REVE: A Foundation Model for EEG - Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
Authors: Yassine El Ouahidi
Jonathan Lys
Philipp Thölke
Nicolas Farrugia
Bastien Pasdeloup
Vincent Gripon
Karim Jerbi
Giulia Lioi
Venue: NeurIPS 2025 poster ()
Keywords: Foundation Model
EEG
SSL
BCI
Primary area: neuroscience_and_cognitive_science
TL;DR: A scalable EEG foundation model leveraging 60,000+ hours of data, adaptable to any electrode setup, offering ready-to-use embeddings and state-of-the-art performance across diverse tasks.

Abstract: Foundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode configurations. Existing EEG foundation models struggle to generalize across these variations, often restricting pretraining to a single setup, resulting in suboptimal performance, in particular under linear probing.
We present REVE (Representation for EEG with Versatile Embeddings), a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces a novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement. Using a masked autoencoding objective, we pretrain REVE on over 60,000 hours of EEG data from 92 datasets spanning 25,000 subjects, representing the largest EEG pretraining effort to date.
REVE achieves state-of-the-art results on 10 downstream EEG tasks, including motor imagery classification, seizure detection, sleep staging, cognitive load estimation, and emotion recognition. With little to no fine-tuning, it demonstrates strong generalization, and nuanced spatio-temporal modeling. We release code, pretrained weights, and tutorials to support standardized EEG research and accelerate progress in clinical neuroscience.

REVE: A Foundation Model for EEG Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
Yassine El Ouahidi Jonathan Lys', Philipp Thölke? Nicolas Farrugia* Bastien Pasdeloup', Vincent Gripon', Karim Jerbi2,3,4 Giulia Lioi
IMT Atlantique, LaCCSTICC, UMR CNRS 6285, F-29238 Brest, France 2 Psychology Department, Université de Montréal, Montreal, QC, Canada Mila (Quchec AI research institute), Montreal, QC, Canada UNIQUE (Quebec Neuro-Al rescarch center), QC, Canada
Abstract
Foundation models have transformed Al by reducing reliance task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode configurations. Existing EEG foundation models struggle to generalize across these variations, often restricting pretraining to a single setup. resulting in suboptimal performance, in particular under linear probing. We present REVE (Representation for EEG with Versatile Embeddings). a pretrained model explicitly designed to generalize across diverse EEG signals. REVE introduces novel 4D positional encoding scheme that enables it to process signals of arbitrary length and electrode arrangement. Using a masked autoencoding objective, we pretrain REVE over 60,000 hours of EEG data from 92 datasets spanning 25,000 subjects, representing the largest EEG pretraining effort date. REVE achieves sfate-of-the-art results on 10 downstream EEG tasks, including motor imagery classification, seizure detection, sleep staging, cognitive load estimation, and emotion recognition. With little fine-tuning, it demonstrates strong generalization, and nuanced spatio-temporal modeling. We release code, pretrained weights, and tutorials to support standardized EEG research and accelerate progress in clinical neuroscience.
Introduction
Electroenncephalograpyy (EEG) is non-invasive technique widely used study brain activity, with applications spanning brain-computer interfaces (BCIs), clinical diagnostics, and neuroscience research. Despite its potential, the adoption of EEG-based technologies remains limited (Lotte et al. 2018). A key challenge is developing models that generalize effectively to new subjects. EEG data varies widely in electrode configurations, recording conditions, and subject-specific factors. complicating model transfcrability. This heterogeneity has led to a fragmented ecosystem of datasets and task-specific models, many of which struggle to generalize across settings.
Foundation models have transformed natural language processing (Achiam et al. 2023 Dubey et al.
2024 Warner al. 2024) and computer vision (Radford al. 2021 Caron 202 Kirillov
et al. 2023) by leveraging large-scale pretraining to enable transfer with minimal supervision. Their
Cooresponniig authors: yassine wwwp://wwasseeeeeeeeaiiieeeeeede www..hhiidhsiiiiirr.aallll. ai, giulia. wtw.ooeemmmaaaaatiuu....... Projecc page:
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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ability to produce general-purpose purpose representations has sparked growing interest in building similar
models for EEG Yang et al. 2024; Wang et al. 2024b] Jiang et al. 2024; Cui et al., 2024] Yuan
et al. 2024b Wang et al. (024a)) Yet, EEG poses unique challenges including data heterogeneity,
low signal-to-noise ratio, and the lack of standardized positional encoding to accommodate varying
electrode configurations.
Recent EEG foundation models such BIOT (Yang et al. 2024). Labram (Jiang et al. 2024). CBraMod (Wang et al. 2024b), and NeuroGPT (Cui et al. 2024) adopt self-supervised learning (SSL) techniques for pretraining. While promising. many of these models rely solely on the TUH database (Obeid and Picone 2016) which uses fixed 19 or 21 -channeel montage. As result, they often fail to generalize datasets with different electrode layouts or recording setups. Furthermore, existing positional encoding schemes, whether absolute (Yang et al., 2024] Diang et al.) 2024) or convolutional (Wang al. (024b), lack the flexibility to accommodate spatial diversity, often necessitating full fine-tuning for transfer.
To address the limitations in current EEG foundation models, we consider three core contributions that enable scalable, generalizable representation learning across diverse, large-scale EEG datasets.
First, we propose a novel 4D positional encoding scheme that enables flexible modeling of EEG signals with varying temporal lengths and electrode configurations. Unlike existing absolute or con- volutional encodings,
formulation naturally supports spatial and temporal variability, eliminating the need for fixed montages or fine-tuning of positional priors.
Thanks to this flexible positional encoding method, we are able to train with a wider range of EEG configurations, allowing to scale to larger and more heterogeneous datasets. To this end, we curate the largest and most diverse EEG corpus to date, comprising over 60,000 hours of data from 92 datasets and 25,000 subjects. This diverse collection spans climical, BCI, and research domains, providing the scale and diversity necessary for robust pretraining.
Combining architectural flexibility with large-scale data results in REVE (Representation for EEG transformer model trained with modified masked with Versatile Embeddings). a objective that promotes learning better representations in the autoencoder (MAE) (He al. 2022) model. REVE learns general purpoose EEG representations that transfer effectively across wide range of downstream tasks.
REVE achieves state-of-the-art performance across numerous benchmarks, including BCI and clinical datasets, outperforming prior EEG foundation models. Our scaling studies further show improved generalization with larger model sizes, reinforcing the benefits of large-scale pretraining. To support adoption, we release open-source code, pretrained models of multiple sizes, and detailed tutorials for applying REVE to various EEG tasks. By addressing the unique challenges of EEG with scalable architectures and flexible spatial encoding, REVE establishes unified foundation for EEG analysis and paves the way for new advances in neuroscience and clinical applications.
2 Methods
We pretrain encoder using masked autoencoder objective. The REVE encoder consists of a patch embedding module, a 4D position encoding module, and a transformer backbone. During pretraining, apply spatio-temporal contiguous masking the patch embeddings and jointly train the encoder and decoder to reconstruct the missing segments of EEG, enabling the encoder to learn robust feature representations. Subsequent hyperparameter values are listed in Table 5 in the Appendix.
2.1 EEG Representation and Block Masking strategy
We represent multi-channel EEG data as X E RCT, where C' is the number of electrodes and T the number of time samples, electrode positions given by P E RCx3, corresponding to their 3D
coordinates. To process the data, we segment each channel into patches size w with overlap o, following BIOT Yang et al. 2024). This yields p w-o
T-w
+ +1[(T w) mod (w - o) + 0] non-
overlapping patches (discarding any incomplete ones), and reshapes into Xp E C'xxx Each patch is lincarly embedded, resulting in E Cxxx where Dg is the embedding dimension.

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Sacenday Lous
50 Tuspeed Dacoliad
Attention thadhe T
Reco arair
Primary
Desader
Reccntincrion
Leurmabie (MASE
Hod Marking
Spk inoo /anke///erraaeeee putches
Jon mo maddaa////mmmeeeee enthoddingg
Reylave with lncraatle [MtoOk] token
Beoben Mask
Figure 1: Overview of the REVE pretraining framework. The model processes multi-channel EEG data through linear Patch Embedding where signals are divided into overlapping temporal patches for each channel and embedded with linear layer. 4D Spatio-Temporal Position Encoding combines spatial coordinates of electrodes with temporal patch indices, augmented with noise for robust generalization. A Block Masking Strategy masks contiguous regions across spatial and temporal dimensions to simulate realistic disruptions. The transformer encoder processes unmasked embeddings. Updated embeddings are joined with learnable placeholders for the masked tokens, from which raw EEG is reconstructed using the decoder. The Primary Task predicts raw EEG signals directly, while the Secondary Task trains a single global token via attention pooling to summarize the input. Both tasks minimize an L1 reconstruction loss.
To enhance learning during pretraining, we apply a joint spatio-temporal block masking strategy that masks structured regions across both spatial and temporal dimensions. Random masking, proposed for EEG by Chien et al. (2022), was later improved through spatial masking (Mohammadi Foumani ct al. 2024: Guetschel et al. 2024p. In this work, we extend the masking strategy to the temporal domain. This builds on insights from image modeling, where structured masking outperforms random masking (Xie et al. 2022), trend also supported by ablation results (Table 18 Appendix). As neighboring segments of EEG, in both spatial and temporal domain, are typically similar, naive random masking could leave redundant information exposed, reducing the difficulty of reconstruction. In contrast, block masking better disrupts these patterns, encouraging more effective learning.
Our block masking strategy is governed by the following parameters: The masking Ratio M, controls the overall proportion of masked tokens. The spatial Masking Radius R2 and Temporal Masking Radius RG respectively define the spatial extent (around selected channel) and the time window (around a selected token) to be masked. Similarly, the Dropout Ratio D, sets the fraction masked tokens for which the entire time series of the corresponding channel is dropped, while the dropout Radius R, determines the spatial neighborhood affected by dropout. For tokens not dropped, temporal masking is applied within radius R2 This process yields binary mask B RCX, containing Nm M,) C pl masked entries (zeros) and Nm = C. p - Nm unmasked entries (ones).
2.2 4D Position Encoding Strategy
Unlike prior works that rely on learned embedding tables for spatial encoding vectors (Tiang et al.) 2024; Wang et al.] 2024b), we directly generate position encodings from the spatio-tempora coor- dinates of the tokens, allowing the processing of signals of any length EEG layout and enabling better generalization to unseen setups. More specifically, our method uses a transformation applicable each position, utilizing the actual 3D coordinates and timestep of each EEG patch, enabling the model to handle arbitrary electrode configurations and sequence lengths without relying on learned embeddings.
4D Positional Encoding and Spatial Augmentation. We start with the spatial positions of the EEG electrodes P E Cx3, where each row of P contains the (a, y. =) coordinates of a channel, to which we add Gaussian noise with standard deviation moise. This improves generalization to diverse electrode positions and ensures robustness to variability in head size or electrode placement. We extend P with temporal component, resulting in Poxt E IR Cxpx1 where p is the number of
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patches obtained from segmenting EEG signal, as defined in Section 2.1 The temporal dimension is represented as discrete values from p. scaled by a factor s, to ensure a scale similar the spatial dimensions.
4D Fourier-Based Position Encoding. Building on the 2D approach proposed by Défossez et al. (2023). we extend the Fourier positional encoding method 4D in our encoding strategy, follows. Each positional component (2t, y, 2, 1) of Pex is projected into a multi-frequency space, using Wree frequencies per dimension. The frequency assignment follows Cartesian product structure, i.e,, all combinations of frequencies across the four dimensions contribute to the encoding, resulting in a flattened vector of dimension miey. A hierarchical periodicity emerges: the period of is Te of y is mis of is Miree and t is "frreg' Then, applying sine and cosine transformations doubles the embedding size, producing a positional vector of dimension 2 nfregg We ensure that the embedding dimension matches the hidden size required by the 4DPE module, with Arreyy E {3, 4, 5, resulting in the final embedding F pe E RCxxxxDE The 4D encoding adds minimal compute overhead, with sinusoidal computations and a small linear layer. Computational cost scales linearly with the number of input tokens (channels x temporal patches) and is negligible relative to the transformer backbone.
Final Adjusted Position Encoding. To complement the fixed Fourier features, we also process P Pext through a linear layer followed by GELU (Hendrycks and Gimpel 2016) and LayerNorm (Lei Ba et al. 2016), producing a learnable representation Fin E RCxpx This component adapts the positional encoding the specific dataset and task, and can compensate for any truncation in the Fourier basis. The final positional encoding is given by Pem. eme = LayerNorm(F pe + F Fim), combining the structured inductive bias of Fourier features with the flexibility of learned adaptation. This vector is added to the non- masked patch embeddings before being passed to the transformer encoder similarly to MAE (He et al., 2022). and consistent with standard absolute positional encoding practices [Vaswani 2017). The ablation study in Table [9 confirms that this method outperforms both fixed learnable and purely MI.P-based positional encoding schemes.
2.3 Transformer
Our model extends the standard Transformer architecture (Vaswani 2017) with enhancements that improve efficiency and stability. We use RMSNorm (Zhang and Sennrich 2019) in lieu of LayerNorm as normalization layer for better training stability, and choose GEGLU (Shazeer 2020) as the activation function in the feed-forward network (FFN) layers as it outperforms standard GELU through more expressive gating mechanisms (Geiping and Goldstein 2023). This choice is further supported by the ablation results in Table 20 Our FFN layers follow a two-layer structure with an expansion ratio of 3. consistent with designs from LLaMA (Touvron al. 2023). Qwen (Bai et al. 2023 or Mistral (Jiang et al. 2023). Following Dayma et al. (221)). we remove bias terms from all linear layers except the final decoder layer. This reallocates the parameter budget to linear transformations, improving efficiency. We use Flash Attention v2 (Dao) 2024) for memory and computational efficiency in the attention as reduces the softmax overhead and ensures scalability to long sequences, while maintaining the core transformer formulation.
2.4 Masked EEG Reconstruction Methodology
During pretraining, our model reconstructs EEG signal of masked patches using information from the visible, unmasked patches. The overall pretraining framework is illustrated in Figure 1
Let Pm E R N. and mm E N denote the masked and visible patches, respectively, with Nm and Nm defined in Section 2.1 The associated patch embeddings are denoted as Em for masked patches and Em for visible patches.
We adopt the MAE structure from He et al. (2022), with a larger encoder and a lighter decoder each following the architecture described in Section 2.3 Only the embeddings of the visible patches Em. enriched with their positional encodings are passed through the encoder, to produce latent representations F Fm- Masked patches are represented using a learned embedding, repeated Nm, times and also augmented with positional encodings. Before entering the decoder, positional encodings are re-added to both visible and masked latent patches. Together, they form the decoder input from which the raw EEG signal of the masked patches is reconstructed.

