BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Authors: Qinfan Xiao
Ziyun Cui
Chi Zhang
SiQi Chen
Wen Wu
Andrew Thwaites
Alexandra Woolgar
Bowen Zhou
Chao Zhang
Venue: NeurIPS 2025 poster ()
Keywords: EEG
MEG
foundation model
tokenizer
self supervised learning
neuroscience
Brain Computer Interface
Primary area: neuroscience_and_cognitive_science
TL;DR: We develop BrainOmni, the first brain foundation model that generalises across heterogeneous EEG and MEG signals.

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

BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Qinfan Xiao 12+ Ziyun Cuil.2+, Chi Zhang Siqi Chen?, Wen Wu' Andrew Thwaites Alexandra Woolgar Bowen Zhou Chao Zhang?.1.4
Shanghai Artificial Intelligence Laboratory, China 2 Department of Electronic Engineering, Tsinghua University, China
Department of Psychology, University of Cambridge, UK Speech Hearing and Phonetic Sciences, University College London, UK
5 MRC Cognition and Brain Sciences Unit, University of Cambridge, UK. {xiaoqf22, uui-zz24)dmails teinghua. edu, en, c2277@teinghua edu, cn
Abstract
Electroencephalograahyy (EEG) and magnetoeencephallgrapyy (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 BrainTokenizze the first tokenizer that quantises spatiotemporal brain activity into discrete representations. Central to BrainTookeiizer is 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 our knowledge, is the first foundation model to support both EEG and MEG signals, well the first incorporate large-scall scgeescale MEG pretraining. A total 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining. Experiments show that OrainOmni outperforms both existing foundation models and state-of-the- the-art task-specific models 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: wtwp:h......mmpe//llllllll...... BrainOmni
Introduction
Neuronal activity underpins human brain function. This activity generates electrical currents in the cortex, which in turn produces secondary electrical and magnetic fields. These fields can be indirectly measured using non-invasive techniques such electroencephalography (EEG) and magnetoen- cephalography (MEG) 42 EEG measures electrical potentials through electrodes placed on the scalp, while MEG uses either gradient amplitude sensors measure the magnetic field specific
"Equal contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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locations and orientations outside the head. As rich sources of neural activity with high temporal res- olutions, EEG and MEG have been widely used in applications such as motor imagery (59] emotion recognition (0), multimodal neural decoding 39 14 71. and clinical assessments [34 88 46
69].
However, the majority of these applications are developed separately for each domain, often tailored to specific tasks, datasets, or recording setups 771 86 34, 26 22] Such specialised models suffer from limited generalisation and poor scalability across tasks and domains. Recently, EEG foundation models have emerged to address these limitations by learning general-purpont neural representations from large-scale data 84 33 78.. Although MEG signals offer significantly higher spatial resolution than EEG, foundation models for MEG remain largely unexplored, likely due to the modality's s complexity and limited data availability. Notably, EEG and MEG share a common biophysical origin, both capturing neural activity through electromagnetic fields generated by dendritic currents. This shared foundation raises a compelling question: can we develop unified model that learns from both EEG and MEG data generalise across modalities and enhance performance in each?
Integrating EEG and MEG signals into a unified foundation model faces two key challenges. First, EEG and MEG exhibit distinct signal characteristics and patterns, posing cross-modality integration challenge. Second, device heterogeneity and lack of standardisation present a significant cross-device generalisation challenge. both within and across EEG and MEG modalities. This heterogeneity includes differences in electrode/sensor configuration (e.g., count, type, position, placement) and naming conventions particularly pronounced in MEG. MEG sensors can differ in both type (gradiometer v.s. magnetometer) and measurement directions (perpendicular v.s tangenital), which adds further complexity to unified modelling.
This paper proposes BrainOmni, the first foundation model for unified EEG and MEG signals. The training of BrainOmni consists of two stages: (i) unifying heterogeneous data into the same feature space; (if) capturing semantic features of brain activity. In the first stage, we introduce Brain Tokenizer. Inspired by source activity estimation (1. BrainTokenizer learns to infer spatiotemporal patterns of brain activity from the observed EEG/MEG signals and generate quantised discrete tokens.
To address device heterogeneity, we propose a novel Sensor Encoder that utilises each sensor's physical characteristics. such as spatial coordinates, orientation, and type, rather than relying solely on channel naming conventions that are often inconsistent across devices and datasets. This design enables Brain'Tokenizer to handle arbitrary EEG/MEG signal inputs, laying the foundation for large- scale joint pretraining. By integrating EEG/MEG signals with sensor metadata, BrainTokeniee infers a set of latent source variables that represent the dominant generative factors underlying electroen- cephalographic and magnetoenccepaalorrppiicc measurements across diverse sensor configurations, thereby unifying heterogeneous data into a common feature space for downstream modelling.
Building on the discrete brain representations produced by semantic representations of neural activity through supe-supe pretraining in the second stage. To support this, we curated and standardised a large-scale dataset comprising 1,997 hours of EEG and 656 hours of MEG recordings. Experimental results show that BrainOmni: (i) outperforms existing methods across a range of downstream tasks; (ii) generalises effectively to previously unseen EEG and MEG devices; and (iii) consistently benefits from joint BEG-MEG (EMEG) training across modalities. Our key contributions are as follows:
BrainOmni learns rich
Joint EMEG pretraining. To the best of our knowledge, BrainOmni is the first single model to perform unified pretraining on both EEG and MEG signals.
Modelling physical heterogeneity across devices. To address the heterogeneity of EEG and MEG recording devices, we propose novel Sensor Encoder that operates independently of electrode naming conventions or fixed topologies, enabling compatibility across both devices and signal modalities.
Spatiotemporal brain signal quantisation. To our knowledge, Brain Tokenizer is the first model that enables spatiotemporal quantisation of brain signals.
2 Method
BrainOmni consists of two training stages. In Stage 1, Brain is developed to discretise heterogencous EMEG signals into semantically rich representations. In Stage 2, the BrainOmni

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Raw EMEG
EMEG Device
Signal
Layout
EMEG Device I Layout
SEANet Encoder
Sensor Encoder
Position Embed Layer
Type Embed Layer
EMEG Device Layout
Sensor Encoder
Brain
V
K
Learnable Q
Raw EMEG
Signal
BrainTokenize
Cross Attention
Latent Source Variable
MLP
Reconstructor
RVQ
Reconstructed Signal
Reconstructoo
K
RMSNoem
Cross Attention
Sensor Embedding
Reconstruction
Loss
SEANet Decoder
Reconstructed EMEG Signal
Figure I: Illustration the training pipeline of BrainTokenizer. Left: Overview of the autoencoder scheme for BrainTokenizer training. Middle: Structure of the BrainTokenizer and Reconstructor. Right: Structure the Sensor Encoder.
model is trained based Boken'nkensszr''s 's outputs with a masked token prediction framework, leveraging longer continuous samples to capture extended temporal dependencies.
2.1 EMEG Signals
EEG measures electrical potentials through electrodes placed on the scalp, while MEG uses either gradiometer (GRAD) or magnetometer (MAG) sensors to measure the magnetic field at specific locations and orientations outside the head. The measured EMEG signals multichannel time-series data, which can be represented as E IR Cxx,, where C denotes the number of sensor channels, and T denotes the number of sampling points. Taking the sensor type, locations and orientations into account, an EMEG sample can be donated as Ω = (X, L, S), where X is the raw signal, E RCx6 the physical position and orientation information of each sensor in Cartesian coordinates, and = 82,,82,.... scl8; E (0 (EEG), (GRAD), 2 (MAG))} is the type of each sensor.
2.2 BrainTokenizer
The overall pipeline for Stage is illustrated in Fig. BrainTokkenierr is trained using masked autoencoder scheme [3], where quantises EMEG samples into compact discrete tokens representing latent features, and reconstructor module recovers the original EMEG signals from these tokens. Stage is trained using a reconstruction loss between the original and reconstructed signals, which enables the BrainTokenizer temporal and spatial features of the raw EMEG signals and device layout.
scheme
generate quantised latent representations which effectively capture
The Brain Tokenizer consists of a SEANet encoder 74 which extracts temporal representations from EMEG signals, and a novel Sensor Encoder, which encodes sensor physical information into sensor embeddings. The temporal representation and the sensor embedding are then fused via specialised cross-attention block, followed by residual vector quantisation (RVQ) 9 for quantisation.
SEANet Encoder. A SEANet encoder 74 is used to extract efficient and compact representations from EMEG signals the temporal dimension, which a convolutional temporal encoder with multi- layer stacked residual I-dimensional (-dim) convolutional block and strided convolutional layers. encodes the raw data to a temporal representation Zume IR CxW xD where W represents for latent steps and D for feature dimensions.
Sensor Encoder. To handle input from different EMEG devices, a novel Sensor Encoder is proposed to support learnable sensor position encoding, which hierarchically fuses physical priors with learned
3

