchmark ran for one epoch across the full dataset, and the average epoch wall-time was extrapolated to 400 epochs, consistent with the original MAE setting.
Figure E. summarizes these benchmarks. The blue line shows relative efficiency compared single-GPU baseline (green dashed line), while the red dashed line indicates estimated wall-time in days. Panels (a-d) correspond to different harel-hardware configurations: (a) small model on 40 GB A100, (b) small model on 80 GB A100, (c) base model on 80 GB A100, and (d) large model on 80 GB A100. Efficiency was defined as:
Efficiency Braseline Thaseline
Beurrent Teurrent
where B is the number of CPU cores and T the wall-time.
We ultimately selected the 16x A100-80GB configuration, which offered the best trade-off between efficiency (Efficiency >> 50%) and throughput. The benchmarks highlight that larger GPU mem- ory enables larger batch sizes, reducing per-epoch training time. However, they also illustrate the extreme resource demands of large-scale EEG pre-training: even with optimized scaling, our pro- duction run consumed 32,614 GPU hours. This underscores both the cost of developing EEG foundation models and the importance of transparent reporting of their computational footprint.
E.8 MAE PRE-TRAINING RESULTS
The pre-training learning curves for the small, base, and large models are shown in Figure E.2 As expected, the large model achieves the lowest reconstruction loss, followed by the base model, and the small model yields the highest loss. The small and base models exhibit smooth and stable convergence, whereas the large model shows some instability during the early epochs before even- tually converging. Overall, all model variants successfully converge, which directly contrasts with
27

---

Published as a conference paper at ICLR 2026
Daseline
Baseline
Chosen jobsize
1.0
1.0
112.36
Total Wall Time (dlays)
100
0.8
100
Efficiency
0.6
0.8
40
0.4
12
32
Number of ERUS
Number of GPUS
(c)
(d)
Baseline
Chosen jobsize
Baseline
Chosen jobsize
Total Wall Time (days)
Total Wall Time (days)
2.26
250
400
0.9
200
Efficiency
0.-
302
0.8
0.77
150
200
0.7
0.6
-0
54.1
100
0.5
0.4
12
32
36
32
Number GPUS
GPUs
Figure E.1: GPU scaling benchmark for MAE pre-training. The blue line shows efficiency relative the single-GPU baseline (green dashed line), while the red dashed line indicates estimated total wall-time in days for 400 epochs. Panels: (a) small model on A A100-40GB, (b) small model on A A100- 80GB, (c) base model on A100-80GB, and (d) large model on A100-80GB The chosen production configuration is indicated by the orange dashed line.
Small Train
0.9
Base Train
0.8
0.7
Large Train
0.6
Small Valid
0.5
Base Valid
MSE loss
0.4
Large Valid
0.35
0.3
0.25
0.2
0.18
0.15
0.1
100
200
300
Epoch
Figure E.2: Learning curves of the small, base and large ST-EEG MAE models during the MAE pertaining phase.
the claim made in LaBraM (Jiang et al. 2024) that masked autoencoding is difficult to train raw EEG signals.
Some examples of the reconstructed signals compared to the original ones presented in fig- ures E3 and E.4
is noteworthy that the model was able to effectively reconstruct the low- frequency trends, though it encountered difficulties in accurately reconstructing figh-freeuency spikes. This could be attributed to the lower signal-to-noise ratio (SNR) of high-frequency EEG components, making them more challenging to learn.
28

---

Published as a conference paper at ICLR 2026
Original
MAE-large
Original
MAE-small
MAE-bune
MAE-large
BCI Comp Iv2a
Large MI Classic
Figure E.3: Random samples from the BCI-IV-2A (the first 4 columns), Large-MI- MI-Clasii (the last 4 columns). For each example (4 rows), the following are shown, from left to right: the original sig- nals with masked segments highlighted in white and unmasked segments in grey; the reconstructed signals in red produced by the MAE-Small model, overlaid on the original signals in white; the reconstructed signals in red produced by the MAE-Bas Base model, overlaid on the original signals in white; and the reconstructed signals in red produced by the MAE-Large model, overlaid on the orig- inal signals in white. The corresponding mean squared error (MSE) loss is displayed at the bottom of each figure.
E.9 ADDITIONAL MODEL VALIDATION ON TRAINING DATASETS
After MAE pre-training and before the large-scale benchmarks described in Appendix D we con- ducted intermediate evaluations to assess the effectiveness of the pre-trained ST-EEGFormer. Popu- lation decoding was performed on four motor imagery/cxecution datasets (EEG-MI-BCI (Cho et al.) 2017. HGD (Schirrmeister
2017p. Large-MI- MI-Classic (Kaya et al. 2018), and Large-MI M 5F (Kaya al. (018)), one P300 dataset (Won et al. (002)), ONE SSVEP dataset (Liu et al.) 2020) and single-channel seizure classification dataset (Andrzejak et al. 2002). The seizure dataset not included in the pre-training corpora. ST-EEGFormer was compared against representative baselines, including EEGNet, EEG Conformer, BIOT, and LaBraM. Additionally, we implemented a simple linear model (Table E.2), consisting of spatial filter, feature extractor, and fully connected layer without nonlinear activations. This model serves as a minimal yet informative baseline, with the extracted feature summarized in Table E.3
29

---

Published as a conference paper at ICLR 2026
Original
MAE-small
MAE-base
MAE-large
Original
MAE-small
MAE: -base
MAR-large
BBG BCI
SSVEP
Figure E.4: Random samples from the EEG-MI-BCI (the first columns), and SSVEP datasets (the last 4 columns). For each example (4 rows), the following are shown, from left to right: the original signals with masked segments highlighted reconstructed signals in red produced by the MAE-Small model, overlaid on the original signals in white; the reconstructed signals in red produced by the MAE- Base model, overlaid on the original signals in white; and the reconstructed signals in red produced by the MAE-Large model, overlaid on the original signals white. The corresponding mean squared enror (MSE) loss is displayed at the bottom of each figure.
white and unmasked segments in grey; the
E.9.1 EXPERIMENT DETAILS
For all MI datasets, we employed 5-fold cross-validation strategy, using the Strati feraaFfiee function from sklearn. model select ion to ensure class balance within each fold. This approach was applied individually each recording. During the 5-fold cross-validation, folds are used the current training set, and the remaining set is the test set for this fold:
1) Training and Validation Split: For each fold, 20% of the training data was set aside as a validation
set, used for model selection.
2) Model Selection: The model achieving the highest classification accuracy on this validation set
was chosen the best model during different training epochs.
3) Testing: The selected model was then evaluated on the test set of the current fold.
30

---

Published as a conference paper at ICLR 2026
Table E.2: Linear model architecture. Input EEG data consist of Neh channels and L time samples. The output corresponds to Nelass: representing the number of different classes to classify.
Layer
Name
Type
Layer specific settings
Output shape
0
Input
NA
NA
(L)
1
Spatial filter
Conv
kernel size: Nec., 1)
(8xL)
number of kernels:8
2
Drop
Dropout
p=0.2
(8xL)
3
Feature extractor
NA
see table E.3
(8 x 12)
4
Flatten
NA
NA
(1 x 96)
5
Classification head
Linear
weights and bias shape: (96, Nelass)
Netaes)
Table E.3: Features calculated in the feature extractor layer.
Feature
Definition
Remark
total number of samples. the i-th sumple. T: mean value NA
Mean
E
Variance
2 >
x)2
Power
I
Ei 29
Skewness
Ai (n-1)-03
(it, (2)
a:: the standard deviation
Kurtosis
K Em (24=2)
NA
256: the number of intensity bins p(æx): the probability of the i-th intensity bin
Entropy
H E p(2,) log (p(x.) +6)
10 9; a small constant for stability
Maximum
max (x) max 22
NA
Minimum
min(x] min
NA
The first quartile
Q: Quantile(x, 0.22)
NA
The secoond quartile
2 Quantile(x.0.50)
NA
The third quartile
23
Quantile
NA
ZCR
Li=a [sgn(z;) sg))
1, if >
Zero cross rate
NA
sgn(2)
0,
if 0
-1,
if <
Additionally, for the HGD (Schirrmeister et al. 2017) dataset, separate hidden test was avail- able. This hidden test set was used as additional test set, and the models selected from the cross- validation step were further evaluated on this set assess their performance comprehensively.
For the SSVEP Liu et al. 2020) dataset, we followed the approach outlined the SSVEP DNN paper (Guney et al. 2022). using a sliding window method generate training samples of 1-second and 2-second lengths, with a hop size of 0.1 seconds. The test set also contains small segments of EEG data generated using the same sliding window on the hidden test trial data. We employed the same leave-one- seive-one-sessoo-ouee validation strategy for the experiment, the model selection process was consistent with that used in the MI experiments.
in (Guney et al. 2022). and
For the P300 (Won et al., 2022) dataset, we utilized the provided training and test sets. As in other P300 decoding experiments, we evaluated the model's performance under varying numbers of trial averaging. These trials were averaged based on the flashing of rows and columns during the experiment.
Remark that the training data in each fold were kept the same when training different models.
E.9.2 MODEL IMPLEMENTATION AND TRAINING DETAILS
This intermediate benchmark was designed as a validation step; thus, only a subset of models was evaluated. The main objective to examine the token fusion strategy in ST-EEGFormer. Follow- ing the ViT paradigm, we compared two variants: using the class token (Cls) or the average of all tokens (Avg) input the final classification layer. Training followed the general settings in Ap-
31

---

Published as a conference paper at ICLR 2026
pendix F.1.2 except that used larger batch size (128) and a higher base learning rate (3 x 10 to accelerate convergence.
E.9.3 INTERMEDIATE BENCIMARK RESULTS
Movement-related datasets The benchmark results on all movement-related datasets are sum- marized in Table E.4 Overall, the pre-trained ST-EEGFormer consistently achieved the highest classification accuracies across multiple BCI datasets. Among the model variants, the large version outperformed both the base and small models on most datasets. For all movement-related bench- marks, the fine-tuned base models yielded higher accuracies than their linearly probed counterparts, although the latter performed similarly to supervised linear models trained from scratch. Further- more, the mean accuracies obtained with subject-specifie linear models reported in Figure 3 Gwon ct al. (2023) (approximately 60%) were comparable to those achieved by our population-trainet lin- models. These findings highlight the efffcctivnness of our proposed approach: self-supervised pre-training on large-scale EEG recordings enables the foundation model to learn robust neural rep- resentations, yielding performance competitive with, superior to, traditional linear classifiers.
P300 The benchmark results on the P300 dataset are shown in Figure E.5 Model performance was evaluated under different numbers of row-column trial averages, a standard strategy in P300 decoding. Multiple runs were conducted with increasing repetitions, and the results are reported as performance curves. Training and test sets followed the official dataset split. Trial averaging was performed by aggregating EEG responses across repeated flashes of the same rows columns, thereby enhancing the signal-to-noise ratio. As expected, performance consistently improved with the number of repetitions. Notably, BIOT underperformed even the simple linear baseline, while the best results were achieved by ST-EEGFormer-I, followed by EEGNet, EEG Conformer and LaBraM showed comparable performance, ranking below the top models.
SSVEP For the SSVEP dataset, we benchmarked asynchronous decoding performance using the small segmented windows generated by the aliding-window method. The results are shown in Ta- ble E.5 The pest-performing model was ST-EEGFormer-l, followed by LaBraM. As expected, performance improved when longer window lengths were used, reflecting the benefit of increased temporal context. In contrast, the linear model performed poorly, achieving less than 10% accuracy in the 40-target classification task.
Seizure classification Additionally, we tested our approach on a single-channel seizure classifi- cation task using the famous Bonn dataset (Andrzejak et al., 2002). This dataset consists of hu- man expert-selected single-channel EEG data from five healthy volunteers and five individuals with epilepsy. The data are divided into two classes for healthy volunteers, including scalp EEG seg- ments recorded while the volunteers were relaxed and awake with eyes closed and open, respec- tively (Dataset A and B, referred to as "Eyes Closed" and "Eyes Open" in figure E.6 (b). Three classes of data are from epileptic patients, consisting of intracranial EEG (iEEG) segments recorded during pre-surgical evaluation. Specifically, one class contains interictal iEEG segments from the epileptogenic zone in the opposite hemisphere (dataset C, referred to "NSeizure- Opeossite in figure [F.6] (b), while another class includes interictal iEEG segments from the epileptogenic zone itself (dataset D, referred to
in figure (b). The final class consists of iEEG segments recorded from the epileptogenic zone during seizure activity (dataset E, referred as "Seizure" in figure E.6 (b). Each subset contains 100 single-channel EEG segments, each 23.6 seconds in duration (4096 samples). The data were sampled at 173.61 Hz, and any artifacts caused by muscle activity or eye movement were manually removed by the database owners after visual inspection.
DuSeizure-EEpilppooee
The hypothesis that if the model learns robust EEG representations from normal EEG-BCI record- ings during the pre-training step, it should be able to classify abnormal EEG data as well. There- fore, in the first experiment, we varied the amount of learning examples from only 5% to 60% and compared the classification accuracies among different models. In this experiment, we tested the performance of 1) directly applying linear probing on the pre-trained model; 2) directly fine-tuning the pre- pre-trained model; 3) further calibrating the model by performing the MAE task, followed by linear probing on the seizure dataset, and 4) further calibrating the model by performing the MAË task and then fine-tuning on the seizure dataset. This was done determine which approach yields
32