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Unlike the original MAE, which uses separate set of fixed positional encodings for the decoder, we reuse the same encoding for both the encoder and decoder. This design ensures flexibility for processing EEG signals with varying temporal lengths and electrode configurations.
The output of the decoder transformer, is passed through linear projection layer that maps latent patches back into the signal space, reconstructing the raw EEG signal of the masked patches. Reconstructed patches minimize the L, loss relative the original raw EEG patches:
C =
1
Σ iEP
P P
P
where 1 represents the reconstructed signal for patch i, and P is the original signal. We chose L1 loss over L2 due to the inherently noisy nature of EEG signals. While L2 amplifies the influence of noise, Lj loss offers greater robustness by reducing the impact of outliers.
In addition to the main reconstruction loss, we introduce a secondary task that reconstructs masked patches from compact global representation. We apply attention pooling over the outputs of all Multi-Head Attention (MHA) layers in the encoder: the output tokens (after FFN) from each MHA block concatenated and attended by learned query token. This pooled token is then repeated, enriched with positional encodings, and passed through a 2-layer FFN to reconstruct the masked patches. As with the primary loss, sum: Loss = Primary Loss + Secondary Loss
L,1 loss for reconstruction. The total loss is weighted
This secondary loss encourages the encoder to distribute useful information across all layers, mitigat- ing over-specialization in the final layer and yielding more generalizable representations.
The secondary loss mitigates a limitation of the MAE framework: the final encoder layer can overfit to the reconstruction task, especially with a shallow decoder (He al., 2022). By pooling features across all transformer layers (Alkin et al.) (024), the learned token captures a compact, global EEG representation, encouraging more balanced use of the encoder depth. This leads stronger, more generalizable features for downstream tasks like linear probing, few-shot learning, and transfer without fine-tuning.
After the pretraining phase, the decoder is discarded, and only the encoder is used. In this case, no embeddings are masked, i.e., Pn Em 0. All patches are processed as usual by retaining their associated positional encoding.
To avoid confusion regarding terminology. clarify that the terms "encoder" and "decoder" are used here in the context of masked auto-encoders (MAE), not in the autoregressive Transformer sense. All Transformer blocks in REVE are non non-causal and operate within standard encoder-stee attention pattern; no autoregressive training is involved. The "decoder" refers solely to the lightweight reconstruction head used recover masked EEG segments during self-supervised pretraining.
3 Experiments
3.1
Pretraining
This section outlines the data sources and preprocessing steps used for pretraining, followed by our strategy for scalable and effective representation learning across diverse datasets.
3.1.1 Dataset Collection & Preprocessing
To enable large-scale pretraining, we assembled massive and diverse collection of EEG recordings from open-source or accussl-accessible datasets. It comprises 19 TB of raw data, spanning 24,274 subjects, 150,833 unique sessions, and 61,415 hours of recordings drawn from 92 different sources,
including OpenNeuro (Markiewicz et al. 2021p. MOABB (Aristimunha et al. 2023) and TUH (Obeid and Picone 2016). To our knowledge, this is the largest and most diverse EEG dataset assembled for training foundation model. The most extensive prior effort, by Yuan et al. (2024a), comprised approximately 40,000 hours of recordings, but primarily relied on intracranial BEG (iEEG) rather than non-invasive EEG. A summary of the dataset composition and full list of included sources are provided in Appendix B While the majority of the data consists of clinical EEG recordings, we also include substantial subset of cognitive and BCI-related data which, although smaller in proportion,
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tend to be cleaner and more diverse. We also collected electrode positional information for each recording. When 3D coordinates were available, they were used directly; otherwise, positions were inferred from standard labels. Channels without identifiable names or positional data were excluded. The dataset spans a wide range of EEG systems and formats-including Brain Vision, BioSemi, EDF, GDF, and EEGLAB, with most recordings adhering to the 10-5 system (Oostenveld and Praamstra 2001]. In total, the dataset includes 396 unique electrode names.
Our preprocessing pipeline is designed to preserve signal diversity and prioritize robustness when scaling. We only removed recordings shorter than 10 seconds, and those used in downstream tasks. Remaining signals were resampled to 200 Hz. band-pas, filtered (0.5-99.5 Hz), and converted to float32, resulting in 6 TB dataset. To address amplitude variations across recordings, we applied Z-score normalization with statistics computed across the recording sessions to ensure robust statistics. After normalization, values exceeding 15 standard deviations were clipped, as in Défossez et al.) (2023). Unlike CBraMod (Wang et al., 2024b), which excluded signals above 100 JVV our approach retains them, resulting in about 60,000 hours of EEG, compared to 9, 9,000 CBraMod and 2,534 in LaBraM.
3.1.2
Pretraining Strategy & Scaling
3.2
Training and Scaling Strategy
We present the training procedure used for pretraining the Small model and detail how scales to larger architectures under constrained resources. Our training framework builds upon recent advances in state-of-the-art NLP methodologies (Warner et al.) 2024). We use the StableAdamm (Wortsman) et al.) 2023) optimizer, designed for low precision frameworks and improved stability, thanks to the Adafactor-style gradient clipping (Shazeer and Stern 2018 Ta of the Appendix lists the optimizer hyperparameters.
The learning rate follows a Warmup Stable Decay (trapezoidal) schedule (Hu et al., 2024), known for its robustness to learning rate variations (Hägele al.) 2024). We use linear warmup over 10% of the first epoch, followed by 80% at peak LR, and a linear decay to 1% of the maximum. Unlike one-cycle schedules that reset every epoch, our cyclic trapezoidal variant allows multiple cooldown phases across epochs, particularly beneficial for EEG training where masked token sampling introduces variability. We apply Megatron-style initialization (Shoeybi et al. 2019) with standard deviation of 0.02 for all transformer layers and the mask token, ensuring stable dynamics. Other parameters use PyTorch's default initializations.
A key factor for the success of foundation models the simultaneous scaling of both training datasets and model architectures (Touwron et al., 2023). We describe scaling methodology maximize computational efficiency and accommodate larger models within constrained resources. To scale model capacity, we adjust depth, width, and number of attention heads while maintaining fixed FFN ratio. of the Appendix summarizes these configurations. This scaling strategy enables efficient capacity expansion while preserving architectural consistency across model sizes.
Recent advances in NLP provide strong theoretical and empirical evidence for the existence of scaling laws (Kaplan et al. 2020; (Hofmmann et al., 2022), which govern the relationship between model size, training dynamics, optimization and initialization hyperparameters. We follow the power law ? xx Dan, with ap -0.90 and D the model dimension, for the learning rate, as derived in Everett et al. (2024). The optimal LR is first swept on the small model and then scaled accordingly. To efficiently train models, we use data parallelism, maintaining a constant batch size by reducing per-GPU loads for large models. A load-aware data-shuffling strategy groups samples by electrode count, shuffles within and across buckets, and balances batches across GPUs to avoid bottlenecks, for constant optimization steps and maximized throughput.
Although scaling laws exist for adjusting AdamW momentum terms (Malladi et al., 2022), our use of a constant effective batch size across models allows us to fix 31 and 32. Regarding initialization, while Hägele et al. 2024) suggests scaling Tinit X D leads us to keep inii 0.02 fixed across scales.
D-0.5 0.5, our width increase (from 200 to 1, 216)
3.3 Downstream tasks
Downstream task datasets To evaluate the performance and generalizability of our EEG founda- tion model, we perform extensive assessments across 10 diverse downstream tasks, selected to ensure

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comparability with existing models in the field. These tasks span variety of EEG-based baGed applications, including sleep staging, emotion and event classification, detection of stress and mental disorder: across the following datasets: PhysioNet-MI (Goldberger et al., 2000). BCIC-IV-2a (Tangermann al. | TUEV (Obeid and Picone 2016), TUAB (Obeid and Picone, 2016), HMC (Alvarez. Estevez. and Rijsman, (021). ISRUC (Khalighi et al. (016), FACED (Chen et al. 2023). Mum-
taz (Mumtaz 2016), Mental Arithmetic (MAT) (Zyma et al., (019). and BCI2020-IV-3 (Jeong et al.,
2022). A summary of these datasets is provided in Table with more detailed description available in the Appendix.
Table 1: Overview of downstream tasks and datasets.
Task
Dataset
# Channels
Duration
# Samples
Rate
# Classes
Motor Imagery
PhysioNet-Me Ml
64
4s
9,837
160Hz
4
BCIC-IV-2a
22
4s
5,184
250Hz
Event Type
TUEV
16
5s
112,491
256Hz
6
Abnormal detection
TUAB
16
10s
409,455
256Hz
2
Sleep staging
HMC
4
30s
137,243
256 Hz
5
ISRUC
6
30s
89,240
200Hz
5
Emotion recognition
FACED
32
10s
10,332
250Hz
Mental disorder
Mumtaz
19
5s
7.143
256Hz
2
Mental stress
MAT
20
5s
1.707
500Hz
2
Imagined speech
BCIC2020-3
64
38
6,000
256Hz
5
Our evaluation process maintains strict consistency with prior works by adhering to the same train/val/test splits used in earlier studies, ensuring that our results are directly comparable to baseline models. Specifically, we follow the protocols from CBraMod (Wang et al., 2024b), LaBraM (Jiang ct al. 2024), and BIOT (Yang et al. (0044), guaranteeing fair comparisons across tasks. For fairness in preprocessing, we adopt the same pipeline the baselines. A notable correction was made for the ISRUC dataset, where we identified and removed a bug in the baseline code involving the inclusion of chin electrode instead of an EEG electrode. Our results for REVE exclude the chin electrode, aligning with proper electrode placement.
Finetuning Fine-tuning EEG-based models presents unique challenges due the small size of available datasets and the high noise levels in EEG recordings. Unlike large-scale vision datasets, EEG datasets often limited in size, subject-dependent and prone to distribution shifts across different recording setups. Effective fine-tuning must therefore maximize generalization while mitigating the risk of overfitting. To address this, we adopt two-step fine-tuning strategy, incorporating techniques specifically designed to enhance stability and adaptability. This includes the use of parameter-efficient fine-tuning techniques (Suzumura et al. 2024) tailored to this domain.
For downstream classification tasks, the two-step strategy, inspired by Kumar et al. (2022) goes as follow: We first train linear probe while keeping the encoder frozen, aligning the classifier with the pretrained feature space. Next, we unfreeze the encoder and fine-tune the entire network for task-specific adaptation, preserving the robustness of the pretrained model. Importantly, this two-step strategy is implemented as a single continuous training run, where the backbone is initially frozen (i.e., only the head is trained) and later unfrozen. This approach is well-suited for EEG data, where distributions can shift significantly across datasets. We employ dropout and Mixup (Zhang et al.) 2018) as data augmentation for improved robustness. To further mitigate catastrophic forgetting and improve efficiency, we integrate Low-Rank Adaptation (LoRA) into the attention blocks, within the query, key, value, and output (QKVO) projection layers. Instead of fine-tuning the entire model, LoRA introduces trainable low-rank matrices that enable effective adaptation while preserving the integrity of the pretrained model's knowledge (Hu et al. 2022).
Each training step includes a warmup phase (Kalra and Barkeshli 2024) followed by a cooldown phase. The cooldown phase employs Reduce-on-Plateau learning rate scheduler, which dynamically lowers the learning rate when training convergence slows to preventing overfitting.
To further enhance robustness, we explore model souping (Wortsman et al. 2022). which averages the weights of multiple fine-tuning runs to improve accuracy. Given the stochasticity and noise inherent in EEG datasets, souping smooths gradients and reduces variance across different fine-tuning
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trajectories. Our experiments confirm that this approach enhances generalization and produces more stable performance across diverse EEG tasks.
By integrating structured fine-tuning with data augmentation, LoRA and model souping, our approach effectively addresses the small-scale and noisy nature of EEG datasets. These techniques effectively ensure robust and generalized adaptation to downstream tasks.
4 Results and Discussion
We evaluate REVE against con-founatiion and foundation model baselines on the previously dis- cussed datasets.
Non-Foundate Models:
We compare EEGNet (Lawhern al. 2018),
EEGConformer (Song
et al.
2022),
SPaRCNet (Jing et al. 2023). Contra WR (Yang et al.] (021).
CNN-Transformer (Peh 2021).
ct al.
2022.
FFCL
(L et at. 2022). and TT-Transformer (Song et al.
Foundation Models: We compare to BIOT (Yang et al. 2024), LaBraM (Jiang et al. 2024 and
CBraMod (Wang al. 2024b). We report results displayed in existing studies.
We report the balanced accuracy for each dataset and provide additional evaluation metrics in the appendix.
Table 2: Balanced accuracy (+ std) of different methods across 9 EEG classification task
Methods
TUAB
TUEV
PhysioNetMI
BCI-IV-2a
FACED
EEGNet
0.7642 0.0036
0.3876 = 0.0143
0.5814 = 0.0125
0.4482
0.0094
0.4090 0.0122
EEGConformer
0.7758 1 0.0049
0.4074 1 0.0164
0.6049 1 0.0104
0.469
0.0166
0,4559 0.015
SPaRCNet
0.7896 = 0.0018
0.4161 = 0.0262
0.5932 = 0.0152
0.4635
0.0117
0.4673 0.0155
ContraWR
0.7746 i 0.0041
0.4384 1 0.0349
0.5892 1 0.0133
0.4678
0.0125
0.4887 1 0.0078
CNN-Transformer
0,7777 1 0.0022
0.4087 1 0.0161
0.6053 1 0.0118
0,4600 10.0108
0.4697 0.0132
FFCL.