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representations. As illustrated in Fig. the Sensor Encoder contains position embedding layer encode the 3-dim Cartesian coordinates of sensor positions and sensor orientations L E RC'6 and type embedding layer to encode sensor types S = {81, 82; ..... .......
8: E (,,1,2)] The sensor physical information L and are then integrated to produce sensor embedding V RCCD
Channel Compression. The temporal representation Zime and the sensor embedding V are then fed into a specialised cross-attention block, which performs channel compression unify signals with varying numbers of channels into a fixed number of latent source variable. The Value of the cross-attention block is set the temporal representation, Key is the sum of temporal representation and sensor embedding, and set of learnable Query is used to adaptively aggregate information from temporal and spatial representations, producing intermediate latent features Zare E RC'xWxx, where C' the number latent source variables determined as a hyperparameter. The cross attention operation is performed independently for each window, ensuring the consistency of channel compression between different temporal windows.
Quantisation.
The latent features Zere are discretised by a 4-layer RVQ module to discrete tokens Q E R""""Wxx which is used to train the BrainOmni model in Stage 2.
Reconstructor: In the reconstructor, the discrete tokens are first decoded into Zere by the RVQ decoder. Another cross-attention block is developed to convert the quantised latent source variables to continuous channel-wise temporal representations Zume. followed by a SEANet decoder reconstruct X, which an inverted mirror of the encoder using transposed convolutions. is important to note that the Brain Tokenizre and the reconstructor correspond to the backward and forward solution, respectively, traditional EEG/MEG source current activity estimation; detailed explanation can be found in Appendix B
Training the Brain Tokenizer. The Brain'Tokenieer trained following an autoencoder scheme where 25% of the input channels are randomly dropped and the reconstructor is required to recon- struct all original channels from discrete tokens. The overall training loss combines multi-level reconstruction loss with RVQ commitment losses, enabling joint optimisation of the network and the codebooks. The multi-leve reconstruction loss includes time-domain loss between the original waveform and the reconstructed waveform X:
Lime X
X||
where || || denotes LI distance; (ii) a frequency amplitude spectrum A and phase spectrum 4;
rrquuency-domain domain loss between the original and reconstructed
Lie | AlI
+
+ |;
(iii) auxiliary loss based on Pearson correlation coefficient (PCC) to regularise waveform trend consistency:
Spce X)
The codebooks of RVQ is updated using exponential moving average (EMA), with rotation trick employed as the gradient estimator. Donate z; and zg, as the residual and nearest entry of 7th layer in the codebook, respectively, the RVQ commitment loss is defined as:
Lavy =
Iz 2q. 112
where Ng is the depth of codebooks. The Brain' Tokenizer is trained to optimise the following loss:
Lioken Laime
Lireq fapee + fov..
2.3 OrainOmni
Built Brain Tokenize in Stage 1, the BrainOmni model is trained with masked token prediction framework, to jointly model spatial and temporal information, thus learning coherent spatiotemporal representations of the brain activity. The training framework of BrainOmni is illustrated in Fig.
The Brain Tokenizer encodes continuous EMEG recordings into sequence of tokens with shape (C', T, N₂). where O' is the number of the channels after compression, I' is the sequence length, and

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EMEG Device
Raw EMEG
Layout
Signal
X
80% mask token
Brain Tokenizer
Z
randorn replace
Brain Omni
Criss-Cross Transformer
X
Chasndl
XIX
Criss-Cross Transformer
Time
CE Loss
Figure 2: Illustration of the training framework of BrainOmni.
Ng is the codebook depth, which is set to 4 in our implementation. Tokens are randomly masked at predefined ratio and subsequently projected through an embedding layer into representations of shape (C*, T, D). The embeddings are then processed by multiple layers of the Criss-Cross Transformer [78] to jointly model the spatial and temporal dependencies. Finally, the output representations from Transformer blocks utilised predict the masked tokens.
Criss-Cross Transformer. The BrainOmni model consists of multiple Criss-Cross Transformer blocks (78). Given the multi-channel time-series nature of EMEG data, joint modelling of spatial and temporal contexts is crucial. The Criss-Cross Transformer divides the input into two halves along the feature dimension: one half is used for spatial attention computation, and the other half for temporal attention. These two halves are then concatenated and passed through a feedforward layer. Additionally, rotary position embedding (RoPE) [72] is applied specifically temporal attention to encode positional information along the time dimension.
Training the BrainOmni Model. During BrainOmni training, 50% of the positions in the (C', T) token grid are randomly masked. For each masked position, all four RVQ layers are masked and simultaneously predicted in non-autoregressive manner.
To prevent over-reliance on special mask tokens, 80% the masked positions are replaced with dedicated mask tokens, while the remaining 20% are substituted with randomly sampled tokens from the sequence. The model training loss is:
M E
N₂ Leo (gigg Vis),
Lmodel
(6)
M
where 4i is the ground-truth codebook index at RVQ layer of token i, Mi is the corresponding model prediction, M denotes the number of masked tokens, and Lee(:,)) is the cross-entropy loss.
3 Experimental Setup
3.1 Pretraining
Pretraining Datasets. 1997 hours of EEG data and 656 hours of MEG data collected from open- source datasets are used for pretraining (see Appendix Hor details), including EEG devices with channel numbers ranging from 19 to 128 and MEG devices with channel numbers ranging from 157 to 306. Among them, 85% of the data is used for training, 10% of the data is used for validation, and 5% of the data is used for testing- Additionally, one EEG dataset and one MEG dataset, which were both collected with unique device systems different from those in the training data, were excluded from the training data evaluate the model's cross-device generalisation ability.
Data Preprocessing. Minimal and standardised preprocessing was applied to maximise data utilisation. 1H-996 Hz bandpass filter and 50/60 notch filters were first applied to remove slow drifts and power-ling line noise, and MEG recordings with HPI coils were additionally filtered in their
5

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Table Overview of the downstream datasets. Duration in seconds.
Modality
Task
Dataset
# Subject
# Channel
Duration
# Segment
# Class
Alzhcimer's Discase
AD65
43
65
19
10.0
5349
Depression
MDD
44
35
20
10.0
7302
Purkinson's Discase
PD31
56
31
32
10.0
882
2
Abnormal
TUAB
47
2328
21
10.0
408853
EEG
Event
TUEV 47
294
21
5.0
112237
Emotion
FACED [9]
123
30
10.0
10332
Motor Imagery
WBCIC SHU
88
4.0
30591
Motor Imagery
PhysioNet-MI
[88
109
4.0
9837
MEG Autism Spectrum Disorder
ASD74
[1
74
306
10.0
12320
Depression
MEG-MMI
40
269
30.0
1770
EMEG
Motor Response
SomatoMotor
37
5
372
2.0
1208
2
HPI frequency bands. All signals were then resampled to 256 Hz. Bad channels were identified via a power- sewver--petralldensitty -based detection algorithm (see Appendix[[]] []] for details) and subsequently interpolated. To address reference inconsistencies and wide amplitude variations, reference values are first subtracted across all sensors of each type within each sample. Then, each channel normalised to zero mean and unit variance at the sample level. Sensor coordinates and orientations are obtained from either the dataset-provided positions or the device's standard montage.
3.2 Evaluation
Downstream Datasets. Eleven different datasets for nine separate tasks are collected to evaluate the performance BrainOmni. Overview of datasets, including modality, tasks, number of subjects, channels, segments, categories, and duration of each segment, are listed in Table I and details are listed Appendix [ To prevent information leakage, all datasets used for downstream testing were excluded from the pretraining process. Consistent with pretraining, bandpass filtering, notch filtering, resampling and normalisation were applied for downstream dataset preprocessing.
Baselines and Downstream Settings. The proposed method is compared to four specialised EEG models: CNN-Transformer [52], Contra WR [S SPaRCNet (35) and ST-Transformer (70), and a specialised MEG model: FAMED (3.. The specialised models were implemented without
pretraining. Two additional EEG foundation models are included for EEG data: LaBraM 33 and
CBraMod [78]. Details of these baselines can be found in Appendix
To ensure fair comparison, BrainOmni is compared all baselines using the same preprocessing procedures, training pipeline and downstream evaluation strategy. The only exception is that in the preprocessing of LaBraM and CBraMod, we followed the same setting of sampling rate and bandpass filter as their preprocessing, All experiments were conducted under 5-fold cross-validation setup, where each split allocated three folds for training, one for validation, and one for testing. Each configuration was run under two random seeds. To evaluate the model's generalisation across subjects, a strict cross-subject split strategy was applied all datasets where subjects in the training set do not appear in the validation or test sets. Given class imbalances in some datasets, balanced accuracy (BACC) was chosen as the primary evaluation metric. The mean and standard deviation of all 10 runs were reported. Models were trained for 30 epochs, and the model checkpoint that achieved the highest validation-set BACC was selected for testing. More details downstream training can be found in Appendix D
4 Results
4.1
Downstream Evaluation
BrainOmni was evaluated on downstream EEG, MEG, and EMEG tasks. Results on EEG datasets are shown Table Results on MEG and EMEG datasets are listed Table It can observed that BrainOmni achieves the highest performance on all tasks, with close second-best place on the PhysioNet-MI dataset. Specifically, on EEG data, BrainOmni base's 's balanced accuracy on the AD65 and PD31 datascts is about 10% higher than the best pretrained bascline LaBraM. On the

