Title: BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Venue: NeurIPS 2025
Accepted: yes
Keywords: EEG

MEG

foundation model

tokenizer

self supervised learning

neuroscience

Brain Computer Interface
Abstract: Electroencephalography (EEG) and magnetoencephalography (MEG) measure neural activity non-invasively by capturing electromagnetic fields generated by dendritic currents. 
Although rooted in the same biophysics, EEG and MEG exhibit distinct signal patterns, further complicated by variations in sensor configurations across modalities and recording devices.
Existing approaches typically rely on separate, modality- and dataset-specific models, which limits the performance and cross-domain scalability.
This paper proposes BrainOmni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings. 
To unify diverse data sources, we introduce BrainTokenizer,  the first tokeniser that quantises spatiotemporal brain activity into discrete representations. 
Central to BrainTokenizer is a novel Sensor Encoder that encodes sensor properties such as spatial layout, orientation, and type, enabling compatibility across devices and modalities.
Building upon the discrete representations, BrainOmni learns unified semantic embeddings of brain signals by self-supervised pretraining. To the best of our knowledge, it is the first foundation model to support both EEG and MEG signals, as well as the first to incorporate large-scale MEG pretraining.
A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining.
Experiments show that BrainOmni outperforms both existing foundation models and state-of-the-art task-specific models on a range of downstream tasks. It also demonstrates strong generalisation to unseen EEG and MEG devices. Further analysis reveals that joint EEG-MEG (EMEG) training yields consistent improvements across both modalities. Code and checkpoints are publicly available at https://github.com/OpenTSLab/BrainOmni