---

Published as a conference paper at ICLR 2026
Table E.4: Movement datasets benchmark results (reversed). "-ev" represents the average k-fold cross-validation accuracy. while "-test" represents the average accuracy on the hidden test set. The highest and second-hige highestt accuracies are in bold, with the highest one marked in bold and sur- rounded by box. For ST-EEGFormer, the default fine-tuning strategy is end-to-end fine-tuning with the average token, "Ip" denotes linear probed model, and "cls" refers to an end-to-end fine- tuned model using the class token.
EEG-MI- BCI-ev
Large-MI-- Classic-cv
Model
HGD-cv
HGD-test
Large-MI-5F-cv
Lincar
0.683
0.631=0.011
0.593=#0.021
0.442 = 0.009
0.320 = 0.015
EBGNet
0.781 0.0011
0.890
0.859 10.003
0.644 + 0.004
0.479 + 0.006
Conformer
0.821 +00012
0.914
0.003
0.800+000100
0.722 + 0.004
0.529 0.004
BIOT
0.028
0.651
0.005
0.612=0.015
0.455 0.012
0.287 = 0.008
LaBraM
0.736 0.0010
0.89210.000
0,902 0.040
0.763 0.005
0.464 0.023
ST-EEGFormer-s
0.000
0.858
0..01
0.763 0.008
0.500 0.008
ST-EEGFormer-h
0,937 1 0.005
0.874
0.000
0.754 + 0.006
0.483 + 0.010
ST-EEGFFormer-b.Ipp
0..01
0..09110.014 0.600
0.439 + 0.004
0.294 0.008
ST-FFGFormer--els
0,936 + 0,010
0.817+0.007
0.731 + 0.004
0.462 + 0.003
ST-EIEGForerr-I
0,931 + 0.005
0.954 + 0.004
0.935 + 0.002
0.831 = 0.003
0.627 + 0.013
the best performance. The results are presented in figure E.6 (a). The confusion matrix of the base model is shown in figure E.6 (b). Moreover, also checked the effects of the mask ratio in the MAE pre-training step by varying the mask ratio and comparing the finetuned model and linear probing model performance under different mask ratios with only 5% training data. The results are presented in figure E.6 (c). Figure E.6 (a) demonstrates that all pre-trained ST-EEGFormer models outperformed both EEGNet and Conformer, particularly when training data were limited. Moreover, performance could be further improved by calibration, as the highest accuracy was ob- tained by the ST-EEGFormer base-cali model. In contrast to results from previous datasets, where linear-probed models significantly underperformed finetuned models, the linear-probed models in this study achieved satisfactory results, especially after calibration, surpassing other models. This success can be attributed not only to the robust EEG representations learned during the MAE pre- training stage that help classify abnormal EEG data but also to the relatively straightforward classifi- cation task, which exhibits distinguishable characteristics that are easily visually inspected, making linear probing more effective. These findings provide a solid foundation for the future application of ST-EEGFormer in seizure classification, as the model could potentially learn even better repre- sentations from large open public seizure datasets not included in this study.
Summary
The above benchmark experiments yield the following insights:
1) Effectiveness of SSL pre-training: Both the benchmarks pre-training datasets and the cal- ibration experiment on the seizure dataset demonstrate that self-supervised pre-training improves downstream task performance. This provides strong evidence for the utility of large EEG founda- tion models. However, the performance gap between calibrated and non-calibrated models suggests that certain useful representations not fully captured during pre- pre-training. This may be attributed to the limited availability of seizure-related data in pre-training or to representation shifts between the pre-training and downstream tasks.
2) Weak linear probing performance: Across all movement-related datasets, linear-probed ST- EEGFormer performed poorly, comparable
the simple linear baseline. This indicates that the representations learned during pre- pre-training do not transfer effectively to downstream classification tasks, even when the same data were part of the pre-training corpus.
3) Inferior class-token fusion: In all experiments, using the class token for classification yielded worse results than averaging over all tokens. This suggests that the class token did not play a mean- ingful role during pre-training. Based on this finding, we adopt the average-token fusion strategy exclusively in all downstream benchmark experiments.
F MODEL IMPLEMENTATION DETAILS
In this section, we present all benchmarked models and implementation details used across the downstream tasks.
33

---

Published as a conference paper at ICLR 2026
Top-1 row-column selection accuracy
Top-2 row-column selection accuracy
0.97
Accuracy
6.5
Linear
EEGNa
Conformer
0.6
0.90
LaBraM
0.50
BIOT
0.82
ST-EBGromera
0.8
ST-EBGFumme-h
0.9
ST- EBGFeeeerr
0.6
0.4
0.3
0.5
0.8
Number trial repetitions
Figure E.5: P300 benchmark results. Row-column selection accuracy of the P300 BCL. The orig- inal interface consists of 6 rows and 6 columns. A prediction is made for the row in which the attended character present after all rows have flashed for the specified number of repetition rounds and, similarly, for the columns. EEG data of the same row column, but from different repetition rounds, are averaged create an averaged epoch for classification. The chance level for selection accuracy is therefore 1/6 (16.7%).
(a)
tves cleeet
629
Top-1 Classification Accuracy Top-2 Classification Accuracy
Accuracy
REChe
True Mldss
GAF
G42
130
51 eppckamee
705
ET SPoPaaee bredl
Eyen Drwm
Predicted Labels
Accuncy
0.00
180
0.84
Gnetane
Iineeegrch
0.4
0,6
0.8
Percent of training data
Figure E.6: (a) Top-1 and top-2 classification accuracy on the seizure dataset with varying training data sizes, comparing EEGNet, EEG Conformer, LaBraM, BIOT, fine-tuned ST-EEGFormer models (small, base, and large), and the linearly-probed base model (ST-EEGFormer-b-ip Additionally, the base model was further calibrated on the seizure dataset by performing the MAE SSL task using a mask ratio of 0.75, then fine-tuned and linearly-probed, referred to as ST-EEGFormer-b and ST-EEEFormer bearrb-cclihhhi lp, respectively. (b) Confusion matrix of the ST-EEGFormer base model trained with 60% of the data. (c) Accuracy of fine-tuned and linearly-probed ST-EEGFormer base- cali models with varying mask ratios during the calibration stage. The chance level is 1/5 (20.0%).
34

---

Published as a conference paper at ICLR 2026
Table E.5: SSVEP dataset benchmark results. The average accuracies from the leave-one-sesee session-- out experiment are reported. The highest and becond-higheds accuracies are in bold, with the highest one marked in bold and surrounded by box. For ST-EEGFormer, the default fine-tuning strategy is end-to-end fine-tuning using the average token. Models denoted by "-cls" indicate end-to-end fine-tuned models utilizing the class token. The chance level 1/40 (2.5%).
Window 1s
Window 2s
Model
Top1-Ace
Top2-Acc
Top1-Ace
Top2-Ace
Linear
0.047
0.088
0.047
0.087
EEGNet
0.433
0.625
0.646
0.785
Conformer
0.328
0.517
0.419
0.618
BIOT
0,316
0.449
0,492
0.627
LaBraM
0,518
0.669
0.700
0,818
SSVEP-DNN
0,385
0.570
0.442
0,606
ST-EEGFormer-s
0.387
0.551
0.441
0.604
ST-BEGFormer-b
0.218
0.344
0.217
0.342
ST-EEGFormer-l
0.590
0.748
0.807
0.893
ST-EEGForrer-bclls
0.251
0.385
0.267
0.404
F.1 FOUNDATION MODELS
In this study, the following foundation models are benchmarked across all downstream tasks.
F.1.1 MODEL INTRODUCTION
BENDR (BERT-inspired Neural Data Representations) (Kostas et al. 2021) is one of the earliest Transformer-basee foundation models for BEG signals. Introduced Kostas al. (2021), BENDR combines convolutional encoder with Transformer decoder and adapts the sulf-supervised train- ing strategy from wav2vec 2.0 (Baevski et al.) (020)) to multi-channel EEG data. In pre- pre-trainnng, contiguous spans of the input EEG are masked and the model learns to reconstruct their latent rep- resentations using contrastive objective. This approach enables BENDR to learn general-purpose EEG features from large unlabeled corpora, which can then be fine-tuned specific tasks. In our implementation, we leverage the official BENDR code and pre-trained weights provided by its au- thors as a baseline foundation model (https: htt.:hhw..ooooeeee cww. Swtc//poclabbboooo ca/ BENDR..
BIOT (Biosignal Transformer) Yang et al. 2023 is a Transformer-based encoder designed for cross-dataset biosignal learning, demonstrated EEG data. The BIOT architecture tokenizes each EEG channel into fixed-length segments (local signal "patches") and then concatenates these seg- ments from all channels into a long "sentence" representation. Channel-specific embeddings and relative positional encodings are added to each token to preserve spatial and temporal context, al- lowing BIOT handle mismatched electrode montages, variable sequence lengths, and even miss- ing channels across different datasets. Pre-trained on multiple EEG datasets in the wild, BIOT has shown superior performance over task-specific models by learning from diverse data sources. The BIOT model used in this study is the version pre-trained all six EEG datasets, obtained from the official repository (https: http//gtttt/.t//. wttp:///wwccocccc00oocco0
LaBraM (Large Brain Model) (Jiang al. 2024 is large-scale EEG foundation model that aims to learn generic representations from tremendous amounts of EEG data. To enable cross- dataset learning, LaBraM segments raw EEG signals into channel-wise patches and employs vector-quantized autoencoder to convert each patch into a discrete neural code (acting as "to- ken")) A Transformer model is then pre-trained to predict masked patch codes from their sur- rounding context, similar in spirit to masked language modeling in NLP. This two-step approach (neural tokenizer + masked code prediction) allows the model to capture rich semantic information from the EEG. The published LaBraM models were pre-trained on approximately 2,500 hours of EEG recordings drawn from about 20 different datasets encompassing various BCI tasks, achieving state-of-r off-the-rtt results diverse downstream evaluations. In study, utilize the base ver- sion of LaBraM ("labram- base" checkpoint) released by the authors (https: : httptgttubb...////// com/ 935563004// B. Baterr///////eeeaaea
35

---

Published as a conference paper at ICLR 2026
EEGPT (Wang et al. 2024) is recently proposed Transformer- based foundation model for EEG that strives to produce universal and reliable EEG feature representations. The model introduces a dual masked self-supervised learning strategy: it performs simultaneous masking in both the spatial (electrode) dimension and the temporal dimension, and learns to predict the high-level representa- tions of these masked portions. By focusing the learning objective higher-level latent representa- tions (with higher signal-to-noise ratio) rather than raw signal reconstruction, EEGPT's pre-training task emphasizes more robust and salient EEG features. Additionally, the EEGPT architecture uses a hierarchical design that processes spatial correlations and temporal dynamics in separate stages, which improves training efficiency and adaptability various EEG paradigms. The model is pre- trained on large-scale compilation of EEG data from multiple tasks, and it achieves top-tier per- formance on a range of downstream benchmarks (often evaluated via linear probing on the learned features). We integrate EEGPT into our pipeline using the official implementation and pre-trained weights provided by its authors (https://githun) /ttp//ggghub.../// httphhuw.cooocccaoo wtt.:coo//iii... Btt///IINE222EEEPEEE
CBraMod (Criss-Cross Brain Model) (Wang et al. 2025) is an EEG foundation model that em- ploys a specialized criss-cross Transformer architecture to capture EEG's distinct spatial and tempo- ral dependencies. In contrast to standard Transformers that entangle spatial and temporal attention, CBraMod's s design uses two parallel self-attention streams: one operates across the channel di- mension model spatial relationships between electrodes, while the other operates along the time dimension to model temporal dynamics. This separated attention mechanism addresses the het- erogeneity of EEG signals and allows the model to learn rich spatiotemporal features. CBraMod large EEG corpus using patch-based masked reconstruction objective, where
is pre-trained patches of the input are masked and the model learns to reconstruct them, akin to a masked autoen- coder for EEG. Furthermore, it introduces asymmetric conditional positional encoding scheme to effectively handle varying EEG channel layouts and session formats. Thanks to these innovations, CBraMod has achieved state-of-the-an performance across broad range of BCI tasks (evaluated on up to 10 different EEG datasets), demonstrating excellent generalizability. In our experiments, we employ the official CBraMod code and pre-trained model checkpoint made available by the authors pestps:/ggitheg com/ w wjq learning/CBraMod
ST-EEGFormer
Three different ST-EEGFormers (small, base, and large models) were pre-trained
using the method described in Appendix E Details about the benchmarked model can be found in E..
F.1.2 MODEL TRAINING STRATEGIES
We evaluate two training strategies for foundation models: linear probing and fine-tuning. In the lin- ear probing setting, the pre-trained backbone is kept frozen, and only a classification head is trained on the downstream task, thereby directly assessing the representational quality of the pretrained em- beddings. In the fine-tuning setting, all model cecunnters-nncuucee jointly with the classification head, allowing the model to adapt its internal representations to the specific downstream dataset.
the backbone-and updated
As detailed in Table F.J. the classification head architecture varies considerably across foundation models, which may strongly influence downstream performance. Two main sources of variation are: (i) token fusion strategy and (ii) classification head design. For token fusion, some models (e.g., BENDR, EEGPT, CBraMod) flatten all tokens without compression, while others compute the average token embedding. For classification heads, while LaBraM and our ST-EEGFormer adopt the simplest ViT-style design (average pooling followed by linear layer), BIOT and CBraMod in- troduce non-linear activations (ELJU,, and EEGPT and CBraMod employ multiple stacked layers. Notably, CBraMod combines flattening strategy with multi-layer head, resulting in exception- ally large head (22.44M parameters) even exceeding the size of its backbone (4.88M).
These architectural inconsistencies across models have not been systematically compared in prior work, and have often been ignored, yet they likely introduce significant performance differences in downstream tasks. Motivated by this observation, our proposed ST-EEGFormer adopts sistent and minimal design: averaging token embeddings followed by a single linear layer, thereby eliminating potential confounds from large and heterogeneous classification heads.
36