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Table Baseline comparison on the eight EEG datasets. Balanced aceuracy is reported with mean + standard deviation. "PT" stands for whether the model involves pretraining. The best results are shown in bold, and the second-best results are underlined.
# Param
PT
AD65
MDD
PD31
TUAB
NNN-Transformer 52
0.695 + 0.118
0.846 + 0.096
0.536 + 0.090
0.795 + 0.024
ContraWR 85
X
0,660 + 0.104
0.863 + 0.068
0.521 + 0.069
0.800 + 0.012
SPaRCNet 135
x
0,582 + 0.085
0.809 + 0.051
0,534 + 0.081
0.779 + 0.017
ST-Transformer 770
X 0.604 + 0.080
0,835 0.040
0,530 0.081
0.793 0.008
LaBraM (33
5.8M
V
0.711 + 0.060
0,880 + 0.037
0.659 + 0.188
0816
CBraMod 78
4.9M
0.681 + 0.040
0.871 - 0.048
0.584 + 0.127
0.808 0.007
BrainOmmi tiny
8.4M
0.795 + 0.030
0.886 + 0.043
0.736 + 0.116
0.819 0.004
BrainOmmi base
33M
0.828
0.030
0.877 + 0.052
0.748 0.139
0.819 + 0.005
# Param
PT
TUEV
FACED
WBCIC SHU
PhysioNet-MI
52
0.331 + 0.047
0.357 + 0.011
0.666 + 0.008
0.337 + 0.019
ContraWR 85
0.371 0.030
0.347 + 0.011
0.579 0.031
0.385 0.026
SPaRCNet
0.362 0.036
0.377 + 0.012
0.748 0.018
0.456 0.016
ST-Transformer
0.392 0.032
0.401 + 0.008
0.749 0.007
0.398 0.015
LaBraM [33
5.8M
0.588 0.017
0.458
0.014
0.831 + 0.015
0.561 0.015
CBraMod 78
4.9M
0.525 0.021
0.441 0.014
0.822 0.016
0.595 0.015
BrainOmni tiny
8.4M
V 0.603 + 0.024
0.472 + 0.018
0.825 + 0.015
0.580 + 0.019
BrainOmni base
33M
V 0.622 + 0.028
0,490 + 0.013
0.832 + 0.013
0.590 + 0.022
Table 3: Baseline comparison on the ASD74 (MEG), MEG-MMI (MEG) and SomatoMotor (EMEG). Balanced accuracy are reported in mean + standard deviation. "PT" stands for whether the model involves pretraining. The best results are shown in bold and second-best results are underlined.
Param
PT
ASD74
MEG-MMI
Somato.Motor
FAMED
31
X
0,555 + 0.038
0.527 + 0.100
0,500 + 0.016
CNN- ONN-Transformet
52
X
0,517 + 0.034
0.549 i 0.074
0.503 : 0.014
ContraWR
85
x
0.502 + 0.009
0.528 1 0.035
0.491 + 0.020
SPaRCNet
R5
x
0.604 + 0.053
0.581 + 0.109
0.541 + 0.027
ST-Transformer
70
x
0.583 + 0.034
0.561 + 0.054
0.661 + 0.047
BrainOmni tiny
8.4M
V
0.621 + 0.048
0.610
0.057
0.863 + 0.128
BrainOmni base
33M
V 0.651 + 0.043
0.604 0.061
0.832 + 0.064
MEG ASD74 dataset, it exceeds the strongest baseline by about 5%, and on the EMEG SomatoMotor task, it outperforms the top baseline by 20%. The results demonstrate that BrainOmni consistently outperforms both specialised and other pretrained foundation models across diverse downstream tasks and modalities, including EEG, MEG, and EMEG data, exhibiting strong generalisation and versatility.
4.2 Cross Device Generalisation
To evaluate Brainiokenizer's generalisation to entirely unseen EEG and MEG device systems, we selected two datasets, Perceivelmagine (6] (EEG, SynAmps2 system) and Gloups-MEG (76) (MEG, Neurolmaging system), whose recording setups were not included in pretraining data. We compare the zero-shot reconstruction losses of these unseen datasets against those of the known-device EEG and MEG test set whose training partitions have been used in training BrainTokennzes
Results are shown in Table For EEG, the zero-shot results on Perceivelmagine even consistently outperform our EEG test set across all metrics, highlighting strong generalisation to a new EEG device system. For MEG, although the reconstruction losses of Brainnokennierr on Gloups-MEG are slightly worse than those of our own MEG test set, the relatively strong PCC value of 0.695 still indicates the model's competitive generalisation capability for MBG devices. The slight performance degradation MEG devices may be attributed to the relatively limited amount of MEG training data.
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Table 4: Zero-shot reconstruction results of BrainTokennierr on unseen devices. "AMP" stands for amptitude MAE. PHASE stands for phase MAE. "1" indicates the higher the better. " indicates the lower the better.
Modality
Data
Seen Device
MSE
MAE
AMP
PHASE
PCC 1
our EEG Test Set
0.404
0.449
0.142
1.506
0.748
EEG
Perceivelmagine [6]
x
0.343
0.415
0.137
1.457
0.802
our MEG Test Set
0.473
0.522
0.220
1.469
0.711
MEG
Gloups-MEG [76]
x
0.567
0.572
0.206
1.517
0.695
Table 5; Comparision of BrainOmni models pretrained using EEG-only, MEG-only, and joint EMEG data. Tiny version used for all models.
Dataset
TUAB
TUEV
ASD74 MEG
SomatoMotor MEG
Modality
EEG
EEG
EEG
EMEG
EEG only
0.811
0.006
0.583 + 0.020
0.766
0.070
MEG only
0.553 + 0.062
0.802
0.141
EMEG
0.819 0.004
0.603 +: 0.024
0.621 + 0.048
0.783 + 0.076
0.838 0.128
0.863 0.128
4.3 EMEG Joint Pretraining
To investigate the effect of joint EEG and MEG training model performance, two BrainOmni variants were trained using only EEG or only MEG data. The variants are compared to the BrainOmni model on EEG datasets (TUAB, TUEV), MEG dataset (ASD74), and multimodal dataset (Somato- Motor). For the SomatoMotor dataset, downstream experiments were conducted with EEG-only, MEG-only, and combined EMEG inputs separately. As shown in Table[5] joint EMEG pretraining consistently outperforms single-modalle ASD74 dataset, the jointly pretrained EMEG model shows a 12% relative BACC improvement pared with the MEG-only model. The results highlight the benefit of multimodal training, particularly for the MEG modality with less accessible pretraining data. Additionally, comparing the EEG, MEG, and EMEG inputs processed by the EMEG model on SomatoMotor, the combined EMEG input achieves better performance than both EEG and MEG inputs, underscoring the effectiveness of jointly leveraging EEG and MEG data in downstream tasks.
pretraining across all datasets. Notably. for the MEG
5
Analysis
5.1
Effectiveness of Sensor Encoder
An ablation study is conducted to assess the effectiveness of the proposed Sensor Encoder in mod- elling the spatial characteristics of sensors. Apart from directly removing the sensor embedding, we also develop pure temporal version of Brain Tokenizer where multi-channel EMEG data is treated as uncorrelated single-channel data. As shown in Table the standard BrainOmni consistently outper- forms the pure temporal model. And the exclusion of sensor embedding significantly undermines the downstream performance, especially on challenging MEG and EMEG datasets, which demonstrates the effectiveness of the proposed Sensor Encoder.
5.2 Impact of Loss Items in Brain TrainToo
The Brain training loss contains four components: time-domain loss, frequency-domain loss, Pearson correlation loss, and RVQ commitment loss. Given that the time-domain and commitment losses are essential for training, we conducted an ablation study for how frequency loss and PCC loss
Table 6: Ablation study of sensor embedding (SE). Tiny version used. Balanced accuracy reported.
Model
TUAB
TUEV
AD65
ASD74
SomatoMotor
BrainOmni
0.819 0.004
0.603 + 0.024
0.795 + 0.030
0.621 + 0.048
0.863 + 0.128
-w/o SE
0.784 0.005
0.607 + 0.016
0.730 - 0.064
0.568 + 0.070
0.744 + 0.076
Pure temporal
0.788 0.003
0.526 0.016
0.763 + 0.047
0.571 + 0.053
0.766 + 0.050
8

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Table 7: Reconstruction results of Brain w or w/o freq loss and PCC loss. Total loss denotes the sum of all four loss items.
Freq loss
PCC loss
MAE /
AMP +
PHASE
PCC1
Commitment +
Total Loss +
0.799
0.256
2.154
0.002
5.01c-7
4.207
0.471
0.160
1.583
0.716
1.64c-4
2.517
0.462
0.167
1.863
0.751
1.83e-
2.703
0,480
0.165
1.495
0.723
1.48c-4
2.626
0.8
Train Loss
2.2
Eval Loss
0.4
Layer
Layer
1o8s
Layer
0.2
0.5
0.4
0.0
52 Number of Ialent source variables
Epochs
Epochs
(a) Latent source variables
(b) Learning curve
(c) Different codehook layers
Figure 3: (a) Trend of L1 loss over the number of latent source variables. (b) The training loss and validation loss curves during the training phase of BrainTokenizer (c) The accuracy curves for parallel mask prediction on the labels of each codebook layer during the training phase of BrainOmmi on the training set.
influence the reconstruction performance. Results are shown in Table 7 With only time-domain and commitment loss, the decoder outputs collapsed into near-constant signals and got poor performance. Introducing the requenccydomall loss greatly improved spectral detail (lower MAE/AMP/PHASSE. but led to spurious frgg-fryy impulses. The PCC term mitigates these impulses by aligning overall waveform trends, yet on its own provides weaker spectral reconstruction. All four components included in the Brain Tokenizer training, suppressing the impulse while preserving much frequency-domaie detail as possible.
5.3 Number of Latent Source Variables
To investigate the effect of the number of latent source activities, we trained variants of Brain Tokenizer with different numbers of latent source variables. Results are shown in Fig. 3a As the number of source activities increases, the EMEG signal reconstruction loss gradually decreases. When the number of source variables reaches beyond 16, the trend of loss reduction weakened with further increases in the number of latent source activities. To balance the amount of information retained and computational efficiency, we chose 16 as the number of latent source variables.
5.4 Pretraining Stability and Codebook Contribution
To analyse the training stability when incorporating diverse devices and signals into pretraining, we plot the training curves in Fig. 3b Overall, BrainTokennierr converges relatively quickly, and the validation loss remains consistent with the training loss. This demonstrates that the proposed BrainTokeniee training framework handle different devices and signal types well. Fig. 3e plots the mask prediction performance for each RVQ layer. It can be seen that the first codebook aggregates richer semantic information and achieves higher accuracy in mask prediction, while the prediction accuracy decreases progressively for the subsequent lower-level codebooks.
5.5
Visualisation of Reconstruction Results
Fig. illustrates the reconstruction performance of Bokenzoke We compare the waveforms and topographic maps before and after reconstruction to show the model's ability to capture the spatiotemporal electromagnetic fields of EEG and MEG in both temporal and spatial domains. The results show that the model effectively preserves the main trends and finer details of the waveforms,