0.7848 0.0038
0.3979 + 0.0104
0.5726 + 0.0092
0
0.4673 0.0158
ST.Transformer
0.7966
0.0023
0.3984 + 0.0228
0.6035 + 0.0081
0.0145 0.4575
0.4810
0.0079
BIOT
0.7959 1 0.0057
0,5281 1 0.0225
0.6153 1 0.0154
0.4748 1 0.0093
0,5118
0.0118
LaBruM-Bast -Base
0,8140 = 0.0019
0.6409 + 0.0065
0.6173 + 0.0122
0.4869
0.0085
0,5273
0.0107
CbraMod
0.8289 0.0022
0.6671
0.0107
0.6417 + 0.0091
0.0066 0.5138
0.5509 0.0089
REVE-Base
0.8315 = 0.0014
0.6759 + 0.0229
0.6480 + 0.0140
0.6396 0.0095
0.5646 0.0164
ISRUC
Mumtuz
MAT
BCI-2020-3
Average
BBGNet
0,7154 1 0.0121
0.9232 1 0.0104
0,6770 1 0.0116
0.4413 1. 0.0096
0,5941 1: 0.0037
REGConformer
0,7400 + 0.0133
0.9308 + 0.0117
0.6805 + 0.0123
0.4506 + 0.0133
0.6128 + 0.0044
SPaRCNet
0.7487 1 0.0075
0.9316 1 0.0095
0.6879 1 0.0107
0.4426 1: 0.0166
0,6156 0.0047
ContraWR
0.7402 = 0.0126
0.9195 - 0.0115
0.6631 = 0.0097
0.4257 0.0162
0.6119 0.0053
formrrerr
0.7363 0.0087
0.9305 0.0068
0.6779 + 0.0268
0.4533 0.0092
0.6133 0.0045
FFCL
0.7277 0.0182
0.9314 = 0.0038
0.6798 = 0.0142
0.4678
0.0197
0.6085 0.0044
ST-Transformer
0.7381 = 0.0205
0.9135 = 0.0103
0.6631 = 0.0173
0.4126 0.0122
0.6071 = 0.0048
BIOT
0.7527 0.0121
0.9358 + 0.0052
0.6875 1 0.0186
0.4920 0.0086
0,6438 0.0044
LaBraM-Base
0.7633 + 0.0102
0.9409 1 0.0079
0.6909 1 0.0125
0.5060 : 0.0155
0.6653
0.0031
CBraMod
0.7865 1 0.0110
0.9560 1 0.0056
0.7256 1 0.0132
0.5373 1 0.0108
0.6898 1 0.0031
REVE-Base
0.7819 0.007
0.9644
0.0097
0.7660
0.0355
0.5635 0.0123
0.7150 0.0057
Table shows that REVE achieves state-of-the-art performance on the downstream tasks in this study, with an average gain of 2.5%, compared CBraMod the highest performing baseline. The results on ISRUC and HMC (Appendix C.6) show that the model effectively generalizes beyond the 10-second segments was pretrained on, performing well on tasks with 30-second inputs, which highlights the strength of our positional encoding method. The results on TUEV highlight the model's 's ability to generalize to unseen electrode configurations, including bipolar setups never encountered during training.
In addition to the detailed evaluation metrics provided in Appendix C. report the performance of the Large model across our downstream tasks in Table 4 We observe that the Large model consistently produces richer embeddings. leading improved linear probing performance compared to the Base model. Model souping consistently improved performance, averaging a 1.5% gain when
NB: our preprocessing pipeline is different from the baseline and fixes potential bug

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Table 3: Impact of pretraining (PT) and weight freezing on REVE and baselines for PhysioNet-Me
PhysioNet-MII 4-class
Settings
Balanced Accuracy
Cohen's Kappa
Weighted F1
CBraMod (w/ PT)
0.6417 + 0.0091
0.5222 0.0169
0.6427 + 0.0100
BIOT (w/ PT)
0.6153 + 0.0154
0.4875 0.0272
0.6158 + 0.0197
LaBraM-Base (w/ PT)
0.6173 + 0.0122
0.4912 + 0.0192
0.6177 + 0.0141
REVE-Base (w/ PT)
0.6480 + 0.0140
0.5306 0.0187
0.6484 + 0.0170
CBraMod (w/o PT)
0.6196 +1: 0.0143
0.4994 + 0.0289
0.6289 + 0.0179
REVE-Base (w/ PT)
0.5409 11: 0.0094
0.3879 0.0125
0.5421 + 0.0101
Cbramod (Frozen)
0.3845 = 0.0345
0.2983
0.0498
0.3946 = 0.0378
BIOT (Frozen)
0.3698 + 0.0318
0.2703
0.0472
0.3723 1 0.0364
LaBraM (Frozen)
0.3715 + 0.0458
0.2814
0.0586
0.3796 + 0.0472
REVE-Base (Frozen)
0.5371 + 0.0052
0.3827 0.0070
0.5376 + 0.0033
Table Linear probing results on downstream tasks for REVE and CBraMod models with (Pool) and without pooling across multiple EEG downstream tasks. Best results are highlighted in bold. To ensure a fair comparison, we reproduced CBraMod (Wang et al. 2024b) using their official code and pretrained checkpoint, carefully following their classification pipeline (notably, no pooling) and matched architectural details to avoid any bias.
Dataset
REVE-B (Pool)
REVE-B
REVE-L (Pool)
REVE-L
CBraMod (Pool)
CBraMod
Mumtaz
0.962 0.003
0.931 0.021
0.985 # 0.006
0,980 # 0.009
0.859 = 0.009
0.907 :: 0.027
M. Arithmetic
0.010
0.740 = 0.073
0.712 1 0.008
0.665 : 0.103
0.500 0.000
0.605 : 0.020
TUAB
0.810 0.007
0.809
0.821 0.004
0.809 0.004
0.500 0.000
0.500 0.000
PhysioNetMI
0.537 0.005
0.010
0,551 # 0.001
0.617 : 0,000
0.256 = 0.002
0.531 = 0.015
BCIC-IV-2a
0.432 = 0.004
0.517 0.015
0.534 : 0.001
0.603 + 0.011
0.287 = 0.023
0.376 : 0.006
ISRUC
0.011
0.032
0.743 # 0.004
0.758 = 0.001
0.407 = 0.049
0.430 = 0.043
HMC
0.008
0.604 0.008
0.703 0.003
0,710 0.007
0.368 0.001
0.538 : 0.009
BCIC2020-3
0.234 = 0.009
0.390 0.011
0,274 +: 0.001
0.378 + 0.021
0.214 = 0.003
0.374 1 0.007
TUEV
0.592 = 0.008
0.508 0.073
0.630 + 0.003
0.550 + 0.014
0,219 = 0.009
0.482 + 0.037
Faced
0,240 = 0.010
0.422 = 0.028
0.283 + 0.003
0.469 + 0.007
0.117 = 0.005
0.261 : 0.013
Avg.
0.586
0.609
0.623
0.654
0.373
0.501
combining at least Base or Large models. For example, REVE-Base achieved 69.6% balanced accuracy on TUEV using the 10 models from Table [ However, souping showed limited benefits for the small models and sometimes led to negative outcomes.
Table 3 highlights the importance of REVE's pretraining phase. Without pretraining, CBraMod outperforms REVE by at least 8%. However, pretraining improves LEVE-Base by 11% while CBraMod gains only 2%, trend also observed in the LaBraM paper. This suggests that REVE benefits more significantly from pretraining, whereas other models derive most their performance from architectural design rather than pretraining learned representations. key advantage of REVE is its ability to produce high-quality latent spaces without heavy fine-tuning, as evidenced by linear probing results in Table 4 REVE consistently outperforms CBraMod across all downstream tasks and model sizes, with REVE-Large achieving nearly 17% higher performance. These results also highlight REVE's ability to scale effectively with model size, yielding richer and more generalizable embeddings as capacity increases. Providing rich, ready-to-use embeddings is crucial for enabling zero-shot analysis, faster BCI calibration, and improved performance in low-data or sparsely annotated settings. REVE also benefits from its spatial encoding strategy, which enables transfer across diverse EEG configurations. In Appendix [ we further demonstrate the contribution of our secondary loss function, a novel component of our framework, which proves particularly effective in frozen- feature scenarios. The secondary objective reconstructs masked tokens using a compressed, global representation from attention pooling. This pooling acts as an information bottleneck, forcing the model to distill key information from the entire input sequence into a single vector. As shown by the secondary loss mainly improves the quality of the frozen embeddings of the model.
Table 17

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5 Limitations and Future Work
The model has some limitations, requiring signals to be least one second and multiples of one second. A way to address this could be to leverage padding with causal masking.
While the focus has been collecting large EEG datasets for pretraining, important next step could be to curate this data more selectively. This includes removing low-quality recordings, balancing distributions, and identifying representative subsets, especially given the inherently noisy nature of EEG signals. Our current pretraining corpus aggregates 92 publicly available EEG datasets spanning over 25,000 subjects, which helps reduce overfitting to any single source. However, most public EEG data originates from North America and Europe, resulting in limited demographic diversity- key limitation that calls for broader, more equitable data collection efforts. To partially mitigate such imbalances, we leverage self-supervised learning (MAE), which has been shown to be robust to long-tailed and heterogeneous data distributions (X al., 2023). Targeted selection strategies, combined with robust SSL objectives, could help focus on the most informative and complementary data for building stronger, fairer, and more efficient foundation models. Thanks to its flexibility in handling any EEG configuration, REVE could itself guide this curation process.
We also plan extend our study diverse tasks, including zero-/few-shot regimes. This first iteration uses a simple MAF approach and a standard transformer, but future improvements could leverage more advanced SSL techniques and architectures. We release the model's code, weights and guidelines for adapting it to mainstream EEG tasks. In parallel, our findings point toward the presence of scaling effects EEG foundation models. Identifying precise scaling laws that capture how model size, data volume, and downstream performance interact would be valuable for future work.
Conclusion
EEG research has lacked foundation model that transfers robustly across devices, montages, and tasks-especiall under linear probing. REVE contributes to bridging this gap. Trained on 60,000 hours from 92 datasets and 25,000 subjects, REVE combines a 4D Fourier positional encoding that natively supports arbitrary electrode layouts and sequence lengths with masked autoencoding en- hanced by spatio-temporal block masking and global-token secondary loss. Across 10 benchmarks, it sets a new state of the art (average +2.5% balanced accuracy over prior foundation models). delivers up to 17% gains in linear probing. and generalizes to unscen/bipolar montages and longer inputs than used in pretraining. These properties enable faster BCI calibration, more reliable cross-site site clinical deployment, and standardized embeddings for downstream analytics. We release code, weights, loaders for arbitrary 3D coordinates, and training/eval recipes. We invite the community to extend REVE broader populations and modalities (MEG/EEG/OPM-MES) and co-build cross-montage benchmark for fair, scalable EEG evaluation.
7 Acknowledgments
This research was supported by the French National Research Agency (ANR) through its AI@IMT program and grant SNNR-24-CE23-736 as well as by grant from the Brittany region. Further support was provided by Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), by funding from the Canada Research Chairs program and the Fonds de recherche du Québec Nature et technologies (FRQ-NT). This work was granted access to the HPC resources of IDRIS under the allocation 2024-AD01 10115327RR made by GENCI, as well as HPC provided by Digital Alliance Canada.
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Appendix
A Configurations
We report the hyperparameters used to train the REVE suite of models, including data preprocessing steps, self-supervised masking configurations, and optimizer settings governing the training dynamics. Notations consistent with those in the main text.
Table 5: Exhaustive list of all hyperparameter values
Variable
Meaning
Value
Data preprocessing
Window size
1s
Overlap
0.1s
O nooie
Position noise std
0.25cm
Masking parameters
Mr
Total masking ratio
55%
R.
Spatial masking radius
3cm
R.
Temporal masking radius
seconds
Dr
Dropout ratio
10%
Rs
Dropout spatial radius
Training dynamics
Optimizer
StableAdamW Warmup Stable Decay
Scheduler
Peak learning rate
2.4 10-4
$1, B2
Momentum constants
0.9, 0.95
e
Numerical stability bias
10-9
Jnit
Initialization std.
0.02
Batch size
4, 096
Secondary loss multiplier
0.1
We report how the scaled number parameters allocated across our models. We also indicate the number of Fourier frequencies encoded (see Section 2.2).. Note that no frequency truncation was required, as we closely matched the hidden dimension of our models the number of components generated by the 4D PE module.
Table 6: Summary of encoder configurations for different sizes
Size
depth
n heads
dim
params (M)
Tirrog
Small
4
512
12
4
Base
22
512
Large
22
19
1250
408
5
B Pretraining dataset
We include summarized description of the pretraining dataset composition, grouped by category, platform of origin and number of channels. The final dataset spans 61,415 hours of recordings from 92 datasets encompassing 24,274 subjects.
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Table 7: Detailed overview of the pretraining datasets.
Group
Subjects
Duration (hours)
Datasets
Category
BCI
791
457
28
Cognition
4,193
10,376
56
Clinic
19,290
50,581
8
Platform
TUH
14,987
26,847
Physionet
607
22,707
2
OpenNeuro
4153
10.194
56
MOABB
711
384
27
Other
3,802
1,250
Channels
[3 30
19,871
50,870
31
330 80
1,781
1,516
48
[80 129
2,622
9,027
13
Total
24,274
61,415
92
We provide and exhaustive list of the datasets in the pretraining set, along with their respective licenses.
MOABB (Aristmmunha et al., 2023): AlexMI (Barachant, 2012). BNCI2014004 (Lebb al.
2007).
BNCI2015001 (Faller et al. 2012). BNC12015004 (Scherer et al. 2013). Cho2017 (Cho et al.
2017. Lee2019MI (Lee al. 2019). Liu2024 (Liu et al. 2024). Ofner2017 Ofner al. 2017)
Shin2017A (Shin et | Weibo2014 (ii al. 2014), 26 (Zhou et al. 2016), Schirmeiis
ter2017 (Schirrmesster et al. 2017), Kalunga2016 Kalunga et al. 2015). Lee2019SSVE! (Lee et al..
2019), Nakanishi2015 (Nakanishi et al. 2015), B/2014a Korczowski et al. 2019h), B12014b (o
rezowski al. 2019c). ANCI201400$ (Riccio et 2013), BNCI201 BNCI201 Guger et al. 2009). EPFLP300 (Hoffmann et al. 2008). BI2015a (Korczowski et al.
(Aricò et al. 2014)).
2019a), B12015b Korezowski et al.
2019c). Sosulski2019 (Sosulski 202T). Lee2019ERP (Lee
et al. 2019)
MOABB is under BSD 3-Clause License.
Physionet GGoldberger al., 2000): Siena (Detti 2020 Detti et al.) 2020). under the Creative
Commons Attribution 4.0 International Public License, ICARE (Amorim et al. 2023) under the 4.0 International Public License,
Creative Commons
OpenNeuro: ds004706 (Rudoler et al., 2023). ds004582 (Makowski et al. 2023), ds004356 (Shan
et al. 2022). ds004817 Grootswagers et al. 2023b), ds005 189 (Helbing et al. 2024).
ds003887
(Shatek al. 2023). ds004043 (Moerel et al. 2022). ds003885 (Shatek et al. 2021
ds004357
(Grootswagers et al.
2024), ds003825 (Grootswagers et al., (022), ds004816 (Grootswa
gers
al. 2023), ds005262 (Metwalli Papastylianou et al. 2023), ds005273 (Esteban et al. 2024). ds004561 ( (Veillette) al. 2023). ds004951 (Haupt et al. 2024), ds004324 (Chacón and Wriessneggeri (023).