---

Published as a conference paper at ICLR 2026
Table F.1: Implementation details of EEG foundation models. The classification head parameters are computed for a 62-channel, 3 second input with 4-class output.
Model
Sumpling rale (Hz)
Data Normalization
Clasiffiation Head
Head Params
BENDR
256
[-1. standardization
Hatten-Lineur
0.016388M
BIOT
200
percentile standadizzaion
Avg. ELU--Lmmea
LaBraM
200
rescale 0. V.l
Lig-Linear
0.000804M
EEGPT
256
rescale ImzV
0.033556M
CBraMod
200
rescale 0. 0.1mV
Flatten- Linear SLLU-Lnnaar-LL L-U--Lines
22.441604M
ST EEGFomerrs
128
stambardizzatinn
Avg. Linear
0.002052M
ST-EEGFomer-b
128
2-standardization
Avg-Lineur
0.003076M
ST EEGFomert
128
stambardization
Avg. Linear
0.004100M
Table F.2: ST-EEGFormer training configurations under two strategies.
(a) Fine-tuning
(b) Linear probing
CONFIG
VALUE
CONFIG
VALUE
Optimizer
AdamW
Optimizer
AdamW
Base learning rate
5e-4
Base learning rate
0.005
Weight decay
0.05
Weight decay
0.05
Optimizer momentum
81, 82 0.9, 0.999
Optimizer momentum
81, 0.9, 0.999
Lise LR decay (Bao
Batch size
64
al
2022
Clark al.
0.75
LR schedule
Cosine decay
2020
Warmup epochs
10
Batch size
64
Training epochs
100
LR schedule
Cosine decay
Label smoothing
0.1
Warmup epochs
10
Training epochs
100
Label smoothing Szegedy
0.1
2015)
Drop
path
(Huag et al.
0.1
2016
During the training foundation models, we adopt several optimization strategies summarized be- low. For ST-EEGFormer, both fine-tuning and linear probing follow the standard Vision Transformer (ViT) practice, as shown in Table F.2 For LaBraM, fine-tuning additionally incorporates layer-wise wise learning rate decay (LRD) and skips weight decay specific parameters, detailed in Table F.3 For all other foundation models, default fine-tuning and linear-probing strategy is used, summa- rized in Table [S5] Finally, for LOO fine-tuning experiments, adopt lighter configuration with reduced learning rate, smaller batch size, and fewer epochs, as shown in Table F4
F.2 CLASSIC NN MODELS
In this study, the following classic neural network EEG decoders are benchmarked across all down- stream tasks.
F.2.1 MODEL INTRODUCTION
NeepConyNet (Schirmeister et al. 2017) is deep convolutional neural network architecture developed for EEG signal decoding. consists of series of convolutional layers (for temporal feature extraction and spatial filtering), each typically followed by a nonlinear activation and pool- ing, which progressively transform raw multi-channel EEG data into higher-level representations. By leveraging deeper hierarchy of conv-pooling blocks, DeepConvNet can automatically learn complex discriminative patterns from the data without any handcrafting features. It has become a standard baseline in brain-come computer interface research, demonstrating that sufficiently deep CNNs can achieve strong performance on tasks like motor imagery classification and EEG- based pathology detection. The corresponding model architecture can be found in Table F.6
37

---

Published as a conference paper at ICLR 2026
Table F3: LaBraM fine-tuning configura- tion.
Table F.4: LOO fine-tuning configuration for foundation models.
CONFIG
VALUE
CONFIG
VALUE
Optimizer
AdamW
Optimizer
AdamW
Base learning rate
5e-4
Base learning rate
Se-5
Weight decay
0.05
Weight decay
0.01
Optimizer momentum
31,
0.9,
Optimizer momentum
81, 32 0.9, 0.999
Layer-wise LR decay
0.75
Batch size
32
Batch size
64
Training epochs
50
LR schedule
Cosine decay
Warmup epochs
Warmup epochs
10
Label smoothing
0.1
Training epochs
100
Label smoothing
0.1
Table F.5: Default training configurations under two strategies.
(a) Fine-tunine tunettuning
(b) Linear probing
CONFIG
VALUE
CONFIG
VALUE
Optimizer
AdamW
Optimizer
AdamW
Base learning rate
5e-4
Base learning rate
0.005
Weight decay
0.05
Weight decay
0,05
Optimizer momentum
81, = 0.999
Optimizer momentum
81. 0.9, 0.999
Batch size
64
Batch size
64
IR schedule
Cosine decay
schedule
Cosine decay
Warmup epochs
10
Warmup epochs
Training epochs
100
Training epochs
100
Label smoothing
0.1
Label smoothing
0.1
EEGNet (Lawhern et al., 2018) is compact CNN architecture specifically tailored for EEG- based brain- brain-computer interfaces. It employs depthwise separable convolutions to efficiently extract features, essentially splitting the filtering operation into temporal convolution (to capture frequency- specific patterns in each channel) and spatial convolution (to learn relationships across channels). This lightweight design drastically reduces the number of trainable parameters while still capturing key temporal-spectral characteristics of EEG signals. In practice, EEGNet has proven effective across many EEG decoding tasks and is widely used as a benchmark model, valued for its balance of simplicity. efficiency, and strong classification performance, In this study, the architecture of EEGNet follows the original implementation shown in Table F7
EEG Conformer (Song et al. 2023 is hybrid convolutional-Trasffoonni network designed to capture both local features and long-range dependencies in EEG data. Its architecture integrates an initial convolutional module that learns low-level temporal patterns and spatial features from the input signals. followed by self-attention based Transformer module that models global temporal correlations. By uniting CNN and Transformer components in this way, the EEG Conformer can leverage the strengths of both: identifying fine-grained short-term EEG patterns as well as broader context across time. This approach has achieved tfate-of-the-ar results on various EEG classifica- tion benchmarks, establishing the EEG Conformer leading example of modern EEG decoding architectures. The corresponding model architecture can be found in Table F.8
CTNet (Convolutional Transformer Network) ZZha al. 2024 another hybrid model com- bining convolutional feature extraction with Transformer-based attention, introduced for high- performance EEG signal classification (with a particular focus on motor imagery decoding). In this architecture, front-end convolutional module-inspires by earlier EEG-specific networks like EEGNet- first extracts temporally filtered and spatially filtered features from the raw EEG, produc- ing condensed feature sequence. That sequence is then passed into a Transformer encoder module, which uses self-attention to capture global temporal dependencies and refine the representation be- fore final classification. By integrating CNN-driven local pattern leamning with global sequence
38

---

Published as a conference paper at ICLR 2026
Table F6: DeepConvNet architecture, Input EEG data consist Neh channels and time samples (with sampling rate fa. L f&x trial duration). The network comprises four convolutional- pooling blocks with increasing filters (25, 50, 100, 200). dropout layer (p 0.5) follows each pooling.
Layer
Type
Input shape
Output shape
Kernels
Kemel size
Stride
Padding
Input
(Noa x L)
(Now L)
NA
NA
NA
NA
Conv2d (temporal)
(Not x L)
(25 x Nok x L)
25
(1, ()
(1,1)
same
2
Conv2d (spatial)
(25 x Net x L)
(25 X L)
25
(Neh,
1)
(1,1)
(0.0)
BatchNorm24
(25 x I)
(25 L)
NA
NA
NA
ELU
(25 1 L)
x
NA
NA
MaxPool2d
(25 L)
x L/3)
(1,3)
(1,3)
(0.0)
Dropout
I,33)
x25x 1,/3)
NA
NA
NA
NA
Conv2d
L/3)
x L/3)
5)
(1,1)
BatchNorm2d
(50 x L/3)
(50 X L/3)
NA
NA
NA
NA
ELU
(50 x x 1,3)
(50 L/3)
NA
NA
NA
NA
10
MaxPool2d
(50 X 1 L/3)
(60 1 L/9)
NA
(L. 3) NA
(13)
(0.0)
11
Dropout
(50 X X L/9)
(50 X X L/9)
NA
NA
NA
12
Conv2d
(50 X X L/9)
(100 1 X L/9)
100
(L 5) NA
(1
same
13
BatchNorm2d
(100 X
L/9)
(100 X 1 X L/9)
NA
NA
NA
14
ELU
(100 X 1 L/9)
(100 X 1 L/9)
NA
NA (I, 3) NA IL 5) NA
NA
NA
15
MaxPool2d
(100 X 1 L/9)
(100 X X L/27)
NA
(1,3)
(0.0)
16
Dropout
(100 x 1 L/27)
(100 X L/27)
NA
17
Conv2d
(100 x x L/27)
(200 X x L/27)
(1,1)
same
18
RatchNorm2d
(200
1,/27)
(200
1,277)
NA
NA
19
ELU
(200
L/27)
(200
L/27)
NA
NA
NA
20
MaxPool2d
(200 X L/27)
(200 X L/81)
NA
(1.3)
(1,3)
(0.0)
21
Dropout
(200 X 1,81)
(200 L/81)
NA
NA
NA
NA
22
Linear (Softmax)
(200 L/81)
(Nela.))
NA
NA
NA
NA
Table F.7: BEGNet architecture. Input EEG data consist of Noh channels and L time samples. The output corresponds to Nelase, representing number different classes to classify. The dropout ratio is set to 0.40.
Layer
Type
Input shape
Output shape
Kernels
Kernel size
Stride
Padding
Input
(N.a
(N.a
NA
NA
1
Cony2d
(N.a x L)
(8 x N x L)
8
[s/2)
(1,1) 1)
same
2
BatchNorm2d
x Nea x L)
x Nos X L)
NA
3
Depthwise Conv2d
(8 x Na x
(32 x L)
32
(Ner. 1)
(1,1)
(0.0)
BatchNorm2d
(32 x
(32 x L)
NA
NA
NA
ELU
(32
(32
NA
NA
NA
6
AvgPool2d
(32 x
(32 x x L/([s/3))
NA
(1.33)
(1,
fs/3))
(0,0)
Dropout
(32 L/(fa/321)
(32 L/(fs/32)
NA
8
Seperable Couv2d
(32 L/[{a/32)
(32 L/(s/32))
32
(1,16)
(1,1)
9
BatchNorm2d
(32 L.((fs/32)
(32 X LJ(fs/32)
NA
NA
NA
ELU
L/(fs332)
L/(fs/32)
NA
11
AvgPool2d
(32 L/(5s/32)
(32 x 1 L/(fs/8)
NA
(1.4)
(1,4)
(0,0)
12
Dropout
(32 x L/(fa(8)
(32 X L/(fs/8)
NA
13
Linear
x (2561/f5))
X Naass
NA
NA
NA
NA
modeling, CTNet effectively leverages both fine-scale EEG features and long-range context, leading to improved accuracy in EEG decoding and exemplifying the advance of CNN-Transformer hybrids in brain signal analysis. The corresponding model architecture can be found in Table F.9
F.2.2 MODEL TRAINING STRATEGIES
For all classic NN models (DeepConvNet, EEGNet, EEG Conformer, and CT'Net), we adopt unified and straightforward training strategy, summarized in Table F.T0 The table reports the settings for two scenarios side by side: the left panel corresponds to population training (full training from scratch), while the right panel corresponds to fine-tuning (adaptation
held-out subjects). To ensure fair comparison in the LOO performance-drop setting, the fine-tuning protocol for these models is aligned with that of the foundation models. This design choice avoids confounding factors such as differences in learning rate or training epochs, ensuring that performance differences can be attributed to the models themselves rather than training hyperparameters.
39

---

Published as a conference paper at ICLR 2026
Table F.8: EEG Conformer architecture. Input EEG data consist of Not channels and time sam-
ples. The output corresponds to Nelass representing the number of different classes to classify.
Layer
Name
Type
Layer specific settings
Output shape
Input
NA
NA
(NaxL)
kernel size:(1 (1, 25) number kernels:40
CNN- module
Conv2d
(40 x No. L)
kemel size: ( Vet. 1) number kernels:40
CNN-module
Conv2d
(40 L)
CNN-module
BatchNorm2d
NA NA kernel si
(40 x x L)
CNN-module
ELU
(40 x 1 L)
CNN-module
AvgPuol2d
(40, | 1)
stride:(1, 7)
CNN-mooe MNN-module
Dropout
Propout_p=O..r
(40, (-"7
kemnel size: 1, 1)
CNN- CNN-module
Conv2d
(40, 8-
number kernels:40
embed size:40
number of heads:10
Tramsformmer-ooculee
Transformer encoder layers
drro-pp:.3. forward 4xxpuasixxpmsiion:11
(40,
- + 1)
forward drroardddpppp. dtrop_p:0.5
depth:6
weights and bias shape: (40
Classification head
Linear
L-3r
+ 1, 1,2)
(1,256)
10
Classification head
ELU
NA
(1,256)
Classification head
Dropout
dropout p=0.5
(1,256)
weights and bias shape:
12
Classification head
Linear
(1,32)
(256, 32) NA dropout p=0.3 weights and hias shape: (32, Nelass)
13
Classification head Classification head
Elu
32)
Dropout
(1, 32)
Classification head
Linear
(1, Notun.
Table F.9: CTNet architecture. Input EEG data consist of Noh channels and L time samples (with sampling rate fa). The convolutional front-end applics temporal, spatial, and separable convolutions to extract local features, followed by Transformer encoder for global dependencies, and fully connected classifier.
Layer
Name
Type
Layer specific settings
Output shape
Input
NA kernel size: (1, 64), kemels::
(Nen
module
Conv2d (temporal)
X.N.. L)
CNN-module
Conv2d (spatial)
kernel size: (Net, 1). kernels: 40
(40 L)
CNN-module
BatchNorm2d
NA
(40 L)
CNN-module
ELU
NA
(40
CNN-module
AvgPool2d
kemel size: 8), stride: (1,8)
(40 x1 L/8)
CNN-module
Dropout
0.5
(40 x L/8)
CNN-module
Separable Conv2d
kernel size: (1, 16). kernel:: 40
(40 x1 L/8)
CNN-module
BatchNorm2d
NA
(40 x L/8)
CNN-module
ELU
NA
(40 X1 L/8)
10
CNN-module
AvgPool2d
kemel size: (1, 8). stride: (1,8)
(40 x x L/
11
CNN-modulc
Dropout
= 0.5
(40 x1 X L/64)
12
Transformer-modl
Transformer encoder
embed=40, heads=4, depth=6, drop=0.5
(40 x L/4))
13
Classification head
Linear
(40 L/64, 256)
(1,)56) (1, 256)
Classification head
FL.U
NA
15
Classification head
Dropout
p = 0.5 (256, Natoss
(1, 256)
Classification
Linear (Softmax)
(1, Natess)
F.3
CLASSIC NON-NN MODELS
We included different classic non-neural network models for each type of downstream tasks.
40