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(a) Device seen in pretraining.
(b) EEG new device.
(c) MEG new device.
Figure 4: The waveforms and topographies of the reconstructed and original EEG signals: (a) the standard 10-20 system that was seen during the pre-training phase; (b) the Synnaps system not seen during the pretraining phase; (c) the Neurolmaging system not seen during the pretraining phase.
while smoothing out high-frequence noise spikes. In terms of spatial representation, the reconstructed topographic maps maintain the original activation patterns and structural integrity. Notably, the model also achieves strong reconstruction performance on previously unseen devices and samples, demonstrating robust generalisation across different recording setups.
6 Conclusion
In this paper, we introduce BrainOmni, the first brain foundation model that generalises across heterogeneous EEG and MEG signals. To model signals from different devices and modalities, we propose BrainTokenizer, which infers spatiotemporal patterns of brain activity from the observed EMEG signals and generates quantized discrete tokens. To address the inherent heterogeneity arising from various recording devices, we develop a flexible Sensor Encoder that leverages physical sensor properties. Through large-scale self-supervised joint pretraining EEG and MEG data, BrainOmni significantly exceeds the performance existing foundation models and state-of-the-an task-specific baselines across various downstream tasks, and demonstrates effective generalisation capability to unseen devices and modalities. Furthermore, extensive ablation analysis highlights the consistent benefits of joint EMEG pretraining, underscoring the advantage of unified modelling strategies. We believe BrainOmni represents an important step towards building versatile and scalable foundation models for neural recordings, opening new pathways for unified brain signal representation learning.
7 Acknowledgements
This work was supported by the New Generation Artificial Intelligence National Science and Technology Major Project of China (Grant No. 2025ZD0121800 and the National Natural Science Foundation of China (Grant No. 62476151).
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A Broader Impacts and Limitations
As the first foundation model enabling unified analysis across EEG and MEG modalities, BrainOmni represents crucial step toward generalizable and scalable neural representation learning, offering substantial application potential in neuroscience research, health monitoring, and clinical diagnosis. Additionally, the model's 's enhanced generalisation heterogeneous devices could reduce the perfor- mance degradation typically associated with device and dataset variability. While may not always outperform domain-specinf models tailored to dataset one task, offers broader coverage and generalisability across datasets, tasks, and modalities - key advantage for scalable and reusable neuro-Al systems.
Here we analyse the limitations of the work. Firstly, the scale of EEG and MEG data used for pretraining is still relatively limited. In particular, due to the limited availability of publicly accessible MEG datasets, only 656 hours MEG data were collected for the pretraining phase. Secondly, the downstream evaluation
MEG and EMEG modalities is constrained by both the scarcity of suitable datasets and the current lack of standardised test paradigms. In future work, we plan to incorporate larger and more diverse collection of datasets, especially across the MEG and EMEG modalities, to strengthen model performance and validate generalisation. Furthermore, we will extend framework include additional modalities of neural signals beyond EEG and MEG to develop more comprehensive and unified representation learning approach for neural activity recordings.
B Neuroscience Background
B.1 Source Current Estimation
Source current estimation seeks to infer the spatiotemporal distribution neural currents within the brain from non-invasive scalp measurements. It is an "ill-posed" inverse problem: it is impossible to unambiguously determine the three-dimennional source current distribution inside the brain that gave rise to those measurements even perfectly record full electric and magnetic field distribution around the head [29]. The distributed source model [12 50 was developed to solve this problem, using large number of dipoles or monopoles distributed within the brain volume or the cortex, making the problem linear. A commonly used and well-tested method is minimun norm estimation (MNE), which obtains the maximum posteriori probability estimate of source activity by constructing linear inversion operator W under regularized conditions [25] This method has been commonly used in multiple neuroscience domains, including but not limited audio decoding 73) emotion
analysis [53], and epilepsy diagnosis [3 18
Broadly speaking, MNE involves two main stages: first, computing the forward solution (also known as the leadfield matrix); and second, estimating the inverse operator (or backward solution) that maps sensor data back to source space.
The Forward Solution The forward problem in EEG and MEG refers to computing the scalp electric potentials or magnetic fields that would be measured given known distribution of source cur- rents within the brain. Under the quasi-static approximation of Maxwell's equations, the relationship between sources and measurements is linear and can expressed as:
y() = Li( + n(f)
where yt) E IR M is the vector of sensor measurements at time 1, (,)( E R is the source current distribution, L E MMN is the leadfield matrix, and n()) is measurement noise 25 5
Estimating the leadfield Matrix from the Head Geometry and Conductivity The leadfield matrix L encapsulates how each source location contributes to the sensor array and is calculated using head model. This model is constructed from anatomical MRI, using numerical methods such as the boundary element method (BEM) or finite element method (FEM), and incorporates realistic tissue conductivities.
The human head typically modelled as a piecewise homogeneous volume conductor with com- partments representing scalp, skull, cerebrospinal fluid (CSF), and brain. Each region is assigned an
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isotropic or anisotropic conductivity value di, and the electrical potential ø in the volume conductor governed by the Poisson equation:
(d(r)V((r) V Jp(r)
where (r) is the spatially varying conductivity, and J (r(rr is the primary current density, confined to the gray matter (cortex). For EEG, the scalp potential at sensor location I'm is obtained by solving this equation using numerical methods like the boundary element method (BEM) finite element method (FEM):
V(rm) - / G(rm, r'VV Jp((') dr'
(9)
Here, Grrm, r') is the Green's function representing the head's geometry and conductivity structure, while Ω the brain/source volume. The leadfield matrix L used in the forward model is computed by solving this equation for each source element and each sensor.
In the case of MEG, the magnetic field B generated by primary current J, is computed using the quasi-static Biot-Savart law:
Jp(r') x (rm r') r/
Brrm) 4x Jn
dr
(10)
F'm
Since MEG is insensitive to radial currents and largely unaffected by conductivity discontinuities (like the low-conductivity skull), it is more robust to inaccuracies in the conductivity model [5]. In contrast, EEG is highly sensitive to these properties, especially the skull's low conductivity (~ 0.0042 S/m compared to the brain's 0.33 S/m) [77].
To construct realistic forward models, anatomical MRI data is segmented to extract surfaces or
volumes of different tissue types. BEM solves the boundary integrals over these surfaces, assum- ing piecewise constant conductivities, while FEM can accommodate more complex, anisotropic conductivity distributions within each region [8]
The Backward Solution and Linear Inverse Operator The inverse problem - estimating the sources j(t) from the measurements yt) is ill-posed and requires regularization. Minimum-norm estimation solves this by seeking the source configuration with the smallest f2-norm that still explains the data:
3(1) arg min (ly(() Lj(! + X |0(0)
(11)
where is regularization parameter controlling the trade-off between data fidelity and source power 25 28
This yields a closed-form solution:
i(1) Wy()), where W L (LL + C
(12)
Here, Cn the noise covariance matrix. When whitening is applied, we define:
L = C/-1/2
= -,
(13)
and compute the inverse operator as:
W GL T m LT + x1)
(14)
where G is a source covariance matrix, often the identity or a depth-weighted diagonal matrix to compensate for depth bias (38].
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Raw EMEG
EMEG Device
Signal
Layour
SEANee Encoder
Sensor Encoder
Backward Solation
EMEG Device Layout Raw EMEG Signal
K
Learmable Q
Backward Solution
Cross Atlention
Brain Source Activity
Latent Source Variable
RVQ
Forward Solution
Forward Solution
Reconstructed EMEG Signal
K
Cross Atlention
SEANet Decoder
Reconstructed EMEG Signal
Figure 5: Correlation between source current estimation and the proposed Brain'ookeizzrr (excluding RVQ) can be viewed as the backward solution in source current estimation, and the reconstructor can be viewed as the forward solution.
The
B.2 Model Explanation From the Perspective of Neuroscience
Our Brkiniokenizer design closely parallels the classical source current estimation process, as illustrated Fig. 5 As introduced above, the backward solution in source current estimation infer brain source activity from externaly measured signals and sensor layout information, and forward solution reconstructs the external signals from the inferred brain source activity.
Inspired by this conceptual framework, our proposed Bokinzokenizer closely mirrors these two steps. Specifically, the Brainiokenizer (excluding the RVQ quantisation step) corresponds directly to the backward solution stage, where it maps the EMEG signals and sensor device layout into latent source variables through parametric module. Recall the classical backward solution,
(15) which relies fixed linear inverse operator W constructed from a leadfield matrix L and noise covariance matrix C,, While in our architecture, we use the attention weights in cross-attention mechanisms to construct a time-varying parametric inverse operator W (Zune S),, where the operator related to sensor prosperity and the measured data since the different fields recorded outside may indicate that there is change in the pattern of neuro activity, formulated as:
j(t) Wy(t), where = (LL + Cn)
K Ziime V),
(16)
Wo L,S) softmax (QK
(17)
Vdhcad
where Q is a set of learnable query embeddings that dynamically aggregate temporal and spatial information, K is the combination of temporal representation Zúie from SEANet encoder and sensor embeddings VLL, S), and dhead is the scaling factor determined by the attention head dimension. Different from traditional linear, fixed operators, these learned attention weights naturally serve as flexible and data-driven inverse operators, adaptively integrating spatiotemporal sensor properties and temporal signal dynamics to estimate latent variables. Appendix G provides a visualisation for attention weights of cross attention.
Following this analogy, the reconstructor corresponds to the forward solution, reconstructing the original observed signals from the inferred latent sources. Similar to the backward process, the reconstructor also uses cross-attention mechanism, now employing latent source variables as keys and sensor embeddings as values. Through the decoder module (SEANet decoder), these features are further translated back into reconstructed EMEG signals.
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C Related Work on Brain Foundation Models
foundation model a deep learning model pretrained on large-scale datasets, generally approached by self-supervised learning (SSL), to learn signal representations from abundant unlabelled data and reduce dependence on labelled samples. This paradigm has achieved notable success in computer vision 154 122, natural language processing [17 [55 49 and speech 4 ratio, high dimensionality and individual variability, making large-scale pretraining for robust representation and general brain model important. In EEG domain, BENDR (36), inspired by Vav2 2.0 model, employs contrastive learning method to learn from massive anouts of EEG data. Since then, more model was proposed for more generalized and stronger representation. BIOT 84 enable cross-data learning with mismatched channels, variable lengths, and missing values by tokenizing different biosignals into unified "sentences" structure. MMM [89] adopts topology-agnostt scheme and implements multi-dimensional position encoding, multi-level channel hierarchies and multi-stage pretraining. LaBraM [3] segments EEG signals into channel patches, discretizes them via vector quantization before training with masked EEG modeling to capture high-level semantics. CbraMod [78] introduces Criss-Cross Transformer to fully leverage EEG's spatiotemporal characteristics. Beyond EEG-only approaches, multimodal brain foundation models have also been developed before. For instance, Brain Wave (90) and PopT [8] jointly train on EEG and intracranial EEG (iEEG) data. However, despite MEG's superior spatiotemporal resolution and its frequent combined use with EEG in neuroscience, foundation model research for MEG and joint EMEG remains largely unexplored.
32 [O]. Brain signals,
especially EEG and MEG, show low signal-to-
D Implementation Details
We trained BrainTokknnizer using 2-second segments. For training BrainOmni,, we inputred 30- second data segments to allow the model to capture longer temporal dependencies. During the segmented tokenization process, we set the overlap ratio between windows to 25% to incorporate partial contextual information. The training was conducted on 16 A100 GPUs, using the AdamW optimizer and warmup- warmup-cosine-deee cosine-decay learning rate scheduler with warmup proportion of 10%. Brain Tokenioeee was trained for 16 epochs with a total batch size of 512 per update step and a maximum learning rate of 2e-4. BrainOmni was trained for 32 epochs with total batch size of 256 per update step and a maximum learning rate of 4e-4. The Trkeiieo training took approximately 11 hours, and BrainOmni required about 14 hours for the tiny model and 18 hours for the base model.
For downstream evaluation, all models follow a unified training pipeline. The output embeddings from each model are first average pooled along the temporal dimension, then flattened across remaining dimensions to serve as feature, which are subsequently fed into a two-layer MLP for classification. Taking BrainOmni as an example, for batch of samples, the model outputs embedding with shape (B, C², T. D), where B represents for batch size, C" the number of compressed channels, T temporal sequence length, and D embedding dimension. The embeddings is firstly average pooled over T. and then flattened to (B, C* D), before fed into the MLP classifier.
The evaluation was conducted in a cross-subject five-fold cross-validation paradigm. For datasets without an official split (i.e,, datasets except TUAB and TUEV), all subjects were divided into five subsets, and five experiments were conducted in rotation. In each experiment, three folds were used for training, one for validation, and one for testing, ensuring that every subset served as the test set once, thereby providing more comprehensive assessment of the model's performance. For TUAB and TUEV which provided an official train/eval split, we kept the eval set for all testing, and split the official training subjects equally into five folds, and in each runs use four folds for training and remaining fold for validation. All experiments were run under two random seeds (42 and 3407) and seed 42 is used for data splitting. Experiments were conducted with learning rates of [3 x 10 1 x 10-5 5, 3 x 10 5 for each model, and the learning rate yielding the highest result was selected to obtain the best performance of each model. All other hyperparameters remained consistent across experiments (specific values are provided in Table [2). On single A A100 GPU, the training throughput was approximately 60 samples/sec, while inference achieved roughly 90 samples/sec.
E Preprocess Details
The power-spetral density-bascd bad-channel detection is implemented as Alg.
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Algorithm 1: Powerr-ppecrra-ddesiilyy-bss bad-channel detection
Input vraw_data, threshold 10
Output:bon bhad_channees
1. Compute per-channel PSD over the full recording:
PSD + COMPUTEPSD( (raw .ata
Stabilise and log-transform:
L. + I-og(PSDD 10-16)
3. Compute pairwise distances between channel spectra:
for 4 to C7 do
for j + 1 to C' do dist[i, Lii L
end end
4. Mean distance per channel:
mii] f- mean, (dist(i.j]) 5]) for lo..,,
5. Identify outliers via IQR:
Q1 + percentile,,,m) (m), Q3 + percentilers(m) IQR t Q3 Q1
upper t Q3 + threshold IQR, lower +- Q1 - threshold IQR
bad__haaa chhanneas < (ch; m|i] > upper V mii] < lower) }
To address the reference inconsistency caused by different recording setups, we first compute a global average reference by taking the mean across all channels, and then subtract this channel-average signal from each channel waveform, effectively aligning all channels to the same virtual reference. This protocol applied regardless of whether a reference channel is unavailable or the signals have already been referenced, and is performed both at the recording level and separately for each signal type.
F
Ablation Study
F.
Latent Source Activity Estimation
As mentioned in the Introduction, modelling at the electrode level can encounter issues such as low information density and redundant information in adjacent channels. The latent source activity modelling process involves compressing the sequence length, which, while condensing semantic features, may also introduce some loss of detail. To further demonstrate the advantages of modelling source activity compared to modelling at the electrode level, we trained model that integrates spatial features through self-attention between electrodes, which would not involve loss of information. To be more specific, the Tokenize of this model replaces the cross attention layer with self-attention layer. Channel random masking and additive noise are also applied before the self-attention layer in the encoder part of the feature input. In the decoder part, the masked channels replaced with mask token, and the waveform of the masked part is recovered by a self-attention layer. Results are show in Table g
We can see that even though models using latent source activity estimation may lose some information during the sequence length compression process, their performance on datasets such as TUAB (23), PD31 (32), AD65 (19), ASD74 (306) and SomatoMotor (372) still exceeds that of models built at the electrode level. The numbers in parentheses represent the number of channels in each dataset. Moreover, since the computational complexity of the Transformer model grows quadratically with sequence length, models using LSAE have significantly higher computational efficiency compared to those that do not use it. This demonstrates that the process of LSAE can eliminate redundant
Table 8: Balanced accuracy results of ablation study for latent source activity estimation (LSAB)
TUAB
PD31
AD65
ASD74
SomatoMotor
tiny w/o LSAE
0.813 + 0.003
0.627 + 0.158
0.768 + 0.056
0.606 + 0.088
0,801 - 0.078
tiny w/ LSAE
0.819 0.004
0.736 + 0.116
0.795
0.030
0.621 + 0.048
0.863 = 0.128
28