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes BrainOmni, a foundational model for unified representation learning from EEG and MEG signals. It introduces BrainTokenizer, which transforms raw brain signals into discrete tokens by incorporating sensor metadata, enabling robust generalization across diverse devices. The model benefits from joint EEG-MEG pretraining and demonstrates strong performance on downstream tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. BrainOmni is the first electroencephalographic (EEG) foundation model that integrates both EEG and MEG modalities. The authors propose a novel Sensor Encoder that effectively fuses inputs from different modality devices. This contributes insight into the development of multimodal physiological signal foundation models.\n\n2. The authors have curated a large-scale MEG dataset for pre-training.\n\n3. The paper provides detailed descriptions of the pre-training, fine-tuning procedures, and model architecture,  significantly enhancing the reproducibility of the work.\n\n**Weaknesses**\n\n1. While the paper reports using 12 pre-training MEG datasets, only a single downstream MEG dataset is used for evaluation. The authors could strengthen the empirical validation by withholding 1–2 of the pre-training datasets for fine-tuning and evaluation, thereby providing a more realistic assessment of BrainOmni’s generalization capability on MEG-specific tasks.\n\n2. The comparison methods selected for the MEG modality are primarily designed for EEG signals, which introduces a potential bias and leads to an unfair comparison (see Table 3). A more principled set of baselines specifically tailored for MEG would be necessary to draw solid conclusions.\n\n3. The use of a cross-subject 3-fold validation setup raises concerns, especially since it deviates from the data splitting protocols used in prior foundation model studies. It would be more appropriate to adopt the same data partitioning scheme as existing FM models for fair comparison. Under the current setup, the performance of baseline foundation models appears to drop significantly—even when the implementation of these baselines is identical to that of competing methods like CbraMod—suggesting that the results may be sensitive to the chosen splits.\n\n4. Fig. 3(c) shows the trend of latent source variables with respect to reconstruction loss, but the paper lacks analysis of their impact on the overall performance of BrainOmni. As the latent source variables influence the size of the tokens generated by the BrainTokenizer, they likely have a direct effect on the subsequent training of BrainOmni. The authors should provide a more in-depth discussion of this relationship and its implications.\n\nQUESTIONS:\n1. Why was a 25% channel dropout rate selected for the BrainTokenizer? It would be helpful to know whether this value was determined through ablation studies or parameter tuning.\n\n2. What is the individual contribution of each of the four loss functions used in training the BrainTokenizer (see Eq. 5)? A more detailed breakdown or ablation analysis of how each component of the loss function contributes to the overall training performance would strengthen the theoretical foundation of the model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes BrainOmni, a foundation model designed to learn unified representations from both EEG and MEG brain signals. The authors introduce a BrainTokenizer module that converts raw neural recordings—along with sensor-specific metadata—into a shared discrete token representation, enabling compatibility across different devices and modalities. A Criss-Cross Transformer is then employed to capture spatiotemporal dependencies among the tokens and generate contextualized embeddings. The model is rigorously evaluated across a wide range of downstream tasks, where the learned BrainOmni embeddings consistently outperform those produced by existing brain foundation models.\n\nSTRENGTHS AND WEAKNESSES:\nOne key strength of this paper lies in itsimpactful research direction. Developing a foundation model capable of integrating heterogeneous neural data across EEG and MEG modalities addresses a long-standing bottleneck in neuroscience: the fragmentation of datasets and the scarcity of large-scale annotated data for individual tasks. By introducing a unified representation framework, BrainOmni potentially offers a scalable solution to harmonize recordings from diverse sources, devices, and paradigms. \n\nAnother notable strength is the rigor and comprehensiveness of the experimental validation. The model is evaluated across 10 diverse downstream tasks, covering a wide range of cognitive, clinical, and motor domains. The authors conduct extensive ablation studies to isolate the contributions of each architectural component. Further, the paper includes additional analyses to probe the learned network, which adds certain level of transparency to the model’s behavior. The authors also commit to open-sourcing the code and pretrained models, which promotes reproducibility.\n\nOne notable weakness of this paper is its inferior performance on several benchmark datasets compared to task-specific, fine-tuned models. For example, on the PD31 dataset, classical methods using carefully engineered features achieve over 95% accuracy [1], whereas BrainOmni reports only 73%. Similar performance gaps can be found across other tasks in the literature. While BrainOmni demonstrates competitive results relative to other foundation models, it fails to match the state-of-the-art performance achieved by specialized models tailored to specific domains. Admittedly, BrainOmni offers superior generalizability across modalities and datasets, which is a key advantage of foundation models. However, the authors should have cited and compared against these specialized baselines, and provided a balanced discussion of BrainOmni’s strengths and limitations in relation to them. A forward-looking perspective on how brain foundation models can offer practical value, even when downstream accuracy is lower, would strengthen the impact and positioning of this work.\n\n[1] Aljalal, Majid, et al. \"Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques.\" Scientific Reports 12.1 (2022): 22547.\n\nQUESTIONS:\nIs the reconstruction visualization (in main and in supplementary) before or after finetuning? Have you explored the effect of finetuning on Tokenizer and the transformers separately?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper describes a foundation model that is trained to ingest either EEG or MEG brain data. The model consists of a raw data tokenizer (BrainTokenizer) and a Transformer encoder (BrainOmni). The tokenizer is trained as a masked autoencoder, and comprises a sensor encoding scheme to model sensor characteristics such as position, orientation and type. The quantized tokens are used as input to a Transformer encoder which is trained with a masked token prediction task. Both parts are pretrained on a corpus of 1997 hours of EEG and 656 hours of MEG. The model is then reused on 8 EEG downstream tasks and 2 MEG downstream tasks, and its performance is compared to fully supervised baselines and two existing foundation models. Results suggest that the proposed model outperforms baselines on most tasks, and ablation studies suggest that the joint training on both EEG and MEG yields improved performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n* Quality: The submission is technically sound, with claims that are overall well supported by experimental results (evaluation on 9 downstream tasks, testing cross-device generalization on unseen devices, and relevant ablation studies).\n* Clarity: The paper is overall clearly written and easy to follow, though some important information appears to be missing (see Weaknesses).\n* Significance: The results are likely to positively impact the brain foundation model community, as it combines two common modalities (EEG and MEG) that are typically studied independently. It also provides a new architecture/pretraining recipe with an interesting connection to the backward/forward models used in traditional biophysical modelling of M/EEG. Finally, the comparison to existing baselines suggest the proposed model outperforms the state of the art on most downstream tasks.\n* Originality: The paper presents the first model to meaningfully combine MEG and EEG to build foundation models, along with interesting innovations for the handling of varying channel layouts.\n\nWeaknesses\n\n* Clarity: Some relevant information appears to be missing from the manuscript:\n    * In the channel compression step of Section 2.2, it is unclear what the sequence length is for the input of the cross-attention layer (C x W, or only C), and therefore whether the module attends to all timesteps or if each timestep is treated independently. \n    * In the description of the downstream evaluation (Section 3.2): it is not clear how the train/test sets are built, i.e. whether this is done as part of the 3-fold CV split, or if all datasets already provide a fixed test set. I assume that the 3-fold CV is carried out on a training set, but it is not obvious from the text.\n    * Finally, it is unclear how the pretrained BrainOmni is finetuned, i.e. whether a linear projector or MLP is used on top to match the output dimensionality.\n    *I didn’t find the parameter count for the BrainTokenizer in the manuscript.\n* Quality: Depending on how the downstream evaluation was actually carried out, a common optimization configuration (i.e. training for 30 epochs) for all downstream tasks and models may be yielding more severe overfitting in smaller models.\n\nQUESTIONS:\n1. Can you describe the cross-validation/testing strategy in more detail, e.g. with pseudo-code? It is not clear to me whether a first train/test split is done, followed by a 3-fold cross-validation on the training set, or if the 3-fold CV is done over the entire dataset.\n2. How can we be sure that the effect of joint pretraining (results of Table 5) is not mainly a matter of pretraining on a lot more data? It looks like the improvement over the MEG-only model is larger than for the EEG-only model, which could support this interpretation as there is about 3x more EEG data in the pretraining set.\n3. Can you provide more information on the “power-spectral-density-based detection algorithm”? Similarly, can you give more details into what are “reference inconsistencies” (line 184) and how reference signals are used in cases where signals have already been referenced and the reference channel is not available? Overall, I wonder whether the BrainTokenizer might be able to handle these steps directly. Finally, can you confirm whether the bad channel detection is done at the recording- or segment-level?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper presents BrainOmni, a brain foundation model to generalize across EEG and MEG signals. It uses 1997 hours of EEG and 656 hours of MEG data from publicly available sources for pretraining. Their approach consists of BrainTokenizer to infer spatio temporal patterns of brain activities and SensorEncoder to use physical sensor properties. The evaluation results show the approach is promising.

STRENGTHS AND WEAKNESSES:
Strengths
- BrainOmni is a significant step toward a general-purpose cognitive decoding model by leveraging both EEG and MEG data.
- The model is used using 1997 hours EEG and 656 hours MEG data from public sources, the largest I've seen so far.  

Weaknesses
- No clear discussion of training efficiency, hardware requirements, or inference speed, which is crucial for scaling.
- More ablation study on where are the benefits coming from (e.g., increased dataset, using EEG and MEG together, specific design of neural networks). These insights are valuable to the research community.

QUESTIONS:
- What are the computational costs for training and inference? Is BrainOmni feasible for clinical use or only research labs?
- How does the model generalize across subjects with different brain anatomy or signal quality?
- More in-depth ablation study would be helpful to understand the benefits of each component.

LIMITATIONS:
None
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03