---

Published as a conference paper at ICLR 2026
Table F.10: Training settings for classic NN models under different strategies.
(a) Population training
(b) Fine-tuning
Config
Value
Config
Value
Optimizer
AdamW
Optimizer
AdamW
Base learning rate
3 10 3
Base learning rate
5 10
Weight decay
0,05
Weight decay
0.05
Optimizer momentum
B1, 82 = 0.9, 0.999
Optimizer momentum
31, 02 = 0.9, 0.999
Batch size
64
Batch size
32
Epechs
100
Epochs
50
Learning rate schedule
cosine decay 10
Label smoothing
0.1
Warmup epochs
Label smoothing
0.1
F.3.1 MOVEMENT AND SPEECH CLASSIFICATION TASKS
For the BCI-IV-2A, Upper Limb Motor Execution, Upper Limb Motor Imagination, and Inner Speech datasets, which involve movement or speech tasks, we implemented two types of decoding pipelines. The first category was CSP-based methods, including CSP-LDA, CSP-SVM (Ramoser et al. 2000). FBCSP-LDA, and FBCSP-SVM (Ang et al. 2008). The second category was Rieman- nian geometry-base based classifiers, including Minimum Distance Mean (MDM) (Barachant al.] 2012a). Fisher Geodesic MDM (FgMDM) (Barachant et al. 2012a), and tangent space mapping with ElasticNet (TS-ElastieNet) (Corsi et al.j 2022). The input EEG signals were first band-pass filtered (3rd-order Butterworth filter) into the 4-40 Hz frequency band. Alternatively, when using filter banks, we land-p the EEG signals into 9 consecutive frequency bands according to the formula: 4k 1((++)) Hz, k 1, 3, ..... 9. After preprocessing. the filtered signals were sub- jected to feature extraction. CSP learns spatial filters by minimizing the variance of power features within each class while maximizing the variance between classes in supervised manner. In this study, we used four spatial filters, resulting in four-dimensional feature vector for each EEG epoch. FBCSP extends this approach by applying CSP to multiple frequency bands, thereby producing nine times more features per epoch. The extracted features were used as inputs to LDA and SVM with a radial basis function kernel. In addition, the filtered EEG signals were transformed into covariance matrices, which are Symmetric Positive Definite (SPD) and reside in the Riemannian space. The MDM classifier computes the class centers in the Riemannian space and assigns unseen samples based on the geodesic distance between their covariance matrices and the class centers. FgMDM incorporates Fisher LDA into MDM. thereby enhancing robustness against noise. Furthermore, samples on the Riemannian manifold can be projected onto the tangent space, yielding vectorized feature representations. I'S-Elasticeet then applies the ElasticNet model these projected features. For all covariance matrix computations, the Oracle Approximating Shrinkage (OAS) estimator was employed to ensure robust estimation.
Signal band-pass filtering and CSP feature extraction were implemented using the MNE tool- box (MNE v1.9.0: httpps://m/ //ne. http://ee..oooooaaaaaaeeeeeeeeee wew.ereeatddddeee...eaae fi lter. filter data.html) LDA, SVM, and ElasticNet were based on scikit-learn (Scikit-learn v1.4.2: https:// wttp:://sciiitttean..oo org.1. 4 htaps//caassssssssesessss while MDM, FgMDM, and covariance estimation were implemented using Rieman (pyRiemann v0.6: https:/
pyriemann. wew.ccecssccccccceoee..om htwp:e/cccc..ooeeaatets////eee/e html).
F.3.2 ERN DETECTION TASK
For the ERN detection task on the ERN dataset, we implemented five baseline models: xDAWN- LDA (Rivet et al. 2009), XDAWNCov-MDM (Barachant) 2014),, XDAWNCov-TS--SV
(Cheval-
lier et 2018), ERPCov-MDN. (Barachant & Congedo 2014), and DCPM (Xiao et al. 2020). All models were trained on EEG signals that were band-pass filtered between 1-20 Hz using a 3rd-order Butterworth filter. xDAWN is a widely used spatial filtering technique that improves the signal-to- noise ratio of evoked potentials (Rivet et al. 2009). For xDAWN-LDA.
applied xDAWN with two spatial filters to enhance signal quality, followed by downsampling to 32 Hz. Temporal features from all channels were then concatenated into vector representations and used train an LDA clas-
41

---

Published as a conference paper at ICLR 2026
sifier. For SEAWNCov-MDM, the band-pass filtered EEG signals were augmented with prototype matrices (trial-averaged template) spatially filtered by xDAWN (four spatial filters). The augmented signals were subsequently transformed into covariance matrices, which were classified using MDM. For DDAWNCov-TS-SV
the same data augmentation procedure was applied, after which the co- variance matrices were projected onto the tangent space obtain vectorized representations. These features were then used to train an SVM classifier with a radial basis function kernel. ERPCov- MDM is simplified version of XDAWNCov-MDMM that omits the spatial filtering step. Finally, DCPM is an ensemble method that integrates variations of LDA and canonical correlation analysis (CCA) for ERP classification (Xiao et al., 2020).
The XDAWN algorithm, covariance estimation, and MDM classifier were implemented using pyRie- mann, while L.A,, SVM, and CCA were based on scikit-learn.
F.3.3 ALZHEIMER S DIAGNOSIS TASK
For Alzheimer's 's diagnosis using the Alzheimer's 's dataset, implemented four decoding pipelines described in the dataset paper (Miltiadous et al. 2023). Each pipeline extracts Relative Band Power (RBP) features and applies them to different classifiers: Random Forest (RBP-RF), SVM (RBP- SVM), k-Nearest Neighbors (RBP-kNN), and LightGBM (RBP-LightGBM), For each trial, EEG signals from all channels were decomposed using the Welch method to estimate the power spectral density (PSD), RBP features were computed by integrating the PSD within standard frequency bands (Delta: 0.5-4 Hz, Theta: 4-8 Hz, Alpha: 8-13 Hz, Beta: 13-25 Hz, Gamma: 25-45 Hz) and normalizing by the total power across 0.5-45 Hz. The resulting relative powers from all channels and frequency bands were concatenated to form feature vector for each trial. The extracted RBP features were used to train RF (100 trees), SVM (Polynomial kernel), kNN, and LightGBM (100 boosted trees with a learning rate of 0.05).
The implementations of RF, SVM, and kNN were based on scikit-learn while LightGBM was implemented using the official LightGBM package (LighiGBM v4.6.0: hattps://lightgan readthedocs -ociicccc.ooeee en
/ v4. 6. 0/Python API htmll..
F.3.4 SSVEP TARGET RECOGNITION TASK
For SSVEP target recognition on the Binocular SSVEP dataset, we implemented two decoding mod- els Filer Bank Canonical Correlation Analysis (FBCCA) and Task-Related Component Analysis (TRCA) as described in the dataset paper Yikee et al. 2024). Both models relied on filter banks for EEG preprocessing, with five frequency bands defined as [5, 95] Hz. [12, 95] Hz, [19, 95] Hz, [27, 95] Hz, and [35, 95] Hz. For FBCCA, sinusoidal templates with five harmonics were con- structed for each target according to its stimulation frequencies. the binocular swap dataset for evaluation, which is not ideal for FBCCA since half of the targets share identical stimulation frequencies. Nevertheless, FBCCA was included to examine the poten- tial of training- free model. For TRCA, we adopted the ensemble version, where class-specific filters were combined to form a universal spatial filter. The filter bank weights followed the rule k 1122 + 0.25, = 1, 1...
should be noted that we used
The implementations followed the provided code demo /ttp::::///hhttb/// /gtgttttthuhhtt//ggggeggg..hhhet cow.bbaaacmm//bba/// /bara//aaa/a/a/// 102557) and relied on the MEEGkit (MEEGkit v0.1.9/tttps: lithon-meegkii.
G BENCHMARK RESULTS
G.1 BENCHMARK DATASET RESULTS
In this section, we show the results for each downstream task.
G.1.1
ERN
Figure G. presents the benchmark results on the ERN dataset, reporting balanced accuracy across six evaluation protocols, along with aggregated model rankings and statistical significance analysis.
42

---

Published as a conference paper at ICLR 2026
(a)
Models
Camor Send
EXOPP
Bateacod Ascarne
0.5
mere
abawhi Das
Henate
CTMe
Lattont
MMSTEE
ID
man
populution
pe--sbjjettst
per wew-ebhjcctttrrneee
lso me--em-shoo
Iso- fhstsot
leu-drop
(b)
Not Significant
Runk
Signifieant (p-0.05)
Depi den
STEMtteeee Lapal in
(1)
Clse
Comttoor
ESchna to Chicked in MENta 2000 alleraain
40A8I (0)
STEaT STentdee BDND (D)
(D)
BOT to SDAWsCaa--Mp Mass DR -MN
snate
Figure G.I: Benchmark results on the ERN dataset. (a) Balanced accuracy of each model across six evaluation protocols: Population, Pur-Subject (Self), Per- Subjett (Transfer), LOO Zero-Shot, LOO Fine-Tune, and LOO Drop. Each dot represents
subject, with box plots summariz- ing distributions. Colors indicate model groups: blue-classic non-neural network decoders; creen-classic neural networks; curple-foundation models (linear probing); red-foundatin mod- els (fine-tuning). (b) Aggregated average rank per model, ordered from best (left) to worst (right). Circles show mean rank, colored bars indicate + standard deviation, and grey whiskers represent the minimum and maximum range. (c) Pairwise statistical significance matrix in the same model order as (b), computed via permutation tests (nresamples 50,000) with Bonferroni correction; red sig--ignificant (p < 0.05), blue-non-signifee -on-signiffiant.
The task appears relatively simple, with many models achieving near-perfect accuracy. Neverthe- less, several classic non-NN models and most linear-probed foundation models perform noticeably worse than the top performers. All models show reduced performance when transferred to unseen subjects, with clear drop under the Per-Subject--Tranffer and LOO Zero-Shot protocols. In the overall ranking, both the small and large variants our proposed ST-EEGFormer achieve the high- est scores, followed by the classic NN models DeepConvNet and CTNet. In general, almost all linear-probed foundation models-except EEGPT-rane among the lowest-performing methods.
G.1.2 BCI-IV-2A
summarizes the benchmark results the BCI-IV-2A dataset. The results show that certain classic non-NN models (e-g.. TS-ElasticNet) achieve top performance in the Per-Subject- Self and Ver-Subject-Transfer protocols. but perform worse in the Population and LOO Zero-Shov settings. Classic NN models remain highly competitive across all protocols. Ranking and statistical analyses indicate that the best NN model, CTNet, is not statistically different from the overall top- ranked model, fine-tuned ST-EEGFormer--, with both significantly outperforming all other models. All linear-probed probed foundation models perform the worst, ranking significantly below even the classic non-NN methods, except EEGPT.
Figure G.2
G.1.3 INNER SPEECH
presents the benchmark results on the Inner Speech dataset. This task appears highly challenging, with most models achieving accuracies close to random chance. The best results are predominantly achieved by classic NN models, which occupy three of the top four positions, with fine-tuned CBraMod ranking second. However, overall performance differences across models are small, suggesting that the inner speech decoding task remains intrinsically difficult.
Figure G.3
43

---

Published as a conference paper at ICLR 2026
(a)
Models
Chumbal
Balanced Acerray 1
2.
Laineel.0)
I5-CaencHa
DepCar/fc
Cllustd
EBON::
CThi
BRTTEI
STE S1teeceeee
popolation
per-unjer
pensubjjct-tcnntee
loo-are-shot
low-inetonc
ba--drpp
(b)
(c)
Not Significant
Significant (p-0.05)
Bank
CTher Stascaiion glons cedee
STEaneemee Couaeed
STEEO a1
Cheal
Figure G.2: Benchmark results on the BCI-IV-2A dataset, using the same notation and panel layout in Figure G.I. Results reported across six evaluation protocols, with colors denoting model groups and statistical significance assessed via permutation testing with Bonferroni correction.
(a)
Models
CXP-LD
CSP-V35
FRCEI-OVM
Holaned
tathont
KENOR.C
popolation
pre-subject-tramveer per-suhjectsell
no--soo-hoot
Iee-drop
(b)
(c)
Not Signiffenat
Rank 5
Significuu (p-0,05)
STEEL BESSM
ST STrrcuemet
Laboal Vrffidemmns
stst NOf straciionn STtatiloeen Faille
Figure G.3: Benchmark results on the Inner Speech dataset, using the same notation and panel layout as in Figure G.I. Results are reported across six evaluation protocols, with colors denoting model groups and statistical significance assessed via permutation testing with Bonferroni correction.
44

---

Published as a conference paper at ICLR 2026
(a)
Models
Cheerted
Chasbalt
DRALDS
Balunod Acturayy
FRCSNLIDA
Facen-ory
nexteuu un
LEGNN
to
Oerbbee
NOTT
STECD
popolation
pensobject-vit
per-utiject-traae -taansff
les-rem-skott
lve lo-finetuee
Ina-trop
(b)
(c)
Not Significant
Significant (p-0.66)
Rank
Srterdsetnn mEscommm 0
(
Bxor (D
Waa HReS A007 Lopull Lobaal
STeri
Figure G.4: Benchmark results on the Upper Limp Motor Execution dataset, using the same notation and panel layout as Figure G.T Results are reported across six evaluation protocols, with colors denoting model groups and statistical significance assessed via permutation testing with Bonferroni correction.
(a)
LUGPT
Dalance Axcurnay
CIFASSI
Leblek 31
FRESANAM
TSClariche
STL Chewoo
DeaCralti
Chandial
G
CThi:
popolation
per-
persubljes-ttandee
lee-tem-sbbt
boo-tinetuee
lee-dcop
(b)
(c)
Significune
8
Significant (p-0,05)
Runk
12
8
STErdimmen m
Dops bur syrenseme
Figure G.S: Benchmark results on the Upper Limp Motor Imagination dataset, using the same no- tation and panel layout as in Figure G.I Results are reported across six evaluation protocols, with colors denoting model groups and statistical significance assessed via permutation testing with Bon- ferroni correction.
G.1.4 UPPER LIMB MOTOR EXECUTION
Figure G.4
presents the benchmark results on the Upper Limb Motor Execution dataset. Overall, classic non-NN methods perform worse than classic NN models, while our proposed ST-EEGFormer achieves the highest performance. A large degree of subject variability is evident, with accuracies ranging from above 90% to near chance level. Most foundation models-bon fine-tuned and linear- probed-an are statistically equivalent to each other and generally underperform compared to classic NN models.
45