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Table 9: Downstream results with frozen backbone vs. full finetuning (BACC).
TUAB
TUEV
AD65
Freeze
0.800 + 0.004
0.432 0.024
0.722 0.039
LaBraM
Full Finetune
0.816 + 0.006
0.588 + 0.017
0.711 + 0.060
Freeze
0.774 + 0.001
0.345 0.014
0.620 0.057
CBraMod
Full Finctune
0.808 + 0.007
0.525 + 0.021
0.681 = 0.040
Freeze
0,800 = 0.002
0,460
0.021
0.774 = 0.048
BrainOmni, tiny
Full Finctune
0.819 = 0.004
0.603 = 0.024
0.795 = 0.030
Freeze
0.809 = 0.003
0.480 + 0.015
0.771 = 0.062 0.828 = 0.030
BrainOmni base
Full Finetune
0.819 = 0.005
0.622 + 0.028
information between channels while retaining core semantic features. It also illustrates the benefits of projecting all data from the electrode level into unified feature space for modelling.
F.2 Freeze Backbone for Downstream
In Table P we report the BACC metrics of BrainOmni and the baseline models under both full fine-tuning and frozen settings on TUAB, TUEV, and AD65 datasets. TUAB and AD65, the performance of BrainOmni under the weight freezing scenario is comparable or even higher than the metrics of the baseline models with full fine-tuning, This indicates that BrainOmni has learned the pattern features of EMEG signals with better generalisation capability through unsupervised training.
G Attention Weights of Cross Attention in the BrainTokentn
Recall the content in Appendix B.2 the cross-attention mechanism in BrainTokenizer is inspired by the inverse operator in the Backward Solution. The attention weights actually be regarded as a parameterised linear inverse matrix, representing the degree of influence of each channel on each latent source activity. From Fig. 6 can that each source variable in model has automatically learned a hierarchical structure. It is evident that each source variable focuses on extracting features from specific scalp regions. Moreover, in the multi-hean attention mechanism, the attention of the first head is relatively concentrated, while the subsequent heads progressively expand the range of regions.
H Pretraining Dataset Description
Below, we provide detailed deseriptions of the datasets used for pretraining Tokeniee and BrainOmni.
MEG-MASC The MEG-MASC dataset includes MEG recordings (208 channels, 1000 Hz) from 27 English speakers listening to 2 hours of naturalistic stories, collected using an axial-gradiometer KIT system.
The MEG-Narrative Wartatt includes MEG recordings (275 channels, 1200 Hz) from 3 English speakers listening to 10 hours of naturalistic stories, collected using an axial gradiometer CTF system.
MEG-Naarrative-aataseel
The OMEGA dataset includes MEG recordings (306 channels, 1000 Hz) from 444 healthy subjects and 200 patient volunteers with Parkinson's disease, ADHD, and chronic pain in a resting state, collected using CTF whole-head MEG systems from VSM MedTech Inc.
OMEGA [55
CC700 75) The CC700 dataset is a subset of the Cam-CAN dataset, including MEG recordings (306 channels, 1000 Hz) from nearly 700 subjects performing various tasks and in a resting state, collected using an Elekta-Neuromag system.
Go-Nogo 16: The Go-Nogo dataset includes EEG recordings (32 channels, 1000 Hz) from subjects performing an animal categorization task and a recognition task, collected using a Neuroscan 5083 system.
29

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2
Figure 6: Visualisation of the topographic maps of cross-attentics for each channel in Brain Tokenizer. The model's queries are displayed columns on the top and bottom, with the first eight and last eight queries shown separately. The 4 rows represent the attention weights of each head in the multi-head cross-attention mechanism.
The MusicEEG dataset includes EEG recordings (19 channels, 1000 Hz) from 31 subjects listening to 40 music clips of 12 duration each, targeting range of emotional states, collected using BrainProducts BrainAmp system.
MusicEEG 13]
HFODI:: The HFO dataset includes sleep EEG recordings (23 channels, 1024 Hz) from children and adolescents with focal or generalized epilepsy, collected using Micromed EEG system.
AversiveMEG [2) The AversiveMEG dataset includes MEG recordings (275 channels, 1200 Hz) from 28 subjects completing an aversive learning task, collected using a CTF Omega system.
22 The SRM dataset includes EEG recordings (64 channels, 1024 Hz) from 111 subjects in resting state, collected using BioSemi ActiveTwo system.
MIND(8] The MIND dataset includes MEG recordings (306 channels, 1250 Hz) from subjects who received nerve electrical stimuli, collected using an Elekta-Neuromag system.
RestCog|80) The RestCog dataset includes EEG recordings (64 channels, 500 Hz) from 60 subjects during experimental sessions together with sleep, emotion, mental health, and mind-wandering related measures, collected using Brain Products GmbH system.
30