2023a), ds004840 (Cordoba-Silva
(024).
ds004477
ds005095 [Zhozhikashvili et al. 2024). ds005509 (Shirazi et al., 2025). ds005505. ds005506. ds005507, ds005510, ds005511, ds005512, ds005514 (Shirazi al. 2022bb] Alexander 2017) ds001787 (Delorme and Brandmeyer] 2024). ds003690 (Ribeiro and Castelo-Branco/ (021). ds004603 (Lowe al. 2023). ds003969 (Delorme and Braboszez 2021). ds004147 (Hassall
et al. 2024). ds003004 OOnon and Makeig 2022). ds002721 (Daly et al. 2020). ds004152 (Has sall al. 2022a) ds005089 (Aguado- Lopez al. 2024). ds004264 Hassall et al. 2022b). ds004315 CCavanagh and Jackson 2022). ds004408 BBialas et al. (023). ds005121 Siefert jet al.) 2024), ds003775 (Hatlestad- Hall et al. 2022). ds004572 (Kekecs and Farahzadi 2024). ds002778 (Rockhill et al. 2020), ds003846 (Gehrke et al. (024). ds004279 (Araya et al. 2023), ds004148 (Wang et al. (022), ds004902 (Xiang et al. 2024), ds002680 (Delorme and Fabre- Thorpe 2020), ds004284 (Veillette et al. 2022). ds004395 (Kahana et al. 2023).
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ds005508 (Shirazi et al. 2024a), ds005697
(Li and Zhao 2024). ds005620 (Bajwal et al. 2024).
ds005594 (Taylor et al. 2024). ds005586
(Baykan and Schütz 2024). OpenNeuro under the
Creative Commons CCO license.
Other sources: NMT (Khan al. 2022) under the Creative Commons Attribution License (CC 4.0 International (CC-BY-NC- 4.0), SparrKULee (Accou et al. 2023) under the Attribution- Commercial 4.0 International (CC-BY-NC-4.0), Inria Large Dreyer et al. 2023) the data on Zenodo being under the Creative Commons Attribution 4.0 International, THINGS2 (Gifford et al. (022). under the CC-By Attribution 4.0 International license, TDBRAIN (Van Dijk et al. (2022). under the GPL-3.0 license, TUH (Obeid
BY), HMS (Ram et al. 2024) under the Atribution-Nonoommevee
and Picone 2016), freely available with registration required.
C Detailed results
This section presents detailed results on downstream tasks along with concise descriptions of the datasets.
C.1 Emotion Recognition
FACED (Chnn et al., 2023) We evaluate on the FACED dataset, which contains 32-channel EEG recordings (originally 250 Hz, resampled 200 Hz) from 123 subjects across nine emotion classes. The data is segmented into 10,332 samples of 10 seconds each. We follow the standard split: subjects 1-80 for training, 81-100 for validation, and 101-123 for testing.
Table The results of different methods on emotion recognition (FACED, 9-class).
Methods
Balanced Accuracy
Cohen's Kappa
Weighted
EEGNet
0,4090 + 0.0122
0.3342 + 0.0251
0,4124 0.0141
EEGConformer
0.4559 = 0.0125
0.3858 + 0.0186
0.4514
0.0107
SPaRCNet
0,4673 = 0.0155
0.3978 + 0.0289
0,4729 + 0.0133
ContraWR
0.4887 0.0078
0.4231 = 0.0151
0.4884 0.0074
CNN-Transformer
0,4697 = 0.0132
0,4017 = 0.0168
0.4720 = 0.0125
FFCL
0.4673 0.0158
0.3987 = 0.0383
0.4699 0.0145
ST-Transformer
0,4810 = 0.0079
0.4137 = 0.0133
0,4795 = 0.0096
BIOT
0.5118 = 0.0118
0.4476 + 0.0254
0.5136 = 0.0112
LaBraM- --Bse
0,5273 = 0.0107
0.4698 + 0.0188
0.5288 0.0102
CBraMod
0.5509 = 0.0089
0.5041 = 0.0122
0.5618 0.0093
REVE-Base (ours)
0.5646 0.0164
0.5080 + 0.0191
0.5659 0.0172
C.2 Mental Disorder Diagnosis
Mumtaz (Mumtaz 2016) We use the Mumtaz2016 dataset, which includes EEG recordings from 34 individuals with major depressive disorder (MDD) and 30 healthy controls, acquired from 19 electrodes (10-20 system) at 256 Hz. Only the eyes-open and eyes-closed sessions are used. Signals are band-pass filtered (0.3-75 Hz), notch filtered at 50 Hz, resampled to 200 Hz, and segmented into 7,143 samples of 5 seconds each. The split includes 24 MDD and 19 control subjects for training, MDD and 4 controls for validation, and MDD and controls for testing. The dataset is under CC BY 4.0.
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Table 9: The results of different methods on mental disorder diagnosis (Mumtaz2016, 2-class).
Methods
Balanced Accuracy
AUC-PR
AUROC
ERGNet
0,9232 + 0.0104
0.9626 0.0095
0,9639 + 0.0093
REGConformer
0,9308 + 0.0117
0,9684 + 0.0105
0,9702 + 0.0101
SPaRCNet
0,9316 + 0.0095
0.9754 + 0.0065
0.9781 + 0.0083
ContraWR
0.9195 + 0.0115
0,9589 1 0.0102
0,9621 + 0.0092
UNN-Transformer
0,9305 0.0068
0.9757 0.0074
0.9742 1 0.0059
FFCL
0,9314 + 0.0038
0.9717 0.0021
0,9753 1 0.0033
ST-Transformer
0,9135 + 0.0103
0,9578 + 0.0086
0,9594 + 0.0059
BIOT
0,9358 1 0.0052
0.9736 1 0.0034
0,9758 1 0.0042
LaBraM-Base
0,9409 + 0.0079
0,9798 + 0,0093
0.9782 + 0.0057
CBraMod
0.9560 + 0.0056
0.9923 1 0.0032
0.9921 + 0.0025
REVE-Base (ours)
0.9644 + 0.0097
0.9961 + 0.0013
0.9957 + 0.0015
C.3 Mental Stress Detection
MAT Zyma et al. 2019) The MentalArithmetic dataset contains EEG recordings from 36 subjects, labeled "with" "without" mental stress depending whether mental arithmetic task was being performed. Signals were recorded from 20 electrodes (10-20 system) at 500 Hz, band-pass filtered (0.5-45 Hz), resampled 200 Hz, and segmented into 1.707 samples of 5 seconds. Subjects 1-28 are used for training, 29-32 for validation, and 33-36 for testing. The Arithmeii dataset under the Open Data Commons Attribution License v1.0.
Table 10: The results of different methods on mental stress detection (MAT, 2-class).
Methods
Balanced Accuracy
AUC-PR
AUROC
EEGNet
0.6770 + 0.0116
0.5763 1: 0,0102
0.7321 1 0.0108
EEGConforner
0.6805 0.0123
0.5829 0.0134
0.7424 1 0.0128
SPaRCNet
0.6879 + 0.0107
0.5825 0.0193
0,7418 + 0.0132
ContraWR
0,6631 + 0.0097
0.5787 + 0.0164
0.7332 + 0.0082
ONN-Transforne
0.6779 = 0.0268
0.5777 0.0285
0.7258 + 0.0336
FFCL
0.6798 0.0142
0.5786 0.0266
0.7330 + 0.0198
ST-Transformer
0.6631 + 0.0173
0.5672 0.0259
0,7132 1 0.0174
BIOT
0.6875 0.0186
0.6004 0.0195
0.7536 + 0.0144
LaBraM-Bass -Base
0.6909 + 0.0125
0.5999 + 0.0155
0.7721 + 0.0093
CBraMod
0.7256 + 0.0132
0.6267 + 0.0099
0.7905 + 0.0073
REVE-Base (ours)
0.7660 + 0.0355
0.7470 + 0.0807
0.8450 + 0.0514
C.4 Imagined Speech
BCIC2020-3 (Jeong et al., 2022) BCIC2020-3 is an imagined speech EEG dataset from 15 subjects, recorded with 64 channels at 256 Hz while subjects silently imagined five phrases ("hello", "help me", "stop", "thank you", "yes") without any articulation. Each phrase has 80 trials per subject, totaling 6,000 3-second samples. The data is resampled to 200 Hz. The official split includes 60 trials per class for training, 10 for validation, and 10 for testing. BCIC2020-3 under the Creative Commons Attribution No Derivatives license (CC BY-ND 4.0).
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Table 11: The results of different methods on imagined speech classification (BCIC2020-3, 5-class).
Methods
Balanced Accuracy
Cohen's Kappa
Weighted F1
ERGNet
0,4413 + 0.0096
0.3016 0.0123
0.4413 + 0.0102
EFGConfforme
0,4506 + 0.0133
0.3133 + 0.0183
0,4488 + 0.0154
SPaRCNet
0,4426 + 0.0156
0.3033 + 0.0233
0.4420 + 0.0108
ContraWR
0.4257 + 0.0162
0.3078 + 0,0218
0.4407 + 0.0182
UNN-Transformer
0.4533 1 0.0092
0.3166 0.0118
0.4506 1 0.0127
FFCL
0,4678 1 0.0197
0.3301 1 0.0359
0,4689 1 0.0205
ST-Transformer
0,4126 + 0.0122
0.2941 + 0.0159
0.4247 + 0.0138
BIOT
0,4920 + 0.0086
0.3650 1 0.0176
0,4917 + 0.0079
LaBraM-Base
0,5060 + 0.0155
0.3800 + 0,0242
0.5054 1 0.0205
CBraMod
0.5373 + 0.0108
0,4216 + 0.0163
0.5383 + 0.0096
REVE-BAR Base (ours)
0.5635 + 0.0123
0.4543 + 0.0154
0.5633 + 0.0124
C.5 Motor Imagery Classification
PhysioNet-MI (Goldberger et al. 2000) is used for motor imagery classification. It contains record- ings with 64 channels at a 160 Hz sampling rate and includes 4 classes: left fist, right fist, both fists, and feet. As in CBraMod, we select 4-second samples of the signals, resulting in 9,837 samples. Following CBraMod's s protocol, subjects 1-70 are used for training, 71-89 for validation, and 90-109 for testing. We retain all subjects and use full 4-second windows to stay consistent with CBraMod. To handle lower sampling rates in some recordings, we load all data at 128 Hz (using a 64 Hz low-pass filter) before resampling to 200 Hz. Physionet-MI is under the Open Data Commons Attribution License v1.0.
BCIC-IV-2a (Tangermann et al. 2012) is also used for motor imagery classification. It contains EEG recordings from 9 subjects performing motor imagery tasks: left hand, right hand, both feet, and tongue. Data were collected over 2 sessions with 22 electrodes at 250 Hz. Each session includes 288 trials (72 per task). We use the [2.6] second window from each trial, apply a 0.5-99.5 Hz band-pass filter, resample to 200 Hz, and apply Euclidean Alignment (He and Wu 2019), proven to be effective on this task (El Ouahidi et al. 2024). resulting in 5184 4-second samples.
Table 12: The results of different methods Motor Imagery classification.
PhysioNetMIL 4-class
BCIC-IV-2a,
Methods
Balanced Accuracy
Kappa
Weighted
Balanced Accuracy
's Kappa
Weighted
BEGNe
0.5814 + 0.0125
0,448 + 0.199
0.57% + 00115
0.4482 0,0094
0.2633 0.0121
0.2266 0.0108
BEOConfumer
0.6049 0.0104
0.4736 1 0.171
0.6062 0.0055
0.4696 :: 0.0106
0.2924 0.0141
04533 0.0128
SPaRCNet
0.5032 - 0.0152
0.4664 1 0.234
0.5937 + 0.0477
0.4635 :: 0.0117
0.2847 = 0.0147
0/4332 1: 0.0126
CommrWW
0.5892 0.0133
0.4527 1 00248
0,5918 1 0.016
0.4678 1: 0.0125
0.2905 0.0160
0.413 0,0142
0,6053 0.0118
0,4735 0.0233
0.6041 00005
0.4600 0.0108
0.7800
0
FFCE
0.5776 0.0092
0,4233
4 00182
0,5701
+ 0.0079
0.470
0.0133
0.2627 0.0176
04338 0.0139
ST Tranffrmmc
0.6035 : 0.008]
0,4712 1: 0.0199
0,6053 1 0.0075
0.4575 1: 0,0145
0.2733
0,0198
0/471 1 0.0142
BIOT
0.6153 + 0.0154
0.4875 4 0,0222
0.6158 + 0.0197
0.4748 +: 0.0093
0,2997
0.0139
0.407
0.0125
LaBraM-Rase
0/6173 = 0.0122
0A9122
+ 0.192
8.6177 + 0.0411
0.4669 : 0.0085
0.3159
0.0154
04758
0.013
CBraModd
0.6417 0.0091
0,5222 00169
0.6427
0.0100
0.5138
0,0066
0.3518
0.00M
0,4984 0.0085
REVE Base
0,6480 0.0140
0.5366 + 0.0187
0,6484
0.0170
0.6396
0,0095
0.5194
0.0126
0,6339 0.0110
C.6 Sleep Staging
ISRUC (Khllighi et al. 2016) We use the sleep staging task on the ISRUC dataset (Subgroup 1). which contains PSG recordings from 100 subjects. Only EEG signals are used (6 channels, sampled at 200 Hz), segmented into 89,240 30-second epochs, each labeled with one of five sleep stages following AASM standards. Subjects 1-80 are used for training, 81-90 for validation, and 91-100 classification problem,
for testing. As in prior work, the task is framed as a using sequences of 20 consecutive epochs to model stage transitions. ISRUC freely accessible online.
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Table 13: The results of different methods on sleep staging (ISRUC, 5-class). 'I the baseline code, a chin electrode might have been used instead of an EEG one; REVE results are reported without it.
Methods
Balanced Accuracy
Cohen's Kappa
Weighted F1
EEGNet
0.7154 0.0121
0.7040 + 0.0173
0,7513 = 0.0124
EEGConformer
0,7400 = 0.0133
0.7143 + 0.0162
0,7634 = 0.0151
SPaRCNet
0.7487 + 0.0075
0.7097 + 0.0132
0.7624 0.0092
ContraWR
0.7402 0.0126
0.7178 0.0156
0.7610 = 0.0137
CNN-Tranfformer
0.7363 0.0087
0.7129 + 0.0121
0.7719 = 0.0105
FFCL
0.7277 0.0182
0.7016 + 0.0291
0,7614 = 0.0197
ST-Transformer
0.7381 = 0.0205
0.7013 + 0.0352
0.7681 = 0.0175
DeepSleepNet
0.7419 + 0.0144
0.7036 + 0.0241
0.7643 = 0.0122
USleep
0.7586 + 0.0116
0.7209 + 0.0143
0.7805 0.0105
BIOT
0.7527 + 0.0121
0.7192 + 0.0231
0,7790 0.0146
LaBraM-Base
0.7633 0.0102
0.7231 + 0.0182
0.7810 0.0133
CBraMod
0.7865 0.0110
0.7442 + 0.0152
0.8011 = 0.0099
REVE-Base
0.7819 0.0078
0.7500 + 0.0156
0.8005 + 0.0135
HMC (Alvarez-Essteez and Rijsman 2021). The Haaglanden Medisch Centrum (HMC) Sleep Staging Datahase is a sleep stage detection dataset, consisting of 151 full-night polysomnographic (PSG) recordings collected from patients referred for sleep studies. The data includes EEG, EOG, EMG, and ECG channels, with a sampling rate of 256 Hz, and annotations for five sleep stages (Wake, NI, N2, N3, REM) manually scored by trained sleep technicians. HMC is under the Creative Commons Attribution 4.0 International Public License.