---

Published as a conference paper at ICLR 2026
(a)
Models
Clewcr Levd
MSCIA
Batanced Aecuracy
Candereer
eacart to
MUNUK.r
STitttteeeo (D
Citalied ID
population
per-ubjjcc-efff
per por-suffeet--taae transfer
hon-Beeteee
loe-drop
(b)
(c)
Significant Siguifioms (g+3.15)
Rank
STEaI 510
in TREA
SYD aiDiloomeegg (0) aEAD
EEOPT (D)
BOY PB4CE1
Deperia
Contero
(Roalled (O
BOT
snunts ())
Lanad RExDal (1)
sTEr02ionn (D)
Figure G.6: Benchmark results on the Binocular SSVEP dataset, using only the synchronous trials (the first I-s trial post-onset) using the same notation and panel layout as in Figure G.T Results are reported across six evaluation protocols, with colors denoting model groups and statistical signifi- cance assessed via permutation testing with Bonferroni correction.
(a)
Models
Londl
Balancod Accuracy
0x-
MExtre
04
Conture:
Eitestaaioo
CINA
Raxert co
62
BIOT
Laitakd on
BENDA di
eeart to
population
per perssubfect-onnnsee prrsubject-offf
lee-tensstol
lon-Iiettuee
loa-drop
(b)
(c)
Signifficnt
Significatt (p-01.55
Rank
Latook
Deppe center
ESCPP
#SCPP/1
Figure G.7: Benchmark results on the Binocular SSVEP dataset, using the asynchronous trials (1-s trials cut by sliding window) using the same notation and panel layout as in Figure/G; G.] Results are reported across six evaluation protocols, with colors denoting model groups and statistical sig- nificance assessed via permutation testing with Bonferroni correction.
G.1.5 UPPER LIMB MOTOR IMAGINATION
presents the benchmark results on the Upper Limb Motor Imagination dataset. Overall, a similar trend to that in Section G. 1.4 is observed: classic non-NN methods perform worse than classic NN models, while our proposed ST-EEGFormer achieves the highest performance. However, overall accuracy drops substantially compared to the execution task, with the best subjects achieving below 40% accuracy, indicating that motor imagery is more variable and challenging. In this difficult setting, there is no statistically significant difference among the top rop-performing models, except for the fine-tuned large variant of ST-REGFormer, which outperforms the rest.
Figure G.5
46

---

Published as a conference paper at ICLR 2026
(a)
(b)
(c)
(d)
Model
Diogusit Accuracy
1.0
Group A
Daggostt Ascurncy
Group F
Diogasit Aseursey
1.0
Group
Bd
18600
B4
0-3
822
02
6n
Bdg
02
0.
Chelledi
DECPTE)
LOO Zero-Shof
Disgnosis Threshold
Diagnosis Threshold
Diagaosis Threshold
(e)
(f)
Ner Signidoun
Prggralommmen (1)
Except to
sTrachhemn (f)
Labaal (D)
snagii-mmee Depprrmtion
(f)
BloT mi)
Cria
Contonar
Resor (f)
Lamall et
nor to
Cheakee
HINoE ())
Figure G.8: Benchmark results the Alzheimer's dataset. This figure summarizes subject-level di- agnostic performance of different models under multiple evaluation views. (a) Model performance under the LOO Zero-Shot evaluation protocol. (b-d) Brreshold-ccuracee curves for diagnosing Alzheimer's ' disease (A), frontal dementia (F), and healthy controls (C), Diagnosis is determined when the percentage of trials from a subject classified as the target class exceeds the diagnosis threshold. (e) Aggregated ranking of all models. (f) Pairwise statistical significance matrix, fol- lowing the same format as Figure(G.) Model groups are color-coded, and statistical significance assessed via permutation testing with Bonferroni correction.
G.1.6 BINOCULAR SSVEP
For the Binocular SSVEP dataset, we evaluated two settings: synchronous trial classification (using the first 1-second trial post-onset) and asynchronous trial classification (using 1-second windows extracted via a sliding window). This design allows a fair comparison with classic non-NN meth- ods, which are typically evaluated in synchronous settings. Results are shown in Figure G.6 and F.g respectively. We observe that TRCA performs exceptionally well in synchronous classi- fication but degrades substantially in the asynchronous setting. In contrast, NN-based decoders, in- cluding foundation models, show stronger performance in the more challenging asynchronous con- dition, indicating better generalization to difficult tasks. Notably, in both settings, the training- FB-C FB-CCA method outperforms many foundation models. These results illustrate that well-developed classic methods can achieve comparable or even superior-performaan BCI tasks with limited data, whereas NN-based approaches tend to generalize better when task difficulty increases, suggesting potential gains with larger downstream datasets.
on specific downstream
G.1.7 ALZHEIMER'S S
Figure G.8 summarizes the benchmark results on the Alzheimer's dataset. Since this task is designed to mimic a clinical diagnostic application, we evaluated models only under the IOO Zero-Shot protocol. As shown in Figure G.8p. all models achieve similar performance, with some linear- probed foundation models showing slightly lower average accuracy. A closer look at the diagnosis accuracy for each group in Figures G.8p-d reveals that all models classify the Alzheimer's group (A) and the healthy control group (C) more easily than the frontal dementia group (F). Overall, most models do not differ significantly performance on this dataset, with the weakest results observed for several linear- probedd foundation models and the EEG Conformer.
G.1.8 TUEV
presents the benchmark results on TUEV. Fine-tuned foundation models perform signifi- cantly better than classic neural decoders; notably, the top seven models are all fine-tuned foundation models, with no significant differences among them. By contrast, linear- proberr-robedd foundation models remain comparatively weak.
Figure G.9
47

---

Published as a conference paper at ICLR 2026
(a)
Models
Charse
Strarioowe da
Ralonced Acveriey
REsaa to
CThe
Clessaccn on
0.4
Aitaiiiiomme 23
Subje-Su
(b)
(c)
Not Signifieant
Ronk
Significam (p:0005)
(0
Labelig
Caltimer
Cris
spant.
ststosomun 1.0)
prreedionnt (1)
Stttrieean Borr nuro
Latolk tD
Figure G.9: Benchmark results on the TUEV dataset, using the same notation and panel layout as in Figure G.I Results are reported using the conventional cross-subject protocol (LOO Zero-Shot), with colors denoting model groups and statistical significance assessed via permutation testing with Bonferroni correction.
(a)
Models
Chevee
CiThe
chardeitt
BIOTED
sscarion on
ESGET
STExkoamark et)
STELOOemma (9)
Cros-Subjects
(b)
(c)
Not Significamt
Significant (p=0.05)
Conforet
(i Deptomntin hit
MbDee (t)
Cyse
STartiieee 1D
imscessentt (2balt
(r
smniiamee Bor() miscaemmn
Labelt (1)
Figure 1.10: Benchmark results on the FACED dataset, using the same notation and panel layout as in Figure/G. G.I Results are reported using the conventional cross-subject protocol (LOO Zero-Shot), with colors denoting model groups and statistical significance assessed via permutation testing with Bonferroni correction.
1.1.9 FACED
Figure G. 10 reports the benchmark results on FACED. The EEG Conformer is the top-performing model and significantly outperforms all others, followed by fine-tuned CBraMod. Classic neural decoders remain highly competitive on this dataset, whereas linear-probed foundation models lag behind.
G.1.10 DTU
Figure G.Il presents the benchmark results the auditory regression DTU dataset. In this rela- tively simple regression task peek-pricitiigg a single value at a time classic NN models outperform all foundation models, with the notable observation that linear-probed foundation models surpass
48

---

Published as a conference paper at ICLR 2026
(a)
Models
CTNNr
Scbiee10 to
Parron
IOT
Clocseal in
DENOR (D
Latedd4
EXXPPT
-632-
-6:3
population
per-snbeett-elf
Ioo-zere-hhtt
Bootune
leo-drop
(b)
(c)
14:8
Significoat Significant (g-2005)
12.5
poosa
Mooee (1
stratikoon (0)
Lahuttn)
sxcerin
Deptiantia
morm
Confones
Labutti
Lokelit
Figure G. G. Benchmark results on the DTU dataset, following the same notation and panel layout as in Figure G.T In panel (a), scatter plots display the Pearson correlation coefficient (R). Panel (b) shows aggregated rankings computed from both MSE and Pearson R. Results are reported across six evaluation protocols, with colors indicating model groups, and statistical significance assessed via permutation testing with Bonferroni correction.
(a)
Models
Confemc:
Perront
secar ot
Laluor to
Labrski
STEDCRRNEN
-02-
population
persubject-efff
prosuhjers trander
Iso Mortor--oott
Ion Anetnne
lae-drop
(b)
Not Significant
Significam (p-0.05)
Labbatd
11
STEatSionenn 10
Choske
Borr
()
Dopt crststemmen (i)
Berr
Acsuoe
sock
Figure G.12: Benchmark results on the SEED-VIG dataset, following the same notation and panel layout as in Figure G. In panel (a), scatter plots display the Pearson correlation coefficient (R). Panel (b) shows aggregated rankings computed from both MSE and Pearson R. Results are reported across six evaluation protocols, with colors indicating model groups, and statistical significance assessed via permutation testing with Bonferroni correction.
their fine-tuned counterparts. This trend may be due to the limited subject-specific data available for fine-tuning. However, even the best- rest-performing NN model (CTNet) substantially underperforms compared to -fate-of-the-art regression models such as Sea- Wave (Yang et al. 2024). which achieve Pearson R values around 0.2, whereas all benchmarked models remain below O.I. These findings suggest that representations learned by EEG classifcattionorrintee models transfer poorly to gression tasks. Future progress may require foundation models with dedicated encoder-decoder architectures explicitly designed for regression.
49

---

Published as a conference paper at ICLR 2026
G.1.11 SEED-VIG
Figure G.12 reports the benchmark results on the SEED-VIG vigilance regression task. Overall, fine-tuned foundation models outperform classic neural decoders, and there no statistically sig- nificant difference among the seven best-performing models. On average, methods achieve Pearson correlations above 0.4 on this dataset, suggesting is comparatively less challenging than the audi- tory regression task.
G.1.12 SUMMARY OF ALL BENCHMARK RESULTS
Overall, the benchmark results vary considerably across datasets. Based on statistically evaluated performance rankings, the models can generally be grouped into three tiers. In most cases, the lowest tier consists linear-probed foundation models. The top tier often includes the large fine- tuned ST-EEGFormer together with few classic NN models such CTNet, while certain classic non-neural methods achieve strong results but more often fall into the middle tier alongside other foundation models.
These findings underscore critical point: performance gaps between models are highly task- dependent, and reporting single accuracy value-as common in many foundation model pa- pers pers-can can be misleading. Without statistical analysis, such reporting may obscure the fact that ob- served differences are not significant. For example. in the Alzheimer's dataset results (Figure (Figure(..2). one might conclude from accuracy alone that ST-EEGFormer-s (f) is the best model; however, it IS statistically indistinguishable from other models.
G.2 LINEAR PROBING VS. FINE-TUNING
We compare the average performance of foundation models under linear probing and fine-tuning strategies. Table G. reports results aggregated across all six evaluation schemes for each model, while Table G.2 presents results for each evaluation scheme aggregated across models.
Table G. Accuracy (Mean + Std) and Paired Wilcoxon Test p-values for Linear Probe and Fine- Tuning Across Foundation Models
Model BIOT
Linear Probe
Fine-Tuning
P-Value
0.256 + 0.266
0.301 + 0.271
p 6.84 x 10-23
BENDR
0.177 + 0.267
0.310 0.275
p = 1.15 x 10-43
CBraMod
0.186 + 0.261
0.233 0.284
p 1.95 X 10-27
EEGPT
0.295 + 0.265
0.225 0.267
p 5.75 X 10-37
LaBraM
0.153 + 0.230
0.320 0.272
2.57 X 10-13
ST-EEGFormer-s
0.179 = 0.252
p 6.65
10-46
ST-BEGFormer-b
0.425 + 0.261
0.608 = 0.290
p 1.22 x 10-17
ST-EEGFormer-l
0.428 + 0.283
0.637 + 0.280
1.22 x 10-17
Table G.2: Accuracy (Mean + Std) and Paired Wilcoxon Test p-values for Linear Probe and Fine- Tuning Across All Datasets and Evaluation Protocols
Evaluation Protocol
Linear Probe
Fine-Tuning
P-Value
Population
0.228 + 0.238
0.349 + 0.263
p= 2.62 x 10 79
Per-Subject (Self)
0.194 0.234
0.234 + 0.267
p = =8.2 x 10-52
LOO Fine-Tune
0.219 + 0.235
0.347 + 0.259
p =4.8 x 10-89
Per-Subject (Zero-Shot)
0.159 + 0.200
0.171 + 0.216
== ==8.48 x 10 10-42
LOO Zero-Shot
0.333 + 0.318
0.400 = 0.326
p == =33.05 x 10 10-48
LOO Drop
0.037 + 0.064
0.047 + 0.052
p 6.86 10-10
50