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SMN4Lang 79:: The SMN4Lang dataset includes MEG recordings (318 channels. 1000
Hz) from 12 subjects listening hours of naturalistic stories, collected using an Elekta- Neuromag system.
HBN Е0/EC[4]] The HBN EO/EC dataset includes EEG recordings (129 channels, 500 Hz) from 2952 children in resting state with eyes-open for 20 seconds and eyes-closed for 40 seconds, collected using an EGI system.
THINGS-MEGI 33): The THINGS-MESS dataset includes MEG recordings (275 channels, 1200 Hz) of 4 subjects who were shown 22448 unique images of 1854 objects, collected using CTF 275 MEG system.
ASWR-MEG[[]] The ASWR-MEG dataset includes MEG recordings (160 channels, 1000 Hz) from 24 subjects listening to random word sequences and judging whether probe word is related to the preceding word, collected using an Flekta-Neuromag system.
ImageLine [6]] The ImageLine dataset includes MEG recordings (306 channels, 1000 Hz) from 30 subjects watching images of objects depicted as photographs, line drawings, or sketch-like drawings, collected using an Flekta-Neuromag system.
Features-EEG 23 The Features-EEG dataset includes EEG recordings (128 channels, 1000 Hz) from 16 subjects watching 256 oriented gratings that varied on four feature dimensions, collected using a Brain Vision ActiChamp EEG system.
PEARL-Neuro [1]: The PEARL-Neuro dataset includes EEG recordings (128 channels, 1000 Hz) from 79 subjects during resting state (eyes opened and closed) and cognitive tasks, including the multi- mouti-source interference task and Sternberg' memory task, collected using Brain Products GmbH system.
The NouroMorph dataset includes MEG recordings (208 channels, 1000 Hz) from 24 subjects doing a lexical decision task and a localizing task, collected using a KIT/Yokogawa MEG system.
Nouro.Morph 57:
Kymata-SOTT 87]: The Kymata-SOTO dataset includes MEG recordings (306 channels, 1000 Hz) and EEG recordings (64 channels, 1000 Hz) from 20 subjects listening English conversations and from 1.5 subjects listening to Russian conversations, collected using an Nlerta-Neuromag system.
The HBN-EEG dataset includes EEG recordings (128 channels, 500 Hz) from 1897 subjects who did 6 tasks including resting state, surround suppression, movie watching. contrast change detection, sequence learning, and symbol search, collected using a Magstim-FGi system.
HBN-EEG(00 61 62 63 64
65
66
67
Awakening The Awakening dataset includes EEG recordings (65 channels, 5000 Hz) from 21 subjects during resting state and propofol sedation, aiming to investigate the effects of propofol on dreaming, collected using a Brain Products GmbH system.
I Downstream Finetuning Dataset Description
Below, we provide detailed descriptions of the downstream datasets used for finetuning BrainOmni.
Gloups-MEG [6] The Gloups-MES MEG dataset includes MEG recordings (248 channels, 2034.5 Hz) of 17 subjects completing a learning task and resting-state condition, collected using an Elekta-Neuromag system.
Percoive The Perceivelmagine dataset includes EEG recordings (64 channels, 1000 Hz) of 52 subjects watching image for seconds, and then imagining the image they see for seconds, collected using a Neuroscan Synamps2 system.
The TUAB dataset includes BEG recordings (21 channels, 256 Hz) from 2328 patients, annotated as normal or abnormal. A total of 408853 10-second samples were used for classification predict these labels.
TUAB 47
The TUEV dataset includes EEG recordings (21 channels, 256 Hz) from 294 subjects, which are segmented into 112277 5-second samples across 6 classes: (1) spike and sharp wave (SPSW), (2) generalized periodic epileptiform discharges (GPED), (3) periodic lateralized epileptiform discharges (PIED), (4) eye movement (EYEM), (5) artifact (ARTF), and (6) background (BCKG). We perform classification to predict these event labels.
TUEV[47:
31

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The MDD dataset includes EEG recordings (20 channels, 256 Hz) from 35 patients with major depressive disorder and 30 normal controls across three sessions: eyes open, eyes closed, and task. A total of 7302 10-second samples were used for classification to predict the presence of major depressive disorder.
MDD[4A]
The WBCIC_SH dataset includes EEG recordings (58 channels, 1000 Hz) from 51 subjects performing motor imagery tasks: left-hand grasping, right-hand grasping. A total of 30591 4-second samples were used for classification to predict the type of task.
SACCICSHUESS
PhysioNet-MI 58 The PhysioNet-MI dataset includes EEG recordings (64 channels, 160 Hz) from 109 subjects performing motor imagery and movement tasks. A total of 9837 4-second samples
used for classification to predict the type of task.
FACED 9 The FACED dataset includes EEG recordings (30 channels, 1000 or 250 Hz) from 123 subjects watching 28 emotion-elicitation video clips covering 9 emotion categories. The coarse categories (negative, positive and neutral) utilised. total of 10332 10-second samples were used for classification to predict the emotion categories.
The PD31 dataset includes EEG recordings (32 channels. 512 Hz) from 16 healthy subjects and 15 subjects with Parkinson's disease during resting state. A total of 882 10-second samples were used for classification to predict the presence of Parkinson's disease.
PD31 [5]]
ASD74 The ASD74 dataset includes MEG recordings (306 channels, 1000 Hz) from 35 children with autism spectrum disorders (ASD) and 39 typically developing children, who watched videos while receiving auditory stimuli. A total of 12320 10-second samples were used for classification to predict the presence of autism spectrum disorders.
The SomatoMotor dataset includes both EEG (74 channels, 1004 Hz) and MEG recordings (306 channels, 1004 Hz) from 5 subjects who received nerve electrical stimuli the right wrist. The task was to lift the left index finger as quickly possible after each right median nerve stimulus. A total of 1208 2-second samples were used for classification predict whether their fingers spontaneously.
SomatoMotor 37]:
lifted because of nerve stimulus,
A A33 The AD65 dataset includes EEG recordings (19 channels, 500 Hz) from 36 subjects with Alzheimer's 's disease and 29 healthy subjects during resting state. A total of 5349 10-second samples were used for classification to predict the presence of Alzheimer's disease.
MEG-MMI 40] The MEG-MMI dataset includes MEG recordings (269 magnetometer channels, 1200 Hz) from 29 adolescents with major depression and 22 healthy subjects during mood induction tasks. A total of 1770 30-second samples were used for classification to predict the presence of major depression.
J Baseline Models Description
We compare BrainOmni with both pretrained and aenpreerrinned baseline models on various down- stream tasks. The basic information of the baseline models is as follows:
CNN-Transformer 522
CNN-Transformer is non-pretrained neural network that bines CNNs and transformer blocks to model long-range dependencies in CNN-derived features.
Contra WR 85 ContraWR a non-pretrained neural network that consists of cascaded pipeline beginning with a short-time Fourier transform (STFT), followed by a 2D CNN layer and three subsequent 2D convolutional blocks.
SPaRCNet 35): SPaRCNet is non-pretrained ID CNN based neural network with dense residual connections.
ST-TTrasffrmer/ (70): ST-Transformer is a non-pretrained transformer-basee model that leverages the attention mechanism to better utilize both spatial and temporal features in EEG data.
32

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FAMED :1I: FAMED is non pretrained model for MEG- epilepsy analysis, built on SCSE-ResNet with dilated convolutions.
LaBraM LaBraM is foundational EEG model pretrained on diverse datasets. employs vector-quantied neural tokenization and masked channel prediction to effectively learn robust EEG features.
CBraMod 78 CBraMod a foundational EEG model pretrained on diverse datasets. employs Criss-Cross Transformer architecture to separately model spatial and temporal dependencies, thereby effectively learning robust EEG features.
K Hyperparameters for model
This section presents the model parameters of Brain'ookenizer and BrainOmni, as well the training hyperparameters during pre-training and downstream fine-tuning.
Table 10: Hyperparameters for BrainTokenizer training
Hyperparameters
Values
Window length
512
N filters
32
Ratios
[8, 4, 2]
Kernel size
5
Last Kernel size
Hidden dim
256
Codebook dim
256
Brain Tokeniser
Codebook size
512
Num quantizers
4
Rotation trick
True
Latent source number
16
Attention head number
Dropout
0.0
Total batch per update Weight decay
512
le-2
Lr Epochs
2c-4
Type
Adam W
Optimizer
Betas
[0.5, 0.9]
Eps
le-5
Type
WarmupCosineLR
Scheduler
Warmup ratio
0.1
Cos min ratio
0.05
33

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Table 11: Hyperparameters for BrainOmni training
Hyperparameters
BrainOmni-base BrainOmniitiny
Hidden dim
256
512
Attention head number
8
16
Attention depth
12
12
BrainOmni
Lr
5c-4
4e-4
Overlap ratio
0.25
Dropout
0.1
Mask ratio
0.5
Total batch per update
256
Epochs
32
Weight decay
5e-2
Type
Adam [0.9, 0.95]
Optimizer
Betas
Eps
1e-6
Type
WarmupCosineLR
Scheduler
Warmup ratio
0.1
Cos min ratio
0.1
Table 12: Hyperparameters for downstream finetuning
Hyperparameters
Values
Total batch per update
128
Epochs Weight decay
30
5e-2
Label smoothing
0.1
Optimizer
Type
AdamW W
Betas
[0.9, 0.99]
Eps
le-6
Type Warmup ratio
WarnupCosineLR
Scheduler
0.1
Cos min ratio
0.1
34