Table 14: The results of different methods on sleep staging (HMC, 5-class).
Methods
Balanced Accuracy
Cohen's Kappa
Weighted F1
SPaRCNet
0.4756.10.1100 0.4242++0.041
0.314710.1315 0.2340=000554
0.410011.13100
ContraWR
0.2987+00.028
Transfrrsfrrm
0.6573=000041
0.5961=0.0105
0.6896+0.0006
FFCL
0,4427=000702
0,2542=t0.063
0,2902-t0.0485
ST-Transformer
0.2559:10.0141
0.0503
0.1428::0.0122
BIOT
0.6862+100001
0.629510.0013
0.70114
LaBraM-Bacd Base
0.7286=10.0101
0.68 0.6012:10.007
0.7554+0.0024
REVE-Base
0.7401 + 0.0075
0.6982 + 0.0078
0.7638 + 0.0074
C.7
Event Type Classification
TUEV (Obeid and Picone 2016 is an EEG dataset with six annotated classes: spike and sharp wave, generalized periodic epileptiform discharges, periodic lateralized cpileptiform discharges, eye movement, artifact, and background. The recordings use 23 channels consistency with CBraMod, BIOT, and LaBraM, we used BIOT's s processing scripts which preprocess the dataset using common bipolar montage channels in the 10-20 system, apply a 0.3-75 Hz band-pas pass filter, remove power line noise with a 60 Hz notch filter, and resample to 200 Hz. The dataset split into 112,491 5-second samples. We follow the original training-test split and further divide the training set into 80% training and 20% validation, matching BIOT setting. To provide our model with the electrode positions, we used the average position of each bipolar montage. TUEV part of the TUH dataset, which is freely available with registration required.
256 Hz sampling rate, For
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Table 15: The results of different methods on event type classification (TUEV, 6-class).
Methods
Balanced Accuracy
Cohen's Kappa
Weighted F1
EEGNet
0.3876 + 0.0143
0.3577 + 0.0155
0.6539 0.0120
BFGConformer
0.4077 + 0.0164
0.3967 + 0.0195
0.6983 + 0.0152
SPaRCNet
0.4161 + 0.0262
0.4233 + 0.0181
0.7024 = 0.0104
ContraWR
0.4384 + 0.0349
0.3912 = 0.0237
0.6893 0.0136
0.4087 0.0161
0.3815 = 0.0134
0.6854 0.0293
FFCL
0.3979 1 0.0104
0,3732 = 0.0188
0.6783 + 0.0120
ST-Transformer
0.3984 + 0.0228
0.3765 = 0.0306
0.6823 = 0.0190
BIOT
0.5281 +: 0.0225
0.5273 + 0.0249
0.7492 + 0.0082
LaBraM-Base
0.6409 + 0.0065
0.6637 + 0.0093
0.8312 + 0.0052
LaBraM-Large
0.6581 + 0.0156
0.6622 + 0.0136
0.8315 0.0040
LaBraM -ugge
0.6616 0.0170
0.6745 = 0.0195
0.8329 0.0086
CBraMod
0.6671 + 0.0107
0.6772 = 0.0096
0.8342 = 0.0064
REVE-Base (ours)
0.6759 0.0229
0.6783 + 0.0199
0.8451 0.0129
C.8 Abnormal Detection
TUAB (Obeid and Picone 2016) is used for abnormal EEG detection, where recordings are labeled as normal or abnormal. shares the same 23-channel, 256 Hz format as TUEV. The dataset is split into 409,455 10-second samples for binary classification. We follow the provided training- test split and apply an 80%-20% raining-validation split, consistent with BIOT. We resampled at 200 Hz, band-pass at 0.5-99.5 Hz, and directly used all channels and their positions. TUAB is part of the TUH dataset, which is freely available with registration required.
Table 16: The results of different methods on abnormal detection (TUAB, 2-class).
Methods
Balanced Accuracy
AUC-PR
AUROC
EFGNet
0.7642 + 0.0036
0.8299 + 0.0043
0.8412 + 0.0031
EEGConformer
0.7758 + 0.0049
0.8427 + 0.0054
0.8445 0.0038
SPaRCNet
0.7896 + 0.0018
0,8414 = 0.0018
0,8676 0.0012
ContraWR
0.7746 1: 0.0041
0.8421 = 0.0104
0.8456 = 0.0074
CNN-Transformer
0.7777 + 0.0022
0.8433 = 0,0039
0,8461 = 0.0013
FFCL
0.7848 + 0.0038
0.8448 = 0.0065
0.8569 = 0.0051
ST-Transformer
0.7966 + 0.0023
0.8521 = 0.0026
0.8707 0.0019
BIOT
0.7959 1 0.0057
0.8792 + 0.0023
0.8815 0.0043
LaBraM-Base
0,8140 0.0019
0.8965 1 0.0016
0.9022 0.0009
LaBraM- Large
0.8226 = 0.0015
0,9130 = 0.0005
0.9127 = 0.0005
LaBraM-H Huge
0.8258 + 0.0011
0.9204 = 0,0011
0,9162 = 0.0016
CBraMod
0.8289 + 0.0022
0.9258 = 0.0008
0.9227 0.0011
REVE-Base (ours)
0.8315 +.000144
0.9281 0.0009
0.9245 +-000133
D Ablation on the SSL Method
The final pretraining hyperparameters were selected based series of ablation studies, the results of which are presented in this section.
Table 17 reports the impact of the secondary pretraining loss eight downstream tasks using REVE- Small, evaluated under frozen-backbone backkonee, linear probing (LP), and full fine-tuning (FT) settings. Results obtained with both losses are compared to those using only the primary loss. The secondary loss consistently improves performance across nearly all datasets, enhancing results both LP and FT settings, while its removal leads to substantial drop, underscoring its importance for the model to produce strong embeddings.
The results in Table 18 show that block masking ratio of 55% yields the best overall performance, providing stable results across both fine-tuned and frozen settings and eight datasets (Mumtaz, TUAB, ISRUC, HMC, BCIC2020-3, TUEV, PhysioNetML, and Faced). In contrast, random masking
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Table 17: Effect of 2nd loss during pretraining and finetuning. The reported metric is balanced accuracy. Best results per dataset are in bold.
LP
FT
Dataset
No 2nd loss
+ 2nd loss
No 2nd loss
+ 2nd loss
Mumtaz
0.818 + 0.043
0,920 + 0.018
0.818 + 0.043
0.922 + 0.018
TUAB
0.797 + 0.004
0,802 0.005
0.803 = 0.003
0.810 + 0.005
ISRUC
0.699 + 0.006
0.625 = 0.003
0.777 = 0.002
0.002
0.770 + 0.002
HMC
0,598 + 0.008
0.591 0.005
0.01
0.723 0.005
BCIC2020-3
0.234 = 0.009
0.237 0.008
0.010
0.481 = 0.008
TUEV
0,442 + 0.060
0.520 0.005
0
0.623 0.011
PeyM
0.379 + 0.058
0.533 + 0.019
0.563 +
0.583 + 0.009
Faced
0,220 + 0.008
0.233 0.004
0.302 + 0.016
0.410 0.004
Avg.
0.523
0.558
0.612
0.665
Table 18: Performance comparison across different masking ratios (0.25, 0.55, 0.75) between block masking strategy and random masking, evaluated for full fine-tuning versus frozen embeddings. We display the average balanced accuracy on the small model over eight downstream tasks.
Frozen
Full Fine- Tine-Tuning
Masking Ratio
Random
Block
Random
Block
0.25
0.523
0.513
0.612
0.602
0.55
0.550
0.558
0.643
0.665
0.75
0.519
0.546
0.606
0.655
favors smaller ratios (25%), but its unstructured nature leads to highly redundant inputs, making the reconstruction task artificially easier. These findings align with ablation results reported in Cbramod, Labram, and BIOT.
Table 19: Ablation study on PhysioNetMI and Mental Arithmetic datasets. The reported metric is balanced accuracy, with the average computed across both tasks, with the Base model. "Note that the learnable positional encoding matches the baseline, but does not allow for the extension to larger time windows unseen spatial configurations.
Ablated component
PhysionetMI
Mental Arithmetic
Average
Learnable PE
0.650 + 0.0113
0.752 0.0421
0.701 0.0218
MLP4D Position noise
0.637 + 0.0056
0.717 + 0.0425
0.677 + 0.0214
0.628 + 0.0084
0,692 0.0665
0.660 - 0.0335
Dropout block masking
0.645 + 0.0155
0,678 + 0.0521
0.662 + 0.0272
Temporal block masking
0.646 = 0.0155
0.723 0.0422
0.685 = 0.0225
Base Performance
0.6480 + 0.0140
0.7660 + 0.0355
0.707 +: 0.0191
Table 19 presents ablation study two downstream tasks to assess the contribution of each component in our SSL pipeline. All components appear to contribute positively to performance. The "Learnalle PE" line not a true ablation, but rather a variant using Iearnable positional embeddings, where separate embedding is learned for each electrode and time index observed during pretraining. Although this approach performs well, it is limited to the spatial and temporal configurations seen during training (approximately 400 unique electrode names, over 10-second windows) and does not generalize to longer sequences unseen electrode layouts, unlike REVE's 4D positional encoding.
Table 20 presents an ablation study on choice of activation and normalization functions, an important design factor in transformer-based foundation models. We compare GEGLU + RMSNorm, GELUU RMSNorm, and GEGLU LayerNorm configurations during pretraining, and report downstream performance after fine-tuning on three datasets using the REVE-Small model.
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Table 20: Ablation study on activation functions and normalization layers (GEGLU vs. GELU, RMSNorm vs. LayerNorm). report downstream balanced accuracy after pretraining the REVE- Small model with each configuration.
Dataset
GEGLU + RMSNorm
GELU + RMSNorm B
BCIC-IV-2a
0.012
0.560 + 0.018
0.537 + 0.018
TUEV
0.623 = 0.011
0.592 0.010
0.577=0.034
PhysioNetMI
0.583 + 0.009
0.586 =0.009
0.559 =0.007
Avg.
0.596
0.579
0.558
The GEGLU R.MNNrmm combination achieves the best average performance (0.596), outperforming the others on BCIC-IV-2a and TUEV. GELU + RMSNorm performs similarly but only leads on PhysioNetMI. In contrast, GEGLU + LayerNorm consistently underperforms, highlighting the effectiveness of RMSNorm over LayerNorm and the benefits of gated activations like GEGLU in this context.
E
Additional results
This section presents supplementary experiments that further support the main results, focusing on few-shot performance and evaluation under reduced-electrode configurations.
E.1
Sparse setups
Table 21:
Performance of REVE-Base under sparse input configurations. Balanced accuracy is reported for PhysionetMI (Left-Right) and imagined speech tasks as the number of EEG channels progressively reduced.
Channels
PhysionetMI L-R
Speech
64
0.824 + 0.008
0.565 10.016
32
0.808 + 0.007
0.490 + 0.094
16
0.787 + 0.008
8
0.781 + 0.006
0.294 0.063
4
0.728 + 0.009
0.258 0.019
2
0.700 + 0.025
0.228 + 0,006
0.660 +0.019
0.209 0.008
Table reports REVE-Base's performance under increasingly sparse input configurations. On the Physionet MI L-R task, accuracy degrades gracefully from 0.824 with 64 channels to 0.660 with single channel, demonstrating robustness to reduced spatial coverage. In contrast, the imagined speech task is more sensitive to channel sparsity, with performance dropping from 0.565 to 0.258 with four channels and 0.209 with one, close to random chance. These results confirm that while REVE generalizes well under limited input, tasks requiring broad spatial information remain more challenging.
E.2 Few-shot experiments
We conducted few-shot (FS) experiments to simulate realistic BCI usage scenarios. Tasks were constructed from the BCI IV-2a dataset using two motor imagery classes (Left-Right). For each subject, multiple inductive FS runs were performed. In each run, N labeled samples per class ("shots"") were randomly selected within session for training, while the remaining samples from both sessions were used for evaluation.
Classification was done using a Nearest Class Mean (NCM) classifier. Each configuration was repeated 20 times per subject, and we report the average balanced accuracy across subjects and runs. We evaluated two configurations of REVE-Base:
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REVE-Base (PT): directly after self-supervised pretraining, with no further supervised adaptation.
REVE-Baan Base (XFT): after cross datsset fine-tuning multiple labeled Left Left-Right MI datasets (Schirreisttrr et al., 2017). (Cho et al.) 2017). (Goldberger et al. 2000 (Lee
al. 2019), Yi al. 2014)). REVE'S 4D positional encoding enables joint training across diverse electrode configurations without requiring channel alignment or selection.
Table 22: Few-shot performance of REVE-Base on BCI IV-2a dataset
N-shots
2
5
10
20
REVE-Base (PT)
0.588 + 1.45
0.601 + 0.001
0.652 = 0.013
0.688 + 0.010
0.723 + 0.010
REVE-Base (XFT)
0.605 +: 1.12 0.645 1: 0.009 0.705 + 0.009
0.768 +: 0.009 0.817 1: 0.004
Table 22 shows that REVE- achieves competitive accuracy even without supervised adaptation, demonstrating that its pretrained embeddings can be effectively leveraged for downstream BCI tasks. After crass-d fine-tuning, performance improves consistently across all shot counts, with gains reaching +10% at 20 shots. This indicates that REVE transfers well across subjects and datasets, while benefiting from minimal supervised adaptation. Such generalization uncommon among BCI embedding models, which typically require task subject-specific retraining.
F
Experiment details
Compute resources
We include details about the compute nodes that were used for pretraining.
Compute Type: GPU-accelerated nodes
GPU Model: NVIDIA A100
CPU Model: Intel Cascade Lake SP 6248
CPU Cores per Node: 40 cores
Total Memory per Node: 192 GB
Storage: Access to shared full-flash parallel file system based IBM Spectrum Scale
Job Scheduler: Slurm
We also estimate the number Roating-poing operations (FLOPs) required to train the REVE-Bass
model, following the formulation from Chowdhery et al. 2023
D (6N 12LHQT)
P.n
where 7 denotes the training time (in seconds), = 60k x 3600 1.1x x is the total number of tokens seen during pretraining (corresponding to 60k hours of EEG, overlap coefficient of 1.1, 68 average channels, and 17 epochs), the number of encoder-decoder layers, H 8 the number of attention heads, dimension, and T 68 x 11 the average number of tokens per sequence (channels x patches).