---

Published as a conference paper at ICLR 2026
G.3 NN MODELS COMPARISON
Per-task and per-evaluation scheme results are shown in Figure G.13 The results highlight that clas- sic NN decoders remain highly competitive, particularly when compared to lincar- probed founda- tion models. Foundation model performance exhibits substantial variability across both downstream tasks and evaluation protocols. While fine-tuned foundation models often achieve top performance in population and LOO Fine-Tune settings, they tend to underperform compared to classic NNs in Per-Subject (self) and Per-Subject (transfer) evaluations.
To enable comprehensive comparison of model performance across datasets, evaluation protocols, and subjects, we computed model ranks separately for each metric of interest. For each combination of dataset, evaluation protocol, and subject, models completing the same experiment according to their metric value. Ranking was performed independently for each metric. In cases of ties, we applied competition ranking scheme ("1, 1, 3, ... "), where models with identical scores received the same (lowest) rank, and the next model was ranked as if the previous positions were occupied. This approach accommodates ties and missing values, enabling robust aggregation and statistical comparison across heterogeneous experimental settings.
ranked
We report results for all six evaluation protocols in Figure G.14 (within-subject: models have ac- cess to the test subject during training) and Figure G.I5 (cross-subject: models do not see the test subject during training. plus LOO Drop). Aggregated results for within-subject and cross-subject evaluations are presented in Figure G.16
Based on the results Figure G.14 Figure G.15 and Figure G.16 several important patterns emerge. While the fine-tuned large ST-EEGFormer achieves the highest overall performance, its advantage over the second-best model, CTNet, is often not statistically segnffiant-Paartculaaty particularly in Per-Subject (self) and LOO Fine-Tune protocols. In fact, foundation models of comparable size (under 30M parameters) generally do not outperform compact classic NN decoders such as CTNet (a few million parameters) even EEGNet (a few thousand parameters).
The clearest advantage for foundation models emerges in the LOO Drop protocol, suggesting a stronger ability to retain and leverage previously seen examples. Our results also indicate that foun- dation model performance may be biased toward the characteristics of their pre-training datasets. Many existing EEG foundation models are pre-trained on large-scale clinical EEG datasets (Obeid) & Picone 2016), potentially favoring tasks such as abnormal EEG detection while providing limited benefits for more diverse BCI paradigms. In contrast, our proposed ST-HEGFormer- prrfoomme------tiiineed exclusively on BCI aacheeessachieves higher average performance across tasks. Nevertheless, its small and base variants still underperform compared to simple classic NN models in several settings, underscoring the need for further investigation into model architecture, pre-training data diversity, and task alignment in EEG foundation model development.
Finally, our statistical analysis underscores an important point: conclusions based solely on mean accuracy, as is common in prior work, can be mislcading. Rigorous statistical testing often reveals that differences between top models are not significant, challenging the narrative of superiority. Together, these findings expose current limitations in EEG foundation models and emphasize the necessity of fair, natistically-groundee benchmarking drive meaningful progress.
G.4 BEST MODEL COMPARISON
For each downstream task, we compare the accuracy distributions across subjects for the best- performing model in each of the four decoder groups: classic non-NN, classic NN, lineer-probed foundation models, and fine-tuned foundation models. The results are shown in Figures G.17 G.24
In summary, the statistical test results reveal that, for many downstream tasks, differences in mean performance between the top models from different EEG decoder types do not translate into statis- tically sigmificant differences. Substantial variability is observed across datasets, underscoring that model performance is heavily task-dependent. Nevertheless, certain trends emerge: classic non-NN models consistently underperform compared to NN-based approaches in population and transfer settings, yet remain competitive in per-subject (self) evaluations.
51

---

Published as a conference paper at ICLR 2026
BELAV-J
LOO Dop
1000 Zum Stve
LCO
Par rejer (Towedary
Pe: Sukker (540
Poydutice
geos alll
SE
Deogie
Upper Bec Imagensiee
Uppc fnd cucatber
200
Deo
LOO Fam- Tux
100 Fan Tanc
Per Stjject (lonade)
Sabjet (Tresfe)
Popeiatice
Popebation
Doppe GRONN
Binscalar mynderaee dandiictioon
Blasedhe Eng (hattication)
100
L00
DO0 Zum Skee
100 Zaro Star Tome
PeeSutees Subret (Sc))
Pre Subven (Sel)
Dogp Erotie
EECPY 2e
Luttoaki (1)
Fay Payiio
Depp DECOhe
ST-ROO (D)
Sotttti (1)
MEED-VIG
Baak
Dee
Depp
Sigeet (erendy
LOO
Por Sulged (Tunader)
Time
Pepalatio
Deppe ElOON
Farmer
Deggs
DTI
Aldhiinr''s (1.00 Zere-Shol
Baak
Lo0 Zom Skal 100 Teee
Subjed (Sd0
Popelascs
Daage
C2tie
RESITI ill
(Cres-Subjee))
Subjet)
Ascrac
Figure G. 13: Comparison of classic NN models (green) with linear- eeearrprobed foundation models (pur- ple) and fine-tuned foundation models (red) across six evaluation protocols on all benchmarked downstream tasks. The bar plot depicts the average aggregated rank for each model (lower rank indicates better performance). The heatmap shows the average aggregated rank per evaluation pro- tocol.
52

---

Published as a conference paper at ICLR 2026
Population
Per-Subject (Self)
LO0 Fine-Tune
BENDS II
BENDe
STER00mme
(0
STERVboa
Ciraaied
BRGHT
CBoMad
Lataphi
minue (n
m
ZECNLE
STESoamece (
S1EEG
CThe
STEEGfemad
CTN
Raak
Rank
Rank
SHICC
Clee
Cls
Not Significant
Significant (p=0.05)
Figure 14: Comparison of classic NN models (green) with linear- prob-eroobddde foundation models (pur- ple) and fine-tuned foundation models (red) under three within-subject evaluation protocols: Pop- ulation, Per-Subject (self), and LOO Fine- Tune. Top row: Model rank distributions, ordered from best (bottom) to worst (top). Circles indicate mean rank, horizontal bars represent the interquar- tile range (25th-75th percentiles), and whiskers denote the minimum and maximum ranks. Bottom row: Corresponding pairwise statistical significance matrices from 50,000-run permutation tests with Bonferroni correction. Red cells indicate significant differences (p < 0.05), and blue cells indicate no significant difference. Model order (top-down, left-right) matches the ranking plots above.
Per-Subject (Transfer)
LOO Zero-Shot
LO0 Drop
LaBeM
CTVC DappOrnN
SENUK
SENUR 11
LSUNS
STERObumed
STEEGR ner 100
2021
B101 10
31E500nee (1)
CB Shald
BENDR
BESDR
BEXOR (0
STEBd b/)
STEEORMEE
BBN
EEGNA
STE
chustal
STED0
STESG
Labrak
10 Rank
Rank
Rank
11 00
Libra
Nort
Hot
Stune
Not Significant
Significant (p=0.05)
Figure G.15: Comparison of classic NN models (green) with linear- linear-probed probed foundation models (pur- ple) and fine-tuned foundation models (red) under two cross-subject evaluation protocols Per- Subject (transfer) and LOO Zero-Shot-aae as well as the LOO Drop protocol. The notation and panel layout follow those in Figure G.14
53

---

Published as a conference paper at ICLR 2026
Within-Subjeet Evaluation
Cruss-Subject Evaluation
Generalization Drop Evaluation
CTN6
SEN3R
BENDR
BEGPT (3
EISPT
(0
STERORmee
BIOT
HOJ (1)
EEGHT
BENDRI
8 STRES
Cesterer
Steedfomma
Latudd
Conferel
Coepho het
ED
EDOPT
CThe
STEtOkmes
Talia
Rank
Librar STEtioeee
Cloved
chuited
muc
smigee
WHi
10
Not Signifieant
Significant (p=0.05)
Comparison of classic NN models (green) with linear- erear-probed foundation models (pur- plc) and fine-tuned foundation models (red) under two aggregated evaluation settings plus the LOO Drop protocol: (i) within-subject, combining Population, Per-Subject (self), and LOO Fine-Tune protocols; and (ii) cross-subjecc, combining Per-Subject (transfer), LOO Zero-Shot protocols. The notation and panel layout follow those in Figure G.14
Figure G.16:
Population
Per-Subject (Self)
Per-Subject (Transfer)
1.00
Accuncy
0.90-
0,00
cri
LOO Zero-Shot
LOO Fine-Tune
LOO Drop
!
0:355
Accuncy
6433
4000
21
00 20
Figure G. 17: Subject-wise accuracy distributions on the ERN dataset for the best-performing model from each decoder group: classic non-NN models (blue), classic NN models (green). linear- probed foundation models (purple), and fine-tuned foundation models (red). The notation and pancl layout follow those Figure 4
G.5 ATTENTION MAPS COMPARISON
We plot the attention maps on the BCIC-IV-2A dataset of BIOT, LabraM and EEGPT models. Each map is categorized based on the adaptation method (linear probing or fine-tuning) and the input data (left-hand MI right-hand MI). Therefore, each model has total of four attention maps. For visualization, we first average the attention scores across all heads and temporal positions. Following the attention rollout approach described below Abbar & Zuidemal (2020), we then aggregate the attention scores across all layers and project them onto the topographic head map for visualization. The results shown in Figures G.25 to Gi36
54

---

Published as a conference paper at ICLR 2026
Population
Per-Subject (Self)
Per-Subject (Transfer)
Accuraey
0:80
0.25
cocS2
Cold CO Seoee
STE2 SOOOR Coneeee
CTSC Staaii S Steccferee HD
LOO Zero-Shot
LOO Fine-Tune
LOO Drop
07-
Accumicy
86-
21
EXCPT 20
Cuckernt WoN0 Cateeaiin
Figure G.18: Subject-wise accuracy distributions on the BCI-IV-2A dataset for the best perffornnf model from each decoder group: classic non-NN models (blue), classic NN models (green), linear- probed foundation models (purple), and fine-tuned foundation models (red). The notation and panel layout follow those in Figure
Population
Per-Subject (Self)
Per-Subject (Transfer)
0.500
19.28
Accurncy
0.000
0.22
0:35
9.24
Couternes
WOODR
WENGOR
BENDR in
Zero-Shot
LOO Fine-Tune
LOO Drop
0.72
0.11
0.35
8.43
Accunccy
8:5
0:33
8:5
0.22
LOOPS CheMAHH
Confbctie
Figure 1919: Subject-wise accuracy distributions on the Inner speech dataset for the perffornnn model from each decoder group: classic non-NN models (blue), classic NN models (green), linear- probed foundation models (purple), and fine-tuned foundation models (red). The notation and panel layout follow those in Figure
G.5.1 ATTENTION ROLLOUT
Attention rollout tracks the information flow from the input layer to the final layer in transformer
model through Eq G.1 AAbar & Zuidema 2020), where, Á is the attention rollout, and A(4,) the raw attention matrix in layer In order focus on the most important tokens while ignoring less relevant ones, we apply discard ratio that retains only the largest rollout weights at each layer. For instance, a discard ratio of 0.9 will keep only the top 10% of the largest weights, setting the remaining weights to zero. After calculating the rollout, each head produces weight matrix. The final weights are obtained by fusing the weights across different heads, using one of the following methods: mean fusion, where the final weight is the average of all head weights; max fusion, where the final weight is the maximum value across all heads; and min fusion, where the final weight the minimum value across all heads.
55

---

Published as a conference paper at ICLR 2026
Population
Per-Subjeet (Self)
Per-Subject (Transfer)
14-
0:20
Accuracy
9.22
8:05
8:00
0.-
15 15-22enbele
Cte Steamin
Stecchaee
Stati Sitoleee
STEEO Seneee
STate
LOO Zero-Shot
LO0 Fine-Tune
LOO Drop
0.5
0.3
Accuracy
02-
0.2
82
SOOPT
S1p
Stteciloser..0
Cafferet CooMtetl
Figure G.20: Subject-wise accuracy distributions on the Upper limb motor execution dataset for the best-performing model from each decoder group: classic non-NN models (blue), classic NN models (green), linear-probed foundation models (purple), and fine-tuned foundation models (red). The notation and panel layout follow those in Figure 4
Population
Per-Subject (Self)
Per-Subject (Transfer)
3.45
8.38
2.80
Accuracy
0.15
Stank Not
CeREe STEDD STECOROSeS Oi
Coste o
LOO Zero-Shot
LOO Fine-Tune
LOO Drop
0.15
0.35
Accuncy
0.10
0,39
0.23
0.8
0.28
115500 mome
5XY Chassadin
Steccimme-tt
Figure G.21: Subject-wise wise accuracy distributions on the Upper limb motor imagination dataset for the best-performing model from each decoder group: classic non-NN models (blue), classic NN models (green), linear- probed foundation models (purple), and fine-tuned foundation models (red). The notation and panel layout follow those in Figure 4
Á(L) (A(I)) + +1)A(4-1) ifi 1 (A(L) +T
(G.1)
ifi=1
G.5.2 DISCUSSION
In summary, analysis yields the following observations:
1. Shifted attention after fine-tuning. With the exception of EEGPT, most foundation models attend to different regions after fine-tuning on downstream classification tasks. This suggests that representations learned during pre-training are not fully aligned with task-specific ones.
56

---

Published as a conference paper at ICLR 2026
Population
Per-Subject (Self)
Per-Subject (Transfer)
10
10-
Accuracy
C3-
06-
66
0.2
600
1och CO
VBCAA
CThi
casa
Ison Chuabar
LOO Zero-Shot
1,00 Fine-Tune
LOO Drop
0.5
Accuracy
DG--
0.2
-0
YECA
68
Labell
chobled
Figure G.22: Subject-wise accuracy distributions on the Binocular SSVEP dataset (synchronous classification) for best-performing model from each decoder group: classic non-NN models (blue), classic NN models (green), linear-probed foundation models (purple), and fine-tuned foun- dation models (red). The notation and panel layout follow those Figure
Population
Per-Subject (Self)
Per-Subject (Transfer)
Accuracy
BAT PORDII
POLCCA
woitt BExORin
LOO Zero-Shot
LOO Fine-Tune
LOO Drop
Accuracy
06-
E.S
0.4
CC36
RIST
Figure G.23: Subject-wise accuracy distributions the Binocular SSVEP dataset (asynchronous classification) for the best-performing model from each decoder group: classic non-NN models (blue), classic NN models (green), linear- linear-probed foundation models (purple), and fine-tuned foun- dation models (red). The notation and panel layout follow those Figure
2. Consistent spatial pattern s across models. Despite architectural differences, all models exhibit symmetric attention patterns, with increased focus on regions near the motor cortex after fine- tuning.
3. Limited distinction between left- and right-hand MI. Attention maps for left- and right-hand motor imagery are highly similar across models, indicating that key discriminative features may lie beyond the coarse spatial patterns captured by attention rollout.
57