Reviews:
[{"review_id": "eJ0SPFIePK", "reviewer": "Reviewer_73i4", "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.", "questions": "- What are the computational costs for training and inference? Is BrainOmni feasible for clinical use or only research labs?\n- How does the model generalize across subjects with different brain anatomy or signal quality?\n- More in-depth ablation study would be helpful to understand the benefits of each component.", "limitations": "None", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n- BrainOmni is a significant step toward a general-purpose cognitive decoding model by leveraging both EEG and MEG data.\n- The model is used using 1997 hours EEG and 656 hours MEG data from public sources, the largest I've seen so far.  \n\nWeaknesses\n- No clear discussion of training efficiency, hardware requirements, or inference speed, which is crucial for scaling.\n- 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.", "was_revised": true, "final_justification": "It's nice to see a model that explicitly supports multiple modalities of brain signals -- EEG and MEG. It puts together and utilizes 1997 hours EEG and 656 hours MEG from public data sources is commendable. The authors are strongly encouraged to release the final dataset and pre-training model along with the weights.", "rebuttal": "Thank you for the comments and for acknowledging that BrainOmni is a step towards a general-purpose model. We would like to first clarify several important misunderstandings presented in the summary:\n- BrainOmni is designed for **EEG and MEG** signals, not \"functional MRI\", as stated in the title. \n- All datasets used in our study are listed in Appendices D and E, without including \"NSD, HCP, or Movie10K fMRI datasets\". \n- As shown in Table 1, our downstream tasks include clinical (e.g., Alzheimer’s, Parkinson’s), abnormal (e.g., TUAB), event (e.g., TUEV), emotion (FACED), and motor imagery tasks. None of them fall under the category of \"cognitive decoding\", and no \"visual-language fMRI tasks\" were involved.\n\nWe then respond to each comments below:\n\n> **Weakness1:** No clear discussion of training efficiency, hardware requirements, or inference speed, which is crucial for scaling.\n\nThank you for pointing this out. Our pre-training was performed on 2\\*8 NVIDIA A100 GPUs. Stage 1 (BrainTokenizer) took approximately 11 hours. Stage 2 required about 14 hours for the tiny model and 18 hours for the base model. Time cost for downstream fine‑tuning depends on dataset size. On a single A100, it took roughly 60 samples/sec for training and 90 samples/sec for inference. We will add this information to our manuscript.\n\n>  **Weakness2:** Attention map visualisations are provided, but no in-depth explanation of specific learned cognitive representations or their neuroscientific implications.\n\nWe discussed the neuroscience background and how BrainTokenizer parallels the classical source estimation process in Appendix B. Specifically, the cross‑attention between raw EMEG signals and the sensor layout implements the backwards (sensor -> source) and forward (source -> sensor) mappings, so that each latent \"source\" functionally mirrors a cortical current source. We presented the topographic attention map in Appendix J. It shows that each source variable in our model has automatically learned a hierarchical structure, indicating that each source variable focuses on extracting features from specific scalp regions. This supports our hypothesis that BrainOmni learns meaningful, brain‑relevant representations rather than generic signal features.\n\n> **Weakness3:** While diverse in modality, most benchmarks are still based on visual-language fMRI tasks.\n\nAs stated in the clarification above, this study focuses on EEG and MEG signals and does not involve visual-language fMRI tasks.\n\n> **Weakness4:** It’s unclear how well BrainOmni generalizes across individuals, or whether subject-specific adaptation is needed.\n\nWe would like to highlight that all downstream evaluations are conducted in a **strict cross‑subject** setup, with no overlap of individuals between train, validation, and test splits. BrainOmni shows superior performance under this setting (Table 2), demonstrating its robust generalisation to unseen subjects.\n\n> **Question1:** What are the computational costs for training and inference? Is BrainOmni feasible for clinical use or only research labs?\n\nPlease refer to the point 1 for the computational device and time costs. As for applicability, it's worth noting that BrainOmni-tiny has only ~10 million parameters, which is lightweight and suitable for clinical settings as well.\n\n> **Question2:** How does the model generalize across subjects with different brain anatomy or signal quality?\n\nFirstly, as stated in Section 2.2, we designed a Sensor Encoder which encodes the physical coordinates of each channel, enabling BrainOmni to cope with different device layouts across datasets and subjects. Secondly, we pretrained BrainOmni on over 2600 hours of EMEG data from diverse sources, improving generalisation across differing anatomies and noise levels. Preprocessing steps (bad channel detection, channel re‑referencing, normalisation, _etc._) also help to improve the robustness regarding signal quality.\n\nAs stated in response to weakness 4, BrainOmni shows superior cross‑subject generalisation performance to unseen subjects. As discussed in Section 4.2, BrainOmni also shows robust cross-device generalisation to unseen devices.\n\n> **Question3:** Can you further interpret the learned representations or link them to known cognitive networks?\n\nPlease refer to the response to Weakness 2 for a detailed explanation for the neuroscientific implications of learned representations.\n\n> **Question4:** Can BrainOmni be extended to handle EEG, MEG, or structural MRI for truly multi-modal brain modelling?\n\nAs stated in the clarification, BrainOmni is designed as a foundation model for unified EEG and MEG signals. Table 5 demonstrates the effectiveness of this unified pretraining: joint E/MEG pretraining consistently outperforms single-modality pretraining across all datasets. This serves as a crucial first step toward broader multi-modal modelling,  which is inherently challenging due to the structural and functional differences across neural signal types. We look forward to extending BrainOmni to include other neural modalities, as discussed in Appendix A.\n\nWe thank you again for the comments and we hope our rebuttal fully resolves your concerns."}, {"review_id": "1KdYSekDTS", "reviewer": "Reviewer_5FEv", "summary": "The 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.", "questions": "1. 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?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "The submission presents convincing evidence that the proposed brain foundation model competes with existing models and that joint training with EEG and MEG yields improved performance. My comments have been appropriately addressed during the rebuttal discussion.", "rebuttal": "Thank you for your positive comments and thoughtful feedback. We response to your comments below:\n\n### Regarding the sequence length in channel compression step\n\nThe sequence length fed into the cross-attention layer is **only C** (the number of channels). The channel compression module does a cross-attention operation to project heterogeneous inputs into a unified latent source variable. The learnable query is shared across all windows, ensuring that channel compression remains consistent between different temporal windows. Local temporal feature is captured by the SEANet Encoder, and long-range spatiotemporal dependencies are subsequently modelled in Stage 2 by the Criss‑Cross Transformer.\n\n### Regarding the 3-fold CV strategy\n\nSince most datasets lack standard train/test splits and some compared models report results using different or undisclosed splits, we chose to use the 3-fold cross-validation to ensure a fair comparison while also taking computational cost into account. Specifically, for datasets without an official split (_i.e.,_ datasets except TUAB and TUEV), we first split all subjects into three equally sized subsets A, B and C. Then three experiments are performed in which (train, validation, test) are assigned in turn to (A, B, C), (B, C, A) and (C, A, B). For TUAB and TUEV, which each provide an official train/eval split, we keep the eval set for all testing, split the official training subjects equally into three folds, and in each of the three runs use two folds for training and the remaining fold for validation.\n\n### Regarding the finetuning strategy\n\nIn downstream fine-tuning, the pretrained BrainOmni produce embedding in shape `[B, Q, W, D]` (B - batch size, Q - number of compressed channels, W - temporal window length, and D - embedding dimension). The embedding is firstly average-pooled over W, and then flattened to `[B, Q*D]`. The classification head is a two-layer MLP. The BrainTokenizer has 5M parameters.\n\nAs for the number of training epochs, we would like to clarify that rather than using the 30‑epoch checkpoint for testing, we evaluated model performance on the held‑out validation set at the end of each epoch and used the checkpoint that achieved the highest balanced accuracy on the validation set for final test evaluation. This strategy prevents smaller models from overfitting due to excessive training epochs.\n\n\n### Regarding the effect of joint training\n\nFirstly, we would like to highlight that a key goal of BrainOmni is to scale across EEG and MEG modalities, especially given MEG's complexity and limited data availability. Although joint training on both EEG and MEG data naturally increases the total amount of pretraining data, it is not guaranteed to help. In fact, unifying these two modalities in a single framework is non-trivial. Due to their distinct signal characteristics, spatial-temporal resolutions, and data formats, naïvely combining EEG and MEG may even hurt performance, as the model could divert capacity to modality-specific patterns, compromising optimal learning for either modality. Despite these challenges, BrainOmni successfully supports joint training across EEG and MEG: results in Table 5 show that EMEG pretraining improves downstream performance on both modalities. \n\nFor further verification, we ran an ablation experiment in which we subsampled the combined EMEG pretraining set to match the size of the EEG‑only and MEG‑only corpora. The results are listed below:\n\n| Configuration                        | TUEV (EEG)    | asd74 (MEG)   | SomatoMotor (EMEG) |\n| ------------------------------------ | ------------- | ------------- | ------------------ |\n| MEG-only (650 hours)                 | –             | 0.530 ± 0.032 | 0.683 ± 0.168      |\n| EEG-only (2000 hours)                | 0.555 ± 0.003 | -             | 0.737 ± 0.042      |\n| Joint ratio 0.25 (close to MEG only) | 0.571 ± 0.009 | 0.569±0.024 | 0.780±0.172  |\n| Joint ratio 0.75 (close to EEG only) | 0.592 ± 0.003 | 0.614±0.011 | 0.775±0.115 |\n| Joint train                          | 0.597 ± 0.019 | 0.606 ± 0.019 | 0.787 ± 0.112      |\n\nResults above show that (i) given roughly the same amount of pretraining data, joint training using EEG and MEG helps improve the performance (comparing rows 1&2 to rows 3-5); (ii) an increase in the total amount of pretraining data does not necessarily yield improved performance (rows 3-5).