72M is the number of model parameters, = 23
64 the head
The peak throughput is P = 312 TFLOPs at half precision, achievable on A100 GPUs, and the model FLOPs utilization set to 1) = 0.5 (50%).
This configuration yields an estimated 260 A100 GPU hours for single pretraining run. The formula can be directly adapted for other model sizes hardware configurations.
F.2 Use of Existing Assets
We used Python (Python Software Foundation License), and some associated libraries for the implementation:
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1. PyTorch (BSD-3 License)
2. NumPy (NumPy license)
3. scikit-leam (BSD license)
4. Pandas (BSD 3-Clause License)
5. Hugging Face's Accelerate (Apache License 2.0)
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The claims in the abstract and introduction accurately reflect the paper's contributions. The introduction clearly states the goals of REVE: building foundation model for EEG that generalizes across datasets, durations, and electrode configurations.
These claims are supported by:
A novel 4D positional encoding (Section 2.2), validated by transfer to unseen setups.
Pretraining on 92 datasets (Section 3.1), the largest EEG corpus date.
Extensive evaluations across 10 downstream tasks, showing consistent gains in full fine-tuning and linear probing (Section 4, Tables 2-16).
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the
contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize other settings.
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The paper includes a dedicated Limitations and Future Work section outlining key constraints of REVE, such as fixed input duration requirements, positional encoding limitations, and the limited dataset curation and selection. We also acknowledge that while scaling effects are observed, identifying precise scaling laws remains future work. These points reflect a clear understanding of the method's current boundaries and opportunities for improvement.
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The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
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The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimess.
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While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
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Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The paper discusses both the potential positive and negative societal impacts of the work. On the positive side, the model can greatly benefit healthcare by improving the accuracy and efficiency of EEG-based applications such as brain-computer interfaces and diagnostic tools. On the negative side, the model's decoder, which could potentially reconstruct raw EEG data, poses a privacy risk. To mitigate this, the decoder is not being released, thus reducing the potential for misuse in generating sensitive or private information. The paper emphasizes the responsible and ethical use of the technology, with awareness of its potential risks.
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Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (c.g., pretrained language models, image generators, scraped datasets)?
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Reviews:
[{"review_id": "Gcl7jfHsn6", "reviewer": "Reviewer_xtx1", "summary": "This paper presents REVE a foundation model trained on over 60,000 hours of EEG data from 92 datasets and 25,000 subjects. REVE achieves SOTA performance on 10 downstream EEG tasks. REVE addresses several challenges in training an EEG foundation model such as data heterogenity, low SNR, and different electrode configurations. The main contributions of this paper are\n\n1. A novel 4D positional encoding to model EEG signals with varying temporal lengths and electrode configuration\n2. Curation of large scale EEG by combining 92 datasets.\n3. State-of-the-art performance on multiple benchmarks", "questions": "What other architecture design choices did you consider? Were there any other successful or failed designs that are not mentioned in the paper. It would be great to mention failed designs as that can help guide future directions\n\nDid you have an ablation on performance vs scale to find out whether there is room for improvement with more data or we have reached a saturation in performance with this dataset", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 2, "strengths_and_weaknesses": "1. This paper has created a large scale EEG dataset by combining 92 datasets spanning data from clinical, BCI and research domains. This will be a valuable resource for foundation model research in EEG\n2. This paper adapts MAE to spatiotemporal EEG signal and proposes a 4D positional encoding to address variation in temporal length and electrode configuration across datasets.\n3. Strong results demonstrating the importance of large scale pretraining on multiple downstream tasks.\n4. Clear writing , description of methods and results. \n5. Modeling choices are inspired by successes in different modalities \n\nI do not see any major weaknesses in this paper and believe it is a strong contribution towards foundation model development in EEG Domain. A minor weakness of this paper is that it is leveraging the ideas from other modalities but is missing an ablation study on the design choices for the architecture.", "was_revised": true, "final_justification": "The authors addressed most of my concerns. I will maintain positive rating", "rebuttal": "We thank Reviewer `xtx1` for their thoughtful review of our contributions. We appreciate the recognition of the novelty of our 4D positional encoding, the significance of our large-scale dataset, our methodological choices, and the impact of our results.\n\n\n> A minor weakness of this paper is that it is leveraging the ideas from other modalities but is missing an ablation study on the design choices for the architecture.\n\n\nWe acknowledge that we did not explore many architecture variations, as this was not the primary focus of our study. Our design choices were mainly guided by prior work across modalities, as discussed in Section 2.3. Transformers have demonstrated strong, modality-agnostic performance, with their core architecture largely unchanged across vision or text domains.\nNevertheless, as reported in Table `aUp9.2` (our answer to reviewer `aUp9`), we provide additional ablation studies on normalization and activation layers. These results support our design choices, as our selected architecture consistently achieved the best performance which aligns well with best practices in modern Transformer models for language.\n\n\nWe report here the average performance across the downstream tasks, see Table `aUp9.1` for the exhaustive results.\n\n\n|Dataset|GEGLU + RMSNorm|GELU + RMSNorm|GEGLU + LayerNorm|\n|-|-|-|-|\n|Avg.|**0.596 ± 0.024**|0.579 ± 0.017|0.558 ± 0.020|\n\n\nTable `xtx1.1`: Average of the ablation results on normalization and activation layers\n\n\n> What other architecture design choices did you consider? Were there any other successful or failed designs that are not mentioned in the paper. It would be great to mention failed designs as that can help guide future directions\n\nTo leverage the Transformer architecture to EEG data, our experimentation focused on patching strategy, positional encoding, and training objectives rather than altering the backbone.\n\n\nFor positional encoding, we initially tested separate spatial and temporal 1D Fourier encodings, but a unified approach proved more practical. We also obtained competitive results with 4D learnable positional embeddings, but this approach limited our ability to generalize to unseen spatial positions and timesteps.\n\n\nFor the patching strategy, we explored various patch sizes and overlaps. The optimal configuration we found was very similar to what has already been reported in the LaBraM (Jiang et al., 2024), BIOT (Yang et al., 2024) and CBraMod (Wang et al., 2025) baselines.\n\n\nFor self-supervised learning, we experimented with random masking instead of block masking, as well as different masking ratios. Following reviewer G86N comments, we ran additional ablations on masking strategy to extend results presented in the Table 18 of the appendix E of the original manuscript.\n\n\n> Did you have an ablation on performance vs scale to find out whether there is room for improvement with more data or we have reached a saturation in performance with this dataset\n\n\nWe thank the reviewer for mentioning the topic of data scaling law. We did observe hints of scaling effects, but setting up robust scaling experiments was challenging due to the need for extensive data curation, which often requires a pretrained model. As a result, our scaling attempts remained limited by the inherent noise in EEG data. In practice, data curation is usually pursued after a foundation model is established, and we see this as a key direction for future work.\n\n\n## References\n\n\n- Jiang, Weibang and Liming Zhao, and Bao-liang Lu. \"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI.\" The Twelfth International Conference on Learning Representations.\n- Yang, Chaoqi, M. Westover, and Jimeng Sun. \"Biot: Biosignal transformer for cross-data learning in the wild.\" Advances in Neural Information Processing Systems 36 (2023): 78240-78260.\n- Wang, Jiquan, Sha Zhao, Zhiling Luo, Yangxuan Zhou, Haiteng Jiang, Shĳian Li, Tao Li, Gang Pan. \"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding.\" The Thirteenth International Conference on Learning Representations. 2025."}, {"review_id": "P0H255NguW", "reviewer": "Reviewer_rHPq", "summary": "REVE is a foundation model for EEG data that generalizes across diverse setups. It uses 4D positional encoding and was pretrained on 60,000+ hours of EEG data from 92 datasets (25,000 subjects). The model achieves SOTA performance on 10 tasks, including motor imagery, seizure detection, and emotion recognition.", "questions": "1. The introduction (lines 37-39) criticizes existing models for issues such as being trained solely on the TUH dataset, leading to poor generalization to EEG data with different electrode layouts. However, among the pretrained models cited in lines 35-36, none actually suffer from this limitation—for example, CBraMod, despite being trained only on TUH dataset, has been successfully applied to downstream tasks with varying electrode configurations. Could the authors clarify the basis for this claim? \n2. Did the pretrained baseline models use the same two-stage fine-tuning strategy as the proposed method in this paper? If the results were directly taken from the original baseline papers without adopting the two-stage fine-tuning strategy, such a comparison might be unfair. \n3. As mentioned in the weaknesses, could the author provide more results of the linear probing experiment to prove the performance of REVE under lightweight finetuning?", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: The paper demonstrates outstanding model performance and achieves state-of-the-art results in 10 different downstream EEG tasks. The paper provides particularly detailed experimental methodology, including thorough implementation specifics that ensure transparency. Importantly, the author ensured the repeatability of the model by publicly releasing all the code and pre-trained weights.\nWeaknesses: While the paper emphasizes REVE’s advantages in linear probing and limited fine-tuning scenarios, its core experimental design exhibits notable gaps. Specifically: \n1.\tAbstract (lines 5-7) critiques existing EEG foundation models for their poor linear probing performance, and (lines 15-16) claim REVE’s strong generalization with “little to no fine-tuning.”\n2.\tThe introduction (lines 41-42) further argues that current models require “full fine-tuning” for transfer.\n3.\tResults and Discussion (lines 319-322) highlight REVE’s “key advantage” in “producing high-quality latent spaces without heavy fine-tuning”.\nYet, the primary results (Table 2) rely on a two-stage fine-tuning strategy (Section 3.3 Downstream Tasks). Only limited validation for the above statement is provided (“w/PT” and “Frozen” settings in Table 3), tested on just one dataset, failing to generalize the “lightweight fine-tuning” advantage.\nThis inconsistency weakens the paper’s argument for REVE’s “lightweight fine-tuning benefits”, potentially undermining the conclusions’ credibility. To substantiate this argument, comparisons of all pre-trained baselines under linear probing across all downstream datasets are essential—a gap the current experiments leave unaddressed. Such validation is critical: if pre-trained models still require two full fine-tuning stages despite their pre-training, REVE’s position in the performance-simplicity tradeoff becomes significantly less competitive.", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer `rHPq` for recognizing the strong performance of REVE across a diverse set of EEG tasks, as well as our commitment to reproducibility through public release of code and pre-trained models. We also thank the reviewer for their constructive and detailed feedback. Below, we address the key concerns regarding linear probing, the fine-tuning setup, and generalization to variable electrode layouts.\n\n\n## Linear probing comparisons against baselines\n\n\n> could the author provide more results of the linear probing experiment to prove the performance of REVE under lightweight finetuning?\n\n\nWe agree that our claims about REVE’s effectiveness under limited adaptation regimes could benefit from more empirical evidence. To address this, we have added linear probing results for CBraMod across our downstream tasks suite. Given the added clarity these results provide and following Reviewer `aUp9`’s suggestion, the complete table will be moved to the main paper.\n\nIn more detail, we extended the results we had in Table 16. To ensure a fair comparison, we reproduced CBraMod (Wang et al., 2025) using their official code and pretrained checkpoint, carefully following their classification pipeline (notably, no pooling) and matched architectural details to avoid any bias. This resulted in performance gains for REVE of up to 16% over what was originally reported.\n\nAll results are shown in the following table, where we present balanced accuracies.\n\n|Dataset|REVE-B (Pool)|REVE-B (No Pool)|REVE-L (Pool)|REVE-L (No Pool)|CBraMod (Pool)|CBraMod (No Pool)|\n|-|-|-|-|-|-|-|\n|Mumtaz|0.962 ± 0.003|0.931 ± 0.021|**0.985 ± 0.006**|0.980 ± 0.009|0.859 ± 0.009|0.907 ± 0.027|\n|Mental Arithmetic|0.725 ± 0.010|**0.740 ± 0.073**|0.712 ± 0.008|0.665 ± 0.103|0.500 ± 0.000|0.605 ± 0.020|\n|TUAB|0.810 ± 0.007|0.809 ± 0.004|**0.821 ± 0.004**|0.809 ± 0.004|0.500 ± 0.000|0.500± 0.000|\n|PhysioNetMI|0.537 ± 0.005|0.510 ± 0.012|0.551 ±0.001|**0.617 ± 0.000**|0.256 ± 0.002|0.531 ± 0.015|\n|BCIC-IV-2a|0.432 ± 0.004|0.517 ± 0.015|0.534 ±0.001|**0.603 ± 0.011**|0.287±0.023|0.376 ± 0.006|\n|ISRUC|0.697 ± 0.011|0.662 ± 0.030|0.743 ± 0.004|**0.758 ± 0.001**|0.407 ± 0.049|0.430 ± 0.043|\n|HMC|0.647 ± 0.008|0.604 ± 0.008|0.703 ± 0.003|**0.710 ± 0.007**|0.368 ± 0.001|0.538 ± 0.009|\n|BCIC2020-3|0.234 ± 0.009|**0.390 ± 0.017**|0.274 ± 0.001|0.378 ± 0.021|0.214 ± 0.003|0.374 ± 0.007|\n|TUEV|0.592 ± 0.008|0.508 ± 0.073|**0.630 ± 0.003**|0.550 ± 0.014|0.219 ± 0.009|0.482 ± 0.037|\n|Faced|0.240 ± 0.010|0.422 ± 0.028|0.283 ± 0.003|**0.469 ± 0.007**|0.117 ± 0.005|0.261 ± 0.013|\n|Avg.|0.586|0.609|0.623|**0.654**|0.373|0.501|\n\nTable `rHPq.1`: **Linear probing** results on downstream tasks for REVE and CBraMod. Best results are highlighted in bold.\n\nNote: To ensure exhaustiveness, we have added the FACED dataset to the linear probing experiments, which affects the reported averages.\nAs shown, REVE consistently outperforms CBraMod across all downstream tasks under linear probing, regardless of whether pooling is used.\n\n## On the “two stages” of fine-tuning\n\n> Did the pretrained baseline models use the same two-stage fine-tuning strategy as the proposed method in this paper? If the results were directly taken from the original baseline papers without adopting the two-stage fine-tuning strategy, such a comparison might be unfair.