---

Published as a conference paper at ICLR 2026
LO0 Zero-Shot
100
80-
Accuracy
20-
BB0N
EEOPT (n
51- Eioorrmme ())
ST-E1GEvmer (1)
Figure G.24: Subject-wise accuracy distributions on the Alzheimer's dataset for the best perf-peeron model from each decoder group: classic non-NN models (blue), classic NN models (green), linear- probed foundation models (purple), and fine-tuned foundation models (red). The notation and panel layout follow those in Figure
Quan
Quen
Cary Fox
Figure G.25: Attention topographical visualization for the LabraM model using linear probing on left-hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
H ADDITIONAL EXPERIMENTS
H.1 TOKEN FUSION AND CLASSIFICATION HEAD STRATEGIES
We performed an ablation on BCI-IV-2A and ERN datasets using three foundation mod- els els-CBraMod, EEGPT, and our ST-EEGGFormr--maall (abbrev. ST-IEEGFormer-) evaluation protocols, including LOO Zero-Shot, LOO Fine-Ture LOO Drop, Per-Subject (Self), and Per-Subject (Transfer). For each model, we compared two variants that differ in (i) how token features are fused and (ii) the final classification head (Figure H.T and Figure H.2
five
Simple Average token pooling followed by single linear classifier.
Complex For CBraMod and EEGPT, the model's default classification head; For ST- ERGFormer-s, use a full-token head with two linear layers.
Across both datasets and all five protocols, the Complex design yields better performance in most set- tings-often with statistical significanc--sseett that classification head capacity and token fusion choices are important to downstream performance. In the case of linear- linear-probed foundation models, relying solely on average token fusion may discard valuable spatiotemporal information that more expressive classification head can leverage. We recommend future work to systematically explore head architectures for EEG foundation models to unlock further gains.
under linear probing. These findings suggest
58

---

Published as a conference paper at ICLR 2026
CWNS FC
CiNEy FLI
Vaegu FC
-4
O
Denyy
Devey Ce
Swey 12
Cery
-2
Figure G.26: Attention topographical visualization for the LabraM model using linear probing on right-hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
Qumm
Deny
ON
Oecy
Quer
Owny
Quey
Denyy NC
Figure G.27: Attention topographical visualization for the LabraM model using fine-tuning on left- hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
H.2 EFFECT OF TRAINING-SDI SIZE
We varied the proportion of labeled training data from 20% 80% BCI-IV-2A and ERN, bench- marking all neural decoders under the five evaluation protocols. Results are shown in Figure H.3 and Figure H.4
Key observations
1. Monotonic gains with more data. Across models and protocols, performance generally in- creases as the training fraction grows.
2. Linear probing is consistently weakest. Linear-probed foundation models are the worst per- formers across training ratios and protocols.
3. Strong competitiveness of classic NNs. Classic neural decoders remain highly competi- tive-often statistically better than foundation-model variants when labeled data are scarce. Aside from the LOO Drop protocol, classic decoders also tend to exhibit larger performance drops after fine-tuning.
4. Best model comparison. When comparing the top model from each decoder group (panel b), classic decoders frequently have higher mean, but differences from fine-tuned foundation mod-
59

---

Published as a conference paper at ICLR 2026
Ownn P
Dece: PO
Decn PEI
Delen PC
Oecoy PC3
Cwry FC4
Quapp
Quep Ca
De
Figure G.28: Attention topographical visualization for the LabraM model using fine-tuning on right- the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
hand motor imagery data from
147
-165
Denr
Own
Oven FC2
Dery e
Ocrm CI
Figure G.29: Attention topographical visualization for the BIOT model using linear probing on left- hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
els are typically not statistically significant. In low-data regimes, the best fine-tuned foundation models sometimes attain a higher mean, again without significant difference relative to classic decoders.
60

---

Published as a conference paper at ICLR 2026
Chappy
Cwe
-541
Ouiyy
OWI
Quan OS
-00
Figure G,30: Attention topographical visualization for the BIOT model using linear probing on right-hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
165
C2
Ouee: DP
Gan F1
Dery PCA
De
-00
Denry
Owry
402
Figure G.31: Attention topographical visualization for the BIOT model using fine-tuning on left- hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
Oeey
Orey PCL
Duay 21
Owr (P)
PCZ
-001
Figure G.32: Attention topographical visualization for the BIOT model using fine-tuning on right- hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
61

---

Published as a conference paper at ICLR 2026
Ouam
Cuers
Quers
Quappe Pt
Cergir
Uhepp CE
Cergg
K35
Guep
Ceigur
De
Derrn OZ
Figure G.33: Attention topographical visualization for the EEGPT model using linear probing on left-hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
Thirep
Sp Pab
-35
TWROUT TE
ENIGUIS
430
630
Oven
Cerorte PM
Opeer OR
Figure G.34; Attention topographical visualization for the EEGPT model using linear probing on right-hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
Qucg-E PN
Dlny PA
QUNYTT
Carq
Oues FD
OWIDO1
QueS
Figure G.35: Attention topographical visualization for the EEGPT model using fine-tuning on left- hand motor imagery data from the BCI-IV-2A dataset. The topographic map shows attention scores averaged across all heads and temporal positions, with all layers aggregated.
62

---

Published as a conference paper at ICLR 2026
Quep FR
MUND TV
Owes: IN
Ouen FO
Carss
Figure G.36: Attention topographical visualization for the EEGPT model using fine-tuning on right- hand motor imagery data from averaged across all heads and temporal positions, with all layers aggregated.
the BCI-IV-2A dataset. The topographic map shows attention scores
LOO Zere-Shit
LOO Fine-Tone Tone
LO0 Drop
Pee-Sabject (Sc10
Per-Subjec (Tramseer)
0.15
Balaccd Aucurazy
Dslancel AllaacelAloooe Aucrrsc
0.
Dilane Avenrayy
Oxinced Acceracy
Balancal Accuracy
0.40
Simple Complex
0.6
0.10
0.6
0.05
0.4
0.00
CBraMod
STREGI ar oemers
ERGPT
CReaMedd EUGIT
ST P.EGGmmmms
CBraMed
ELGITE ST-EEG enason4
CBraMed
ST DEEEA Comees
ELGTT
CRraMed SUCPE
ST STEEGLOme PrEeetmmee
LO0 Zere-Shet
LO0 Fine-Tum
LO0 Drop
Sar-Stbject (Sein)
Per PerSSajjcct (Tranclee)
Balancdd Acenacy
0,6
Balreed Aucuracy
Q.6
Uninnced Accncccy
0.6
Accummy Accuney
0,35
Simple
Compplex
0.4
Q.4
Batroeed
0.1
0.39
0.25
0.2
0.2
0.2
CBuahfod
ST. BF-BBG oroncos
FEGPP
CBraMed PECAT SEEEOWOO OrEeee ormsess
CRoaMed RECPT
ST. ST-BRRLIFFrRRS COTEUFFOORR
Cinaalad
ESOPT STEEUUFOORC Foneet
C'oaMedd EERPP 5T. CEAEI
Figure H.I: BCI-I-22A ablation of token fusion and classification head strategies for CBraMod, EEGPT, and ST-BEGFormer-s. Simple (red and purple): average-token pooling + linear head. Complex (light red and purple): default head for EEGPT and CBraMod; for ST-EEGFormer-s, full-token two-layer head. Top row: fine-tuning; bottom row: linear probing. Wilcoxon signed-rank test: x**, 0.001, ":: p < 0.01, ": < 0.05.
63

---

Published as a conference paper at ICLR 2026
LOO Zere-Sbet
LOO Fine- Tone
LOO Drop
Per Subject (Sein)
Pee Subject (Transter))
in
1.0
Balance Acuncy
Palumodl Acrunry
Balaned Acuneyy
Balanod BalloodAAccuue Accuney
Simple
0,7
0.6
0.0
0.6
CBesMaa
REGHT ST-EE STrEEGoomer Commers
CRablad
STEETT comtons
BEGTT
CResMed Eigrr
STEEL SPEELRSMEEE Comeees
CReaMaad
BEGHT SEREGG aroens
CRublad
ST STEE crmeess
EE-227
1.0 Zere-Shet
LO0 Fine-Tune
LO0
Per-Subject
Subjetb ('Iranster)
1.0
Simple
Natuncat Acsumay
Bahasoot Avsurayy
Bahncod Accunss
0.8
Q.1
0.8
Baluncal
0.7
Balnncdd
0.6
0.5
0.5
CRoMMd D8 DEGPT
ST STT.RFoom Fommrss
(BoMMdd ST. ST.EECO OOTREE
CReaMed CPT
CBaMMdd
EEGPT
CHiaMod
DEGPP EBrEeemets
ST.
Figure H.2: ERN ablation token fusion and classification head strategies for CBraMod, EEGPT, and ST-EEGFormer-s. Simple (red and purple): average-token pooling + linear head. Complex (light red and purple): default head for EEGPT and CBraMod; for ST-EEGFormer-s, a full-token two-layer head. Top row: fine-tuning, bottom row: linear probing. Wilcoxon signed-rank test: ***;
< 0.001, *": < 0.01, ::p 0.05.
64

---

Published as a conference paper at ICLR 2026
(a)
LO0 Zero Shot
LO0 Fine- Tune
LO0 Drop
Balanod Balaxod/Aseurryy Accurayy
0.8
Accunaye
1,0
Balanced Accunay
0,3
0..6
0.6
0.4
0.4
0.0
0.0
20
40
Fine Tone Dara Ratio (16)
Fine Tone Daca Ratio (16)
Fine Tome Data Rasio (%)
Per-Subject (Self)
Pan-Suhject (Transfer)
Balaxed Accuncy
0.8
Balrood Accuncy
0.6
0,3
0.2
Fine Tune Ratio (%)
Fine Tune Data Ratio
(b)
LO0 Zero Shot
LO0 Mune
LO0
0.8
1,0
Balunced Aocuncy
Balanced Accurazy
0.3
0.8
0.6
0.2
0.6
0.1
0.4
20
80
Fine
Per-Subject (Sei)
Per-Subject (Transfer)
Balnned Accuney
0.8
0.45
0.4
0.30
0.25
40
40
20
60
Fine- Tonne Data Ratio (%)
Fine- Trmee Data Ratio (%))
Figure H.3: BCI-IV-2A results when varying the training fraction from 20% to 80% under five evaluation protocols. (a) Group-level comparison: classic neural decoders (green), linear- linear-probed foundation models (purple), and fine-tuned foundation models (red). Boxplots reflect balanced ac- curacy over all test subjects. (b) Best-performing model from each group (model names annotated). Wilcoxon signed-rank test with Bonferroni correction: ***; p 0.001, -*: p < 0.01, 0.05.
65

---

Published as a conference paper at ICLR 2026
LO0 Zero-Shot
LO0 Fine Tume
LOO Drop
Baloncal Accnncy
Balnnced Accuncy
1.0
0.2
0.1
0.8
Balanced
0.6
Fine- Tume Data Ratio (*5)
Fine: Tune Data Ratio (9%
Fine- Tune Data Ratio (96
Sur-Sueject (Seli)
Per-Subject (Transfor)
Belaned Acairey
Balntedd Accency
1.0
0.8
0.6
0.6
40
60
80
20
400
641
801
Fiue-Tuse Dota Ratio (56)
Fine- Tune Data Ratio (%)
(b)
1.00 Fine- Tune
Drop
1.10
Ralavcdd Relancal Acciaay
Balnoed Accuncy
Acnuary
0,2
1.05
1.00
0.0
20
60
80
20
400
20
40
60
80
Fino Tume Data Ratio (%)
Ratio (%)
Fliee Tume Data Ratio (%)
Per-Subject (Self)
Per-Subject (Trunsfor)
Balnncal Aseuncy
Dalnced Aceency
0.
0.3
0.9
07
0.7
20
40
60
Fino- Fino-Trae Data Ranio (96)
Fine-Tlune Data Ratio (96
Figure H.4: ERN results when varying the training fraction from 20% to 80% under five evaluation protocols. (a) Group-level comparison: classic neural decoders (green), linear-probed foundation models (purple), and fine-tuned foundation models (red). Boxplots reflect balanced accuracy all test subjects. (b) Best-performing model from each group (model names annotated). Wilcoxon
signed-ran -eened-ranka test with Bonferroni correction: "**: p < 0.001, **; p 0.01, *; p < 0.05.
66