\n\nWe agree that simplicity is critical to the practicality of foundation models and appreciate the concern regarding potential complexity in our fine-tuning procedure. To clarify: our “two stages” of fine-tuning are implemented as a single continuous training run, where the backbone is initially frozen (i.e., only the head is trained) and later unfrozen. This helps stabilize training but it is not a two-stage pipeline in the sense of full fine-tuning twice.\n\nCBraMod adopts a similar strategy via a multi-learning-rate setup (i.e., lower LR for the backbone), which serves a comparable purpose. Such strategies are common in transfer learning (Kumar et al., 2022). We will revise the paper to explicitly clarify this point and avoid any misinterpretation about the complexity or practicality of our fine-tuning approach.\n\n## On generalization to varied electrode setups of other methods\n\n> The introduction (lines 37-39) criticizes existing models for issues such as being trained solely on the TUH dataset, leading to poor generalization to EEG data with different electrode layouts. However, among the pretrained models cited in lines 35-36, none actually suffer from this limitation—for example, CBraMod, despite being trained only on TUH dataset, has been successfully applied to downstream tasks with varying electrode configurations. Could the authors clarify the basis for this claim?\n\nOur introduction primarily refers to LaBraM (Jiang et al., 2024) and BIOT (Yang et al., 2024), which rely on fixed positional embeddings, making direct transfer to unseen electrode layouts infeasible (e.g., BIOT’s checkpoints use 16 or 18 fixed electrodes). We agree that CBraMod, through its convolution-based asymmetric conditional positional encoding (ACPE), can technically handle arbitrary layouts, but it requires training the weights of this module to reach the best performance.\nCBraMod is pretrained on a single electrode setup, as both number and order of electrodes must match. Adapting to a different configuration requires retraining / fine-tuning, so true cross-configuration transfer is not supported, unlike REVE.\n\nThis is reflected in linear probing performance: as noted in the original CBraMod paper (Appendix J), “fixing the pre-trained parameters during training on downstream datasets will lead to a very large performance decline... CBraMod cannot currently serve as a fixed-parameter feature extractor like CLIP and SAM, and fine-tuning is still necessary.”.\n\nOur new linear probing results further confirm that these models struggle to generalize across electrode setups. We nonetheless acknowledge that the phrasing in our introduction could be clarified and will revise it to more accurately distinguish between the architectural limitations of LaBraM/BIOT and the more flexible design of CBraMod.\n\n## References\n\n- Wang, Jiquan, Sha Zhao, Zhiling Luo, Yangxuan Zhou, Haiteng Jiang, Shĳian Li, Tao Li, Gang Pan. \"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding.\" The Thirteenth International Conference on Learning Representations. 2025.\n- Kumar, Ananya, et al. (2022) \"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.\" International Conference on Learning Representations.\n- Jiang, Weibang and Liming Zhao, and Bao-liang Lu. \"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI.\" The Twelfth International Conference on Learning Representations.\n- Yang, Chaoqi, M. Westover, and Jimeng Sun. \"Biot: Biosignal transformer for cross-data learning in the wild.\" Advances in Neural Information Processing Systems 36 (2023): 78240-78260."}, {"review_id": "yA60qaxlag", "reviewer": "Reviewer_G86N", "summary": "The paper introduces REVE (Representation for EEG with Versatile Embeddings), a foundation model for electroencephalogram (EEG) designed to address the heterogeneity of EEG datasets by enabling generalization across diverse recording setups, electrode configurations, and task types. The core challenge addressed is the limited generalizability of existing EEG foundation models, which often rely on homogeneous datasets and fixed positional encodings. Key Contributions:\n\nNovel 4D Positional Encoding: REVE uses a Fourier-based 4D encoding that integrates spatial (3D electrode coordinates) and temporal (patch indices) dimensions, allowing flexibility in processing signals of arbitrary length and electrode arrangements. This addresses the rigidity of prior absolute or convolutional encodings.\n\nLarge-Scale Heterogeneous Pretraining: REVE is pretrained on 60,000+ hours of EEG data from 92 datasets spanning 25,000 subjects, the largest such effort to date. This diversity enhances generalization to unseen setups.\n\nSpatio-Temporal Block Masking: A joint masking strategy disrupts contiguous regions in both spatial and temporal dimensions during pretraining, improving representation learning for robust feature extraction.\n\nState-of-the-Art Performance: REVE achieves superior results on 10 downstream tasks, including motor imagery classification, seizure detection, and sleep staging, outperforming prior models like CBraMod and LaBraM.", "questions": "Question1: How does the 4D encoding perform with extremely sparse electrode setups (e.g., <4 channels) or non-standard montages not seen in pretraining? Are there limitations in capturing fine-grained spatial patterns with low channel counts?\n\n\nQuestion2: The pretraining corpus includes clinical, BCI, and cognitive data, but how is data quality ensured? Are there biases in dataset selection (e.g., overrepresentation of certain demographics)?\n\nQuestion3: How does REVE perform in zero-shot or few-shot scenarios, where minimal task-specific data is available? The current evaluation focuses on fine-tuning, but real-world applications often have limited labels.\n\n\nQuestion4: Does the 4D encoding incur higher computational costs than prior methods? How does it scale with increasing channel counts and signal length?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nQuality:\n\nThe experimental design is rigorous, with comprehensive evaluations across diverse tasks and datasets. The use of linear probing and fine-tuning protocols ensures fairness against baselines.\nThe large pretraining corpus and clear ablation studies (e.g., masking ratio, secondary loss impact) validate the method’s robustness.\n\nClarity:\n\nThe paper is well-structured, with detailed explanations of the 4D encoding, masking strategy, and training pipeline. Technical details (e.g., hyperparameters, dataset composition) are provided in appendices.\nVisualizations (e.g., pretraining framework diagram) and tables (performance metrics) aid comprehension.\n\nSignificance:\n\nREVE advances EEG foundation modeling by addressing long-standing challenges in data heterogeneity, which could accelerate progress in brain-computer interfaces (BCIs) and clinical diagnostics.\nThe focus on generalizable representations reduces reliance on task-specific fine-tuning, making it accessible for low-data scenarios.\n\nOriginality:\n\nThe 4D positional encoding is a novel approach for EEG, enabling true cross-setup generalization. The scale of pretraining and integration of spatial-temporal masking are also innovative.\n\nWeaknesses\n\nQuality:\n\nWhile the dataset is large, the preprocessing (e.g., z-score normalization, clipping) might introduce biases. The impact of data cleaning on downstream tasks is not fully explored.\nSome ablation results (e.g., Table 18) show inconsistent performance across masking ratios, leaving questions about optimal configurations for specific tasks.\n\nClarity:\n\nThe 4D encoding’s mathematical derivation (Section 2.2) could be more transparent, especially regarding how Fourier frequencies are selected and truncated to match embedding dimensions.\nThe secondary loss function’s design (attention pooling across layers) is briefly described; more intuition on its effectiveness in preventing over-specialization is needed.\n\nSignificance:\n\nThe clinical impact is implied but not directly validated (e.g., case studies in real-world diagnostics). The model’s utility in resource-constrained settings remains unaddressed.\n\nOriginality:\n\nWhile the 4D encoding is novel, the use of masked autoencoding (MAE) follows prior work in vision. The contribution lies more in adaptation than radical architectural innovation.", "was_revised": false, "final_justification": "", "rebuttal": "We thank Reviewer `G86N` for their feedback, highlighting the novelty of our 4D positional encoding, our strong experimental design, and the clarity of our work. Below, we clarify and directly address each of the concerns raised regarding the positional encoding, the secondary loss, data preprocessing, demographic biases, ethical concerns, practical applicability, and task specific configurations.\n\n## 4D positional encoding\n\nThe results and discussion of this part will be integrated into the final manuscript.\n\n> The 4D encoding’s mathematical derivation (Section 2.2) could be more transparent\n\nIf the embedding size does not match $2\\cdot n_{freq}^4$, we round up, compute the full 4D Fourier basis, and truncate as needed. For released checkpoints (base: 512, large: 1250), $n_{freq}$ is chosen to match exactly, so truncation isn’t required.\n> Does the 4D encoding incur higher computational costs than prior methods? How does it scale with increasing channel counts and signal length?\n\nThe 4D encoding adds minimal compute overhead, with sinusoidal computations and a small linear layer. Cost scales linearly with the number of input tokens (channels × temporal patches) and is negligible relative to the transformer backbone.\n> How does the 4D encoding perform with extremely sparse electrode setups (e.g., <4 channels) or non-standard montages not seen in pretraining? Are there limitations in capturing fine-grained spatial patterns with low channel counts?\n\nThe TUEV downstream task uses a bipolar montage, covering the case of montages not seen during pretraining.\nFor sparse input, we benchmarked REVE-Base on MI (Left-Right) and imagined speech tasks while reducing channels by relevancy in line with Panachakel et al. (2021) and Abdullah et al. (2022):\n\n|Channels|64|32|16|8|4|2|1|\n|-|-|-|-|-|-|-|-|\n|PhysionetMI L-R|0.824 ± 0.008|0.808 ± 0.007|0.787 ± 0.008|0.781 ± 0.006|0.728 ± 0.009|0.700 ± 0.025|0.660 ±0.019|\n|Speech|0.565 ±0.016|0.490 ± 0.094|0.469 ±0.014|0.294 ± 0.063|0.258 ± 0.019|0.228 ± 0.006|0.209 ± 0.008|\n\nTable `G86N.1`: Sparse input ablation results on Fine-Tuning REVE-Base\n\nThe MI performance goes from 0.824 with 64 channels to 0.660 with a single one, showing robustness to sparse input.\nFor the imagined speech task, it goes from 0.565 to 0.258 with four channels and 0.209 with one, barely above the random-chance level, highlighting that REVE’s flexibility has limits when broad spatial coverage is required.\n\n## Secondary loss intuition\n\n> The secondary loss function’s design (attention pooling across layers) is briefly described; more intuition on its effectiveness in preventing over-specialization is needed.\n\nTo provide more intuition, as described in the paper:\n“This secondary loss encourages the encoder to distribute useful information across all layers, mitigating over-specialization in the final layer and yielding more generalizable representations.”\nThe secondary objective reconstructs masked tokens using a compressed, global representation from attention pooling. This pooling acts as an information bottleneck, forcing the model to distill key information from the entire input sequence into a single vector. As shown by table 17 and its extension with results following Table aUp9.2, the secondary loss mainly improves the quality of the frozen embeddings of the model. We agree that this discussion would have been beneficial for the reader, and therefore changed the text accordingly.\n\n## Data preprocessing\n\nOur pipeline applies z-score normalization followed by clipping at 15 standard deviations. This choice was motivated by stability concerns when training on large and heterogeneous datasets, as highlighted in Appendix A.2 of Défossez et al. (2023), which notes the harmful impact of extreme outliers on the training run: “Outliers can also cause extreme gradients and throw off the optimization process.”\n\nWe acknowledge that our pipeline may limit exploration of more elaborate preprocessing or artifact mitigation strategies. However, given the scale and compute cost of pretraining REVE, we prioritized a streamlined and robust approach. We will update the manuscript to clarify this rationale.\n\n## Demographic Bias\n\n> The pretraining corpus includes clinical, BCI, and cognitive data, but how is data quality ensured? Are there biases in dataset selection (e.g., overrepresentation of certain demographics)?\n\nDemographic bias is indeed a key concern when training foundation models at scale. Our pretraining corpus aggregates 92 publicly available EEG datasets spanning over 25,000 subjects. While this reduces overfitting to any single source, most public EEG data originates from North America and Europe, resulting in limited demographic diversity—an issue we recognize as a key limitation and a priority for the field to address.\n\nTo further mitigate demographic and dataset imbalances, we leverage self-supervised learning (MAE). As shown by Xu et al. (2023), MAE is robust to long-tailed and imbalanced data, making it a practical choice for heterogeneous EEG corpora where some populations or recording conditions are underrepresented. While large-scale, diverse pretraining helps, equitable data collection remains an open challenge for future EEG foundation models.\n\nThis additional information will be added to the final manuscript.\n\n## Ethical concerns\n\n> Ethical Concerns: Major Concern: Improper research involving human subjects\n\nWe recognize the importance of ethical considerations in research involving human subjects. However, as indicated in the NeurIPS checklist, this concern does not apply to our work. We exclusively used publicly available, open-access datasets that have already undergone review and approval by relevant scientific and ethics committees. No new data were collected, and no direct interaction with human subjects took place in the course of our study.\n\n## Practical clinical impact and few shot performance\n\n> The clinical impact is implied but not directly validated (e.g., case studies in real-world diagnostics). The model’s utility in resource-constrained settings remains unaddressed.\n> How does REVE perform in zero-shot or few-shot scenarios, where minimal task-specific data is available? The current evaluation focuses on fine-tuning, but real-world applications often have limited labels.\n\nWe conducted experiments in a few-shot (FS) setting, to simulate the use of a BCI system. We use the BCI IV-2a dataset to construct FS tasks with 2 classes (Left-Right). For each subject, we perform multiple inductive FS runs. In each run, we randomly select N labeled samples per class (“shots”) within a session and use the remaining samples from both sessions to evaluate performance. We classify using a Nearest Class Mean method, and repeat the runs 20 times per subject. We report the average balanced accuracy across subjects and runs.\n\nBelow the results with REVE-Base.\n\n|N-shots|1|2|5|10|20|\n|-|-|-|-|-|-|\n|REVE-Base (only SSL)|0.588 ± 1.45|0.601 ± 0.001|0.652 ± 0.013|0.688 ± 0.010|0.723 ± 0.010|\n|REVE-Base + second pretraining|0.605 ± 1.12|0.645 ± 0.009|0.705 ± 0.009|0.768 ± 0.009|0.817 ± 0.004|\n\nTable `G86N.2`: **Few-shot** performance of REVE-Base on BCI IV-2a dataset\n\nThe first row of the table shows the model's performance directly after SSL pre-training, without further adaptation. REVE's embeddings can be leveraged even without supervised fine-tuning or probing, highlighting the potential for direct application to BCI tasks. This level of generalization is rare among BCI embedding models, as most existing approaches require task-specific retraining (e.g., from other subjects in the dataset).\n\nIn the second row, we see the result of a second experiment, involving supervised cross-dataset (MI LR) training on REVE using Left-Right MI open datasets (Schirrmeister 2017, Cho 2017, Physionet MI, Lee 2019, Weibo 2014, and Large). REVE's 4D positional encoding allows the joint pre-training across all electrode configurations without channel alignment or selection typically required in such scenarios. This step improved FS performance across all shot configurations, achieving gains of up to 10% at 20 shots. A summary table of these results will be included in the final manuscript's appendices.\n\nProper zero-shot with REVE would require a second modality (e.g. text, image) to anchor label semantics. Nonetheless, REVE’s high-quality representations enable flexible plug-in classifiers, which is a practical direction for EEG where labeled data is scarce.\n\n## Optimal task specific configurations\n\nWe acknowledge that the ablation on the masking ratios, based Table 18, could be more impactful if we include more results confirming that an optimal setting exists for the majority of the tasks. We thus ran those ablations on 8 DT in order to report the average below.\n\n|Masking Ratio|Frozen Random|Frozen Block|Full Fine-Tuning Random|Full Fine-Tuning Block|\n|-|-|-|-|-|\n|0.25|0.523|0.513|0.612|0.602|\n|0.55|0.550|**0.558**|0.643|**0.665**|\n|0.75|0.519|0.546|0.606|0.655|\n\nTable `G86N.3`: Average ablation results on masking ratios with REVE-Small\n\nThe results comfort our choice of a masking ratio of 0.55, we will consequently update table 18 in the final manuscript, with the 8 tasks.\n\n## References\n\n- Panachakel, Jerrin Thomas, and Angarai Ganesan Ramakrishnan. \"Decoding covert speech from EEG-a comprehensive review.\" Frontiers in neuroscience 15 (2021)\n- Abdullah, Ibrahima Faye, and Md Rafiqul Islam. \"EEG channel selection techniques in motor imagery applications: A review and new perspectives.\" Bioengineering (2022)\n- Défossez, Alexandre, et al. \"Decoding speech perception from non-invasive brain recordings.\" Nature Machine Intelligence 5.10 (2023)\n- Xu, Zhengzhuo, Ruikang, Liu, Shuo, Yang, Zenghao, Chai, Chun, Yuan. \"Learning imbalanced data with vision transformers.\" CVPR 2023"}, {"review_id": "808JGtTENT", "reviewer": "Reviewer_aUp9", "summary": "This paper addresses the limited generalization of existing EEG foundation models across diverse datasets, especially under linear probing. The authors propose REVE, a new pretrained model with a novel 4D positional encoding scheme that enables flexible handling of variable-length signals and electrode configurations. Trained on a large-scale EEG corpus, REVE achieves strong performance across multiple downstream tasks with minimal fine-tuning, offering a promising step toward more robust and transferable EEG representations.", "questions": "- The paper adopts a Transformer-based architecture with both encoder and decoder, but it’s not entirely clear why the decoder is needed. From what I could tell, the model doesn’t seem to involve any autoregressive training — so what role does the decoder play in the overall design? A clearer explanation would help readers understand its purpose.\n- The authors borrow several training techniques from large language models, such as RMSNorm and GEGLU activation. While these components are well-established in NLP, it’s less clear whether they bring real benefits to EEG foundation model training compared to more traditional choices like LayerNorm or GELU. Some ablation or discussion on this would be helpful.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "#### Strengths\n\n- The scale of the pretraining dataset is truly impressive, covering over 60,000 hours of EEG data from 92 different datasets and 25,000 subjects. This kind of breadth has not been seen before in the field of EEG foundation models.\n\n- The proposed 4D positional encoding scheme introduces a fresh idea for handling EEG signals with varying electrode configurations and lengths. It’s a thoughtful design that could help future models better adapt to real-world EEG setups.\n\n- The authors evaluated their model across a wide range of downstream tasks — including motor imagery, seizure detection, sleep staging, emotion recognition, and more — showing both broad coverage and solid performance across the board.\n\n- The results are consistently strong, often outperforming existing methods by a meaningful margin. Especially under linear probing or minimal fine-tuning settings, REVE still delivers competitive accuracy, which speaks well of its generalization capability.\n\n\n#### Weaknesses\n\n- Although the paper claims strong performance under linear probing, this is mostly supported by experiments on PhysioNet-MI. That’s just one dataset, and it’s hard to draw general conclusions from such limited evidence. More cross-dataset comparisons would make this claim much stronger.\n\n- Looking at Table 17, it seems like the secondary loss might actually be the key factor behind REVE’s impressive linear probing performance. However, the authors don’t explain what this loss does or why it helps. It feels underemphasized, especially given how important it appears to be. I’d strongly suggest expanding on this part and running more ablation studies to back it up.\n\n- While the empirical results are convincing, the paper lacks deeper analysis. There’s no visualization or interpretability study showing what the model learns. Also, the advantage of the 4D positional encoding over standard approaches isn’t clearly demonstrated through ablation experiments.\n\n- Some practical details are missing — for example, how many devices were used during training, or how long the pretraining actually took. These are important for reproducibility and understanding the computational cost. There are also some citation formatting issues that should be cleaned up.", "was_revised": true, "final_justification": "The authors have adequately addressed most of my concerns regarding linear probing results, architectural clarifications, and ablation studies. I maintain my positive score and look forward to the open-sourced code and pretrained weights contributing to and inspiring the EEG research community.", "rebuttal": "We thank reviewer `aUp9` for their thoughtful and constructive feedback, as well as for recognizing the breadth, generalization capabilities, and novelty of REVE. We address concerns about linear probing results, terminology, the secondary loss, interpretability results, the performance of our 4D positional encoding, the compute cost of our pretraining runs, and the citation formatting.\n\n## Linear probing results on more datasets\n\n> Although the paper claims strong performance under linear probing, this is mostly supported by experiments on PhysioNet-MI. That’s just one dataset, and it’s hard to draw general conclusions from such limited evidence. More cross-dataset comparisons would make this claim much stronger.\n\nWe agree that broader linear probing evaluation is critical and could improve the impact of the paper. In response, we extended linear probing experiments to all downstream datasets and combined existing results in Table 16 and new experiments per reviewer `rHPq`’s simultaneous request. This results in table `rHPq.1`, from which we indicate the average performance across all datasets:\n\n|Dataset|REVE-B (Pool)|REVE-B (No Pool)|REVE-L (Pool)|REVE-L (No Pool)|CBraMod (Pool)|CBraMod (No Pool)|\n|-|-|-|-|-|-|-|\n|Avg.|0.586|0.609|0.623|**0.654**|0.373|0.501|\n\nTable `aUp9.1`: Average linear probing results on downstream tasks for REVE and CBraMod. Best results are highlighted in bold.\n\nFor more context on the additional results, we reproduced CBraMod (Wang et al., 2025) using the official code and checkpoint, and ran it under the same linear probing setup as theirs across those datasets (with and without pooling).\n\nThese results confirm that REVE consistently outperforms CBraMod under frozen encoder settings, supporting its generalization under minimal fine-tuning. We will move these new results into the main body for visibility.\n\n## Decoder terminology\n\n> The paper adopts a Transformer-based architecture with both encoder and decoder, but it’s not entirely clear why the decoder is needed. From what I could tell, the model doesn’t seem to involve any autoregressive training — so what role does the decoder play in the overall design? A clearer explanation would help readers understand its purpose.\n\nWe thank the reviewer for the helpful comment regarding terminology.\nIn the manuscript, we use the terms \"encoder\" and \"decoder\" in the context of masked auto-encoders, which follow an encoder-decoder structure. For readers more familiar with the Transformer-specific terminology, this can indeed be interpreted as referring to non-causal and causal attention patterns, respectively. To avoid any ambiguity, we will clarify in the revision that all Transformer blocks used in our model are non-causal, corresponding to standard \"encoder\" blocks. We will also explicitly state that no autoregressive training is involved.\n\n## Secondary loss\n\n> Looking at Table 17, it seems like the secondary loss might actually be the key factor behind REVE’s impressive linear probing performance. However, the authors don’t explain what this loss does or why it helps. It feels underemphasized, especially given how important it appears to be. I’d strongly suggest expanding on this part and running more ablation studies to back it up.\n\nWe recognize that including more results on the secondary loss could improve the paper. Following the simultaneous request of reviewer `G86N`, we ran the ablation studies on the full downstream task suite, and will also update table 17 with the complete results.\n\nIn the Table below, we report the balanced accuracy using REVE-Small\n\n|Dataset|no 2nd loss (LP)|no 2nd loss (FT)|2nd loss (LP)|2nd loss (FT)|\n|-|-|-|-|-|\n|Mumtaz|0.818 ± 0.043|0.818 ± 0.043|0.920 ± 0.018|**0.922 ± 0.018**|\n|TUAB|0.797 ± 0.004|0.803 ± 0.003|0.802 ± 0.005|**0.810 ± 0.005**|\n|ISRUC|0.699 ± 0.006|**0.777 ± 0.002**|0.625 ± 0.003|0.770 ± 0.002|\n|HMC|0.598 ± 0.008|0.713 ± 0.011|0.591 ± 0.005|**0.723 ± 0.005**|\n|BCIC2020-3|0.234 ± 0.009|0.390 ± 0.017|0.237 ± 0.008|**0.481 ± 0.008**|\n|TUEV|0.442 ± 0.060|0.533 ± 0.024|0.520 ± 0.005|**0.623 ± 0.011**|\n|PhysioNetMI|0.379 ± 0.058|0.563 ± 0.011|0.533 ± 0.019|**0.583 ± 0.009**|\n|Faced|0.220 ± 0.008|0.302 ± 0.016|0.233 ± 0.004|**0.410 ± 0.004**|\n|Avg.|0.523|0.612|0.558|**0.665**|\n\nTable `aUp9.2`: Secondary loss ablation results on REVE-Small\n\nThe extended analysis supports our claim, showing improved average downstream performance in both LP and FT scenarios. For further intuition on the secondary loss, we refer to our response to reviewer `G86N`. The additional results support our claims in this section and highlight the positive impact of the secondary loss on the quality of the pooled pretrained embeddings.\n\n## Interpretability results\n\n> While the empirical results are convincing, the paper lacks deeper analysis. There’s no visualization or interpretability study showing what the model learns. Also, the advantage of the 4D positional encoding over standard approaches isn’t clearly demonstrated through ablation experiments.\n\nWe acknowledge the value of deeper analysis and interpretability results. We point out that we already provided initial experiments on interpretability at submission time on the public repository. We provide attention maps for Motor Imagery and for Imagined speech decoding in the figs/ folder, along with the code to reproduce them. Per NeurIPS policy, we can not add new interpretability figures beyond what was already provided. We strongly consider a focus on interpretability for future work.\n\n## 4D positional encoding\n\nWe would like to clarify the advantage of the 4D PE over fixed learned embeddings. The main benefit of the 4D positional encoding is handling heterogeneous electrode configurations unknown at training time, rather than strictly outperforming learned embeddings in raw metrics. This is the key interpretation behind Table 19.\n\n## Compute cost\n\n> Some practical details are missing — for example, how many devices were used during training, or how long the pretraining actually took. These are important for reproducibility and understanding the computational cost. There are also some citation formatting issues that should be cleaned up.\n\nWe agree that reporting the training time and necessary compute is important. We will include the following calculation in the final manuscript.\n\nWe first derive the number of floating point operations (FLOPs) required for the training of REVE Base using the following formula derived from appendix B from PALM (Chowdhery et al.,  2023).\n\n$$\n\\tau =  \\frac{D \\cdot (6N+12LHQT)}{P \\cdot \\eta}\n$$\nwhere:\n- $\\tau$ is the training time in seconds\n- $D = 60k * 3600 * 1.1 * 68 * 17$ is the total number of token seen during pretraining (60k hours, 1.1 for the patch overlap coefficient, 68 channels on average and 17 epochs)\n- $N = 72M$ parameters for the base model\n- $L = 23$ layers in the encoder + decoder\n- $H = 8$ the number of attention heads\n- $Q = 64$ the head dimension\n- $T = 68 * 11$ the average number of tokens per sequence (number of channels by 11 patches)\n- $P = 312T$ the peak throughput in TFLOPs at half precision, achievable on A100 GPUs\n- $\\eta = 0.5$ the model flops utilization (MFU), a measure of efficiency of the training run, which we set to 50% as a conservative estimate.\n\nThis results in 260 A100 GPU hours.\n\n## Ablation on normalization and activation layers\n\n> The authors borrow several training techniques from large language models, such as RMSNorm and GEGLU activation. While these components are well-established in NLP, it’s less clear whether they bring real benefits to EEG foundation model training compared to more traditional choices like LayerNorm or GELU.\n\nWhile we argue that many components of our model, including the Transformer backbone itself, are modality agnostic, we agree that those claims could be backed up by more empirical evidence. We acknowledge that many architectural choices are taken from the well established NLP literature, such as using GEGLU as an activation function. To back up our architectural choices, we ran ablations with LayerNorm and GELU, the results of which will be included in the final manuscript.\n\nRMSNorm (Zhang et al., 2019) is empirically modality agnostic, as shown in the original paper where the method is applied on both text and image processing. To further back up this claim, we ran ablation studies with LayerNorm and GELU.\n\n|Dataset|GEGLU + RMSNorm|GELU + RMSNorm|GEGLU + LayerNorm|\n|-|-|-|-|\n|BCIC-IV-2a|**0.581±0.012**|0.560 ± 0.018|0.537 ± 0.018|\n|TUEV|**0.623 ± 0.011**|0.592± 0.010|0.577±0.034|\n|PhysioNetMI|0.583 ± 0.009|**0.586 ±0.009**|0.559 ±0.007|\n|Avg.|**0.596 ± 0.024**|0.579 ± 0.017|0.558 ± 0.020|\n\nTable `aUp9.3`: Ablation results on normalization and activation layers with REVE-Small\n\nOur results show RMSNorm and GEGLU yield the best average performance. RMSNorm also helped stabilize training, consistent with findings in the original paper.\n\n## Citation formatting\n\nWe thank the reviewer for the helpful style suggestion. While the NeurIPS guidelines offer liberty regarding the formatting of citations, we agree that differentiating parenthetical citation and narrative citation improves the overall readability. The final manuscript will include the suggested modifications.\n\n## References\n\n- Wang, Jiquan, Sha Zhao, Zhiling Luo, Yangxuan Zhou, Haiteng Jiang, Shĳian Li, Tao Li, Gang Pan. \"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding.\" The Thirteenth International Conference on Learning Representations. 2025.\n- Chowdhery, Aakanksha, et al. \"Palm: Scaling language modeling with pathways.\" Journal of Machine Learning Research 24.240 (2023): 1-113.\n- Zhang, Biao, and Rico Sennrich. \"Root mean square layer normalization.\" Advances in neural information processing systems 32 (2019)."}]

Author rebuttal:

Meta-review: This paper presents REVE, the largest EEG foundation model to date, trained on 60,000+ hours from 92 datasets and 25,000 subjects. Its key innovation is a 4D positional encoding that enables generalization across variable electrode layouts and signal lengths. REVE achieves state-of-the-art results on 10 diverse EEG tasks with strong performance under both fine-tuning and lightweight adaptation.

The paper is well-executed and impactful, with comprehensive evaluations, meaningful ablations, and a strong commitment to reproducibility through open code and pretrained weights. While interpretability and clinical validation remain areas for future work, the contribution is substantial, addressing long-standing challenges in EEG modeling.
Decision: Accept (poster)