Reviews:
[{"review_id": "UoLVfSaBnH", "reviewer": "Reviewer_EwsP", "summary": "This paper highlights the importance of comprehensive evaluation—an aspect often overlooked in previous studies—when assessing EEG foundation models. The authors emphasise that fair comparison with classical methods, diverse train–test splitting strategies, and statistical testing of performance differences are essential to truly assess progress in this field. They systematically evaluated five popular EEG foundation models and uncovered meaningful relationships between model performance, downstream tasks, and evaluation protocols, revealing that the performance gains over classical approaches remain limited. Furthermore, they proposed a robust EEG foundation model that serves as a strong baseline and underscored the need for more diverse and large-scale EEG datasets to enable more reliable evaluations.", "questions": "1.\tOnly 5 representative EEG foundation models are selected. Would the conclusions generalise to the large body of EEG foundation models not tested in this evaluation study?\n2.\tLine 89 (section 2) mentions that many studies on EEG foundation model lack comparison to “compact neural network decoders.”  However, the well-cited foundation models -- for example, the one listed in Table C.1 --  are benchmarked against classical neural network models, including both general time-series models and EEG-specific models. Could you please clarify which types of baseline NN models are considered under-explored in existing EEG foundation model research?\n3.\tThe difference between variants of STEEGformer -- STEEGformer-s, STEEGformer-b, and STEEGformer-l, is not clearly stated in the paper. Could you please explain the differences?\n4.\tThe use of the phrases “significant” and “statistically significant” may be slightly misleading to readers in Section 4.3. This could be improved by replacing the non-statistical “significant” with other expressions such as “notable,” or “considerable.”\n5.\tHow are the model sizes for EEG Foundation models in Figure 5 (a) calculated? Are the parameters of classification / regression heads included?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "This paper is the first study in EEG Foundation literature with systematic comparisons and standardised the evaluation protocols. It addresses the fundamental question of whether pre-training large-scale foundation models on extensive EEG corpora truly benefits EEG signal decoding. It also serves as an important reminder to researchers in the field not to blindly upscale models—an approach that often leads to wasted resources—and encourages future studies to pursue more reliable and reproducible results without selective reporting.\n\nQuality and Clarity: \n- The comparative analysis is comprehensive, with evaluation protocols that cover most considerations relevant to real-world BCI applications. Fair comparisons are conducted using matched metrics, training strategies, and model sizes.\n- Rationale of most strategic selections are clearly stated in the body or appendices. (e.g., selection of evaluation protocols and pre-training corpus of ST-EEGFormer)\n- The Result section (Section 4) is well-organised, addressing important questions related to foundation models.", "weaknesses": "1.\tA brief description of the train–validation–test splitting strategy (e.g., whether data were randomly shuffled or split by leaving entire trial sessions out) could be added to Section 3.1, as this choice may influence the performance of models.\n2.\tThe scaling law of EEG foundation models has so far been examined only with respect to model size (i.e., the number of parameters). However, the volume of pre-training data is also a key factor contributing to the computational footprint and could influence the overall performance. Including an investigation of scaling behaviour with respect to dataset size would provide valuable insights and better inform the design of future EEG foundation models.\n3.\tThis paper encourages the community to develop better EEG datasets, but it relies on vague descriptions such as “large-scale,” “diverse,” and “standardized.” These terms are not clearly defined, and the paper lacks elaboration on how greater diversity and standardisation in EEG datasets could specifically address the bottlenecks of EEG foundation models identified in the comparative analysis.\n4.\tLine 939 of Section C.9 highlights the importance of regression tasks; however, only one regression task is included in the evaluation.\n5.\tThe joint visualisations of all six protocols in Figures 3 and 4 appear somewhat visually cluttered. Since each protocol or pair of protocols evaluates different aspects of the EEG decoding models, selectively presenting the most relevant ones would enhance clarity and allow readers to interpret the results more easily.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HHdKkrEouh", "reviewer": "Reviewer_Jg9y", "summary": "This paper presents a comprehensive benchmark of EEG foundation models against traditional neural and non-neural decoders across diverse BCI tasks and evaluation protocols. The authors introduce ST-EEGFormer, a Vision Transformer-based model pre-trained with masked autoencoding on over 8 million EEG segments. The study finds that while fine-tuned foundation models perform well in data-rich, population-level settings, they often do not significantly outperform simpler neural networks or classical methods in data-scarce or subject-specific scenarios. Linear probing consistently underperforms, and no clear scaling law is observed. The work emphasizes the need for large-scale datasets and rigorous, reproducible benchmarking.", "questions": "1.  **Data Transparency:** The extensive use of distribution plots provides a excellent overview of model performance, but makes it difficult to extract precise numerical results for community reference and future benchmarking. Could the authors provide a supplementary file or table with the detailed, per-dataset numerical results (e.g., accuracy, MSE, Pearson R) for each model under the different evaluation protocols? This would greatly enhance the reproducibility and long-term utility of this valuable benchmark.\n\n2.  **Ablation on Token Fusion Strategy:** The paper identifies the token fusion strategy (e.g., flatten-all vs. average) as a critical hidden implementation factor. Given its potential impact, have the authors conducted a systematic comparison of how different fusion strategies affect the performance of various foundation models (like EEGPT and CBraMod) across different types of downstream tasks (e.g., motor imagery vs. emotion recognition)? Such an ablation study would be highly insightful for the community to understand the interaction between model architecture, fusion strategy, and task characteristics.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "*   **Comprehensive and Rigorous Benchmarking:** The paper provides an exceptionally thorough evaluation, comparing a wide range of EEG foundation models against both classic neural networks and non-neural baselines across six diverse tasks and six distinct evaluation protocols, supported by extensive statistical testing.\n*   **Introduction of a Strong, Transparent Baseline:** The proposed ST-EEGFormer model serves as a valuable and reproducible baseline, effectively demonstrating that a simple MAE pre-training strategy on raw EEG can achieve competitive performance, challenging assumptions from prior work.\n*   **Critical and Actionable Findings:** The study delivers nuanced, evidence-based conclusions that question the universal superiority of foundation models, highlighting their limitations in data-scarce settings and the weak performance of linear probing, which provides crucial guidance for future research directions in the field.", "weaknesses": "**Limited Scope of Downstream Task Evaluation**: The selection of seven downstream datasets, while diverse, may not be fully representative of the broad spectrum of EEG applications. The benchmark omits several common and challenging tasks such as emotion recognition (e.g., FACED), sleep staging, and seizure or depression detection, which are frequently used to evaluate foundation models. Furthermore, the chosen tasks like ERP and SSVEP often have simple, stereotypical patterns that can be decoded effectively by classical non-neural methods, potentially skewing the conclusion that foundation models offer limited advantages. The low overlap with the evaluation benchmarks from existing foundation model papers also raises concerns about the generalizability of the findings across the field's common evaluation practices.\n\n**Concerns over Data Transparency for Community Reference**: The heavy reliance on distribution plots (e.g., violin plots, box plots) to present aggregated results, while useful for visualizing statistical distributions, limits the transparency of the raw results. This makes it difficult for the community to directly reference specific performance numbers (e.g., exact accuracy on a specific dataset) or to perform alternative meta-analyses. Providing detailed numerical results in supplementary tables for each model-dataset-protocol combination would enhance reproducibility and utility for future comparative studies.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KiqVlTYfXe", "reviewer": "Reviewer_ABR4", "summary": "This paper offers a systematic, large-scale comparison of EEG foundation models against classic neural and non-neural decoders across multiple datasets, tasks, and six evaluation protocols. This paper also introduces a baseline, ST-EEGFormer (a ViT trained with MAE on ~8M raw EEG segments). This work systematically summarizes how different types of models perform under various evaluation protocols and draws a series of performance-related conclusions.", "questions": "1. The six “evaluation protocols” described in this work do not always align with commonly used paradigms or terminology in EEG research—for example, the notions of “population” and the specific zero-shot configurations. To my knowledge, widely adopted paradigms include subject-dependent, subject-independent, cross-subject, cross-session, and cross-dataset. Approximately, your (2) within-subject seems to correspond to subject-dependent; your (4) leave-one-subject-out zero-shot to subject-independent; and your (3) per-subject zero-shot to cross-subject. However, other mainstream settings I mentioned appear to be missing, and some of your configurations—such as (5) and (6)—do not have clear counterparts in established experimental practice. Moreover, the terminology for all protocols is presented without supporting citations. I recommend clarifying the provenance and rationale of each protocol, aligning your naming with standard conventions (or explicitly motivating any deviations), and adding appropriate references.\n\n2. The claim that this work is the “first comprehensive benchmark” does not seem justified; comparable efforts already exist—for example, Adabrain-bench [5].\n\n3. The pretraining paradigm and architecture of ST-EEGFormer are not novel, as closely related designs already exist, thus this model lacks originality. \n\n4. Moreover, while ST-EEGFormer-s outperforms works like LaBraM and BENDR overall—and ST-EEGFormer-b and -l achieve further gains—I suspect these improvements appear largely attributable to substantially larger model sizes. For example, ST-EEGFormer-s has 32.7M parameters compared with only 5.8M for LaBraM-base, and ST-EEGFormer-b and -l scale up to 110.9M and 328.4M parameters, respectively. This also raises my next question.\n\n5. In Section 4.5 the authors write: “although there is a slight upward trend in normalized accuracy with increasing model size, the poor logarithmic fit suggests that a clear scaling law does not exist for downstream EEG classification tasks.” I believe this analysis is problematic. The comparison mixes models of different sizes and different architectures rather than holding the architecture fixed, yet architectural choices are known to have a large impact on EEG performance; prior classic neural-network baselines for EEG have clearly demonstrated the sensitivity to model design. As a result, the paper’s conclusion about the absence of a scaling law is insufficiently rigorous. Moreover, within the authors’ own ST-EEGFormer family, a clearer scaling trend appears: moving from the small model (32.7M) to the base (110.9M) and then to the large (328.4M) yields consistent performance gains. This, if anything, suggests the presence of a scaling law and contradicts the stated conclusion.\n\n6. The figures are difficult to read—especially the radar charts, which include too many elements to distinguish clearly. While I appreciate the substantial scope of the work, the visual presentation should be improved for clarity.\n\n7. If positioned as a benchmarking suite, the current coverage of models and datasets is relatively limited and leaves ample room for improvement. Mainstream EEG tasks—such as seizure detection, emotion recognition, and mental workload detection—should be included, and additional foundation models (e.g., EEG2Rep [3]) as well as classic deep-learning baselines (e.g., LGGNet [6]) should be incorporated.\n\nIn sum, I agree the authors’ call to build a more cohesive and standardized EEG community; however, this advocacy cannot substitute for substantive novelty and technical contributions in the present work.\n\n[6] Ding, Yi, et al. \"LGGNet: Learning from local-global-graph representations for brain–computer interface.\" IEEE Transactions on Neural Networks and Learning Systems 35.7 (2023): 9773-9786.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper delivers a systematic, comprehensive analysis spanning EEG foundation models, neural network–based approaches, and classical baselines. The empirical workload is substantial and reflects significant effort. It also provides a clear synthesis with thorough descriptions of existing models, and the manuscript is well written and highly readable.Its calls to the community are measured and well-judged.", "weaknesses": "Despite the substantial effort and extensive scope, the work lacks originality. As a benchmarking study, it largely aggregates and uniformly evaluates existing open source methods rather than introducing genuinely new ideas. Although the paper proposes ST-EEGFormer, the architecture amounts to a straightforward Transformer combined with MAE-style pretraining—a paradigm already widely used in EEG self-supervised learning (e.g., EEGPT [1], CBraMod [2], EEG2Rep [3]). Similarly, the spatio-temporal Transformer concept has ample precedent (e.g., CBraMod [2] , Brant-2 [4]). In addition, the manuscript contains several inappropriate or insufficiently rigorous elements (see Questions for details). Taken together, I do not consider this submission a good fit for ICLR; it reads more like an engineering report than a research contribution. Moreover, comparable benchmarking studies already exist such as Adabrain-bench [5], which is open source , so this is not the first of its kind.\n\n[1] Wang, Guangyu, et al. \"Eegpt: Pretrained transformer for universal and reliable representation of eeg signals.\" Advances in Neural Information Processing Systems 37 (2024): 39249-39280.\n\n[2] Wang, Jiquan, et al. \"Cbramod: A criss-cross brain foundation model for eeg decoding.\" arXiv preprint arXiv:2412.07236 (2024).\n\n[3] Mohammadi Foumani, Navid, et al. \"Eeg2rep: enhancing self-supervised eeg representation through informative masked inputs.\" Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024.\n\n[4] Yuan, Zhizhang, et al. \"Brant-2: Foundation model for brain signals.\" CoRR (2024).\n\n[5] Wu, Jiamin, et al. \"Adabrain-bench: Benchmarking brain foundation models for brain-computer interface applications.\" arXiv preprint arXiv:2507.09882 (2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ajrqFj5zkz", "reviewer": "Reviewer_gwxQ", "summary": "The paper introduces a benchmark to compare EEG foundation models and baseline deep neural networks. It also introduces a ViT based model that is used to compare the other foundation models with.", "questions": "1. What is the main purpose of the paper and if it’s just benchmarking what’s the novelty compared to [1] ?\n2. How about the reconstruction ? Any examples here. \n3. An interesting addition to distinguish yourself from [1] would be an analysis of the number of training samples during deep baselines training and foundation model fine tuning. For instance, if currently 100 samples (all) are used for training of both models and the performance is similar how about if you use 10-20-40-60-80 random samples (over multiple sampling) for deep baselines training and foundation model fine tuning. Do the foundation models have any advantage there ?\n\n[1]: Na Lee, Konstantinos Barmpas, Yannis Panagakis, Dimitrios Adamos, Nikolaos Laskaris, & Stefanos Zafeiriou (2025). Are Large Brainwave Foundation Models Capable Yet ? Insights from Fine-Tuning. In Forty-second International Conference on Machine Learning.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "The paper is very well organised. The authors provide a plethora of experiments, comparisons as well as ablation studies. These experiments range from various models, experimental setups (e.g. how the various ways one can treat training subjects) and classification / regression tasks. All of these make the analysis meticulously thorough.", "weaknesses": "1. Although very thorough, I fail to see the novelty behind the paper. For example, a recent work [1], although it covers less in experimental results, it comes across the exact same observations / conclusions. One could argue that this newest work extends [1] but its insights do not seem novel. \n2. The main message of the paper is also complicated. Is it a benchmarking paper or a new model (in this case ST-EEGFormer) introduction ?\n3. The authors challenge LaBraM’s claim that direct MAE in EEG signals is ineffective but they fail to provide any evidence against it. For example, a comparison between reconstruction capabilities of LaBraM’s tokenizer and ST-EEGFormer would be useful here.\n\nWriting:\nThe paper is well-written.\n\nOverall:\nThe paper is clear and with insightful observations / results but the main message or its novelty need to be clearly decided.\n\n[1]: Na Lee, Konstantinos Barmpas, Yannis Panagakis, Dimitrios Adamos, Nikolaos Laskaris, & Stefanos Zafeiriou (2025). Are Large Brainwave Foundation Models Capable Yet ? Insights from Fine-Tuning. In Forty-second International Conference on Machine Learning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)