\n\n\n### Regarding the preprocessing\n\nThe power-spectral-density-based bad-channel detection is implemented as follows:\n```csharp=\nInput: raw_data, threshold=10\n1. Compute per‑channel PSD over the full recording:\n     PSD ← compute_psd(raw_data).data\n2. Stabilise and log‑transform:\n     L ← log(PSD + 1e‑16)\n3. Compute pairwise distances between channel spectra:\n     for i in 1…C, j in 1…C:\n         dist[i,j] ← L[i] – L[j]\n4. Mean distance per channel:\n     m[i] ← mean(j, dist[i,j])\n5. Identify outliers via IQR:\n     Q1 ← 25th_percentile(m), Q3 ← 75th_percentile(m)\n     IQR ← Q3 – Q1\n     upper ← Q3 + threshold·IQR\n     lower ← Q1 – threshold·IQR\n     bad_channels ← { ch_i | m[i] > upper or m[i] < lower }\n```\n\nThe reference inconsistency is caused by the fact that different data recording setups may use different physical reference electrodes. To address this, we first compute a global average reference by taking the mean across all channels, and then subtract this channel-average signal from each channel waveform, effectively aligning all channels to the same virtual reference. This protocol is applied regardless of whether a reference channel is unavailable or the signals have already been referenced, and it is performed both at the recording level and separately for each signal type.\n\nThank you again for your positive comments and constructive suggestions. We will further clarify the above points in our manuscript. We hope our response resolves your concerns."}, {"review_id": "gAi7Cf0hej", "reviewer": "Reviewer_ThgG", "summary": "This 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.", "questions": "Is 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?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "One 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.", "was_revised": true, "final_justification": "This is a solid paper.", "rebuttal": "Thank you for your insightful feedback and for acknowledging our research direction and experimental validation. We response to your comments below:\n\n### Regarding the performance compared to specialised models\n\nWe appreciate you for raising this important point, and agree on the importance of explicitly addressing these points. Paper \\[1\\] designs channel combinations and feature extraction methods based on the distribution of features across frequency bands and brain regions, demonstrating the outstanding performance of machine learning approaches in this task. That said, we would like to highlight a key difference in experimental setup and data splitting strategy, which makes direct comparison of results between BrainOmni and the cited classical methods inappropriate for our context.\n\nThe referenced baseline (\\[1\\]) employs a **random split** approach on the PD31 dataset, which may result in the same subjects appearing in both training and testing sets, thereby compromising subject independence. Instead, our experiments utilise a **cross-subject split**, ensuring no subject-level information leakage, which is a more challenging and practically relevant evaluation setting. Indeed, paper \\[1\\] explicitly acknowledges concerns regarding data leakage associated with random splits in the \"UNM‑based results\" section.\n\nSince the implementation of [1] is not open-sourced and the specific data split used is not disclosed, a direct comparison under a unified setup is not feasible. To estimate the impact of the split strategy, we conducted an additional experiment using a random split on PD31, following a similar protocol. This resulted in a substantially higher accuracy of 92% for BrainOmni, highlighting that the choice of data split can significantly affect performance.\n\nWe fully agree with your suggestion that it is important to provide a balanced discussion of BrainOmni’s strengths and limitations in relation to specialised baselines. This also reflects a broader challenge in current AI research: general-purpose models often trade off some task-specific performance in exchange for scalability, flexibility, and cross-domain transferability. \n\nBrainOmni is designed to serve as a unified foundation model across heterogeneous non-invasive neurophysiological electrophysiological modalities (specifically EEG and MEG). It supports cross‑device and cross‑subject generalisation, making it suitable for practical applications where domain shifts are inevitable. While it may not always outperform domain-specific baselines tailored to one dataset or one task, it offers broader coverage and generalisability across datasets, tasks, and modalities — a key advantage for scalable and reusable neuro-AI systems. We will add this discussion to our manuscript.\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\n### Regarding the questions\n\nAnswer to Q1: We visualise the reconstruction results of unseen devices without finetuning. \n\nAnswer to Q2: We didn't separately finetune the BrainTokenizer because it relies on a learned VQ codebook whose discrete embeddings encode meaningful spatiotemporal tokens, and updating it on limited downstream data risks corrupting those codebook semantics. Instead, following standard practice, we freeze the entire Tokeniser (including the VQ quantiser) during downstream adaptation and finetune only the Transformer layers.\n\nThank you again for your positive comments and constructive suggestions. We hope our response resolves your concerns."}, {"review_id": "Cc8EJv4dO8", "reviewer": "Reviewer_VHcX", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "Most of my concerns have been addressed.", "rebuttal": "Thank you for your detailed feedback. We respond to your comments below:\n\n### Regarding downstream MEG datasets\n\nWe further introduce an additional publicly available MEG dataset, MEG-MMI \\[1\\]. The results (in balanced accuracy) are listed below:\n|  | CNNTransformer | ContraWR      | SPaRCNet      | STTransformer | BrainOmni     |\n|--------|----------------|---------------|---------------|---------------|---------------|\n| BACC   | 0.513 ± 0.019  | 0.496 ± 0.005 | 0.616 ± 0.080 | 0.610 ± 0.020 | 0.663 ± 0.052 |\n\nIt can be seen that BrainOmni still outperforms the baselines on the additional MEG dataset.\n\n\\[1\\] Liuzzi, Lucrezia, et al. \"Magnetoencephalographic correlates of mood and reward dynamics in human adolescents.\" _Cerebral cortex_ 32.15 (2022): 3318-3330.\n\n### Regarding MEG baselines\n\nWe would like to first clarify that all of our chosen baselines are not specifically designed for EEG but rather general-purpose architectures for multi-channel time-series data -- spanning CNN+Transformer (CNNTransformer), pure CNN (ContraWR and SPaRCNet), and separately spatial and temporal Transformer (STTransformer). Training them directly on MEG, therefore, provides a fair indication of how those paradigms perform in the MEG domain. We further include an additional model, FAMED \\[2\\], primarily designed for MEG signals. The results (in balanced accuracy) are shown in the following table:\n\n| Dataset  | CNNTransformer  | ContraWR       | SPaRCNet       | STTransformer  | FAMED          | BrainOmni_tiny |\n|----------|-----------------|----------------|----------------|----------------|----------------|----------------|\n| asd74    | 0.508 ± 0.011   | 0.533 ± 0.047  | 0.565 ± 0.039  | 0.532 ± 0.024  | 0.566 ± 0.038  | 0.606 ± 0.019   |\n| MEG‑MMI  | 0.513 ± 0.019   | 0.496 ± 0.005  | 0.616 ± 0.080  | 0.610 ± 0.020  | 0.505 ± 0.010  | 0.663 ± 0.052   |\n\nResults show the superior performance of BrainOmni.\n\n\\[2\\] Hirano, Ryoji, et al. \"Deep learning based automatic detection and dipole estimation of epileptic discharges in MEG: a multi-center study.\" _Scientific Reports_ 14.1 (2024): 24574.\n\n### Regarding cross-subject 3-fold validation setup\n\nIn the downstream part, we evaluated a variety models on a variety of datasets. Since most datasets lack standard train/test splits (only TUAB and TUEV have) and some compared models report results using different or undisclosed splits (_e.g._, AD65 dataset used in \\[3\\]), we built a unified evaluation pipeline across all datasets and models to guarantee fully comparable results. The cross-subject 3-fold validation setup, where every individual appears once in test, can capture inter-subject variability better than a single fixed split, achieving a more comprehensive and robust assessment.\n\nTo verify that our performance is not an artifact of this protocol, we followed CbraMod’s original PhysioNet-MI dataset filtering (only use motor imagery sessions) and splitting (subject (1-70, 71-89, 90-109) for (train, valid, test)) procedure, repeated experiments with 5 random seeds, and report the averaged metrics of BrainOmni. Results are shown in the following table:\n\n| Model | Setting |Balance Accuracy|Cohen's Kappa    |Weighted F1      |\n|-------|----------|----------------|-----------------|-----------------|\n|CBraMod | result in \\[4\\] | 0.642 ± 0.009   |0.522 ± 0.017    |0.643 ± 0.010    |\n|BrainOmni | same split as \\[4\\] | 0.650 ± 0.006   | 0.533 ± 0.007    | 0.651 ± 0.005    |\n|CBraMod | reshuffled split | 0.572 ± 0.008   | 0.430 ± 0.011    | 0.574 ± 0.008    |\n\nComparing row 1 and 2, BrainOmni still outperform competing methods under the same splitting, demonstrating that the gains are not due to differences in data assignment.\n\nFurthermore, we generated an alternative subject split, by reshuffling the subject list (with random seed 0) while maintaining the train/val/test ratio as \\[4\\]. Then we reproduced CBraMod's downstream experiments strictly using their codebase and following their hyperparameters listed in \\[4\\]. The results are listed in row 3 of the above table. \n\nIt can be observed that the baseline performance dropped significantly, demonstrating the sensitivity caused by inter-subject variability, and highlighting the necessity and robustness of our cross-subject 3-fold setup.\n\n\\[3\\] Yuan, Zhizhang, et al. \"Brainwave: A brain signal foundation model for clinical applications.\" _arXiv preprint arXiv:2402.10251_ (2024).\n\n\\[4\\] Wang, Jiquan, et al. \"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding.\" _The Thirteenth International Conference on Learning Representations_ (2025).\n\n\n### Regarding the number of latent source variables\n\nWe added an ablation study on the number of latent source variables $ Q \\in \\{8, 16, 32\\} $, with results listed below: \n\n| Num of latent source variables | TUEV (EEG)    | asd74 (MEG)   | SomatoMotor (EMEG) |\n| ------------------------------------ | ------------- | ------------- | ------------------ |\n| 8 | 0.590 ± 0.017 | 0.558±0.019 | 0.773±0.140 |\n| 16 | 0.597 ± 0.019 | 0.606 ± 0.019 | 0.787 ± 0.112      |\n| 32 | 0.605 ± 0.022 | 0.613±0.036 | 0.776±0.116 |\n\nOverall, Q=8 performs worse than Q=16 and Q=32, while Q=16 and Q=32 show similar performance across three datasets. Combined with the trend in Figure 3\\(c\\), this shows that too few source variables lead to poor reconstruction and limited downstream performance, whereas increasing Q beyond 16 brings diminishing improvements.\n\n### Regarding the channel dropout rate\n\nThe channel dropout serves as a structural regulariser, enabling BrainTokenizer to adapt to varying channel layouts and quantities, as well as equipping it with the capability to predict target position electrodes based on existing ones, which improves robustness to channel corruption or occlusion. The 25% value was chosen based on experience. We did not perform a full hyperparameter search on this value due to computational constraints, but we expect that tuning this parameter could further improve results.\n\n### Regarding the loss functions in BrainTokenizer\n\nThe BrainTokenizer training loss contains four components: time-domain loss, frequency-domain loss, Pearson correlation loss, and RVQ commitment loss. Given that the time-domain and commitment losses are essential for VQ-AE training, we add an ablation study for how frequency loss and PCC loss influence the reconstruction performance, with results listed in the following table. \n\n| time & commitment | freq loss | PCC loss       |MAE $\\downarrow$ |AMP $\\downarrow$|PHASE $\\downarrow$|PCC value $\\uparrow$ |commitment $\\downarrow$|total loss $\\downarrow$|\n|-|-|---------|---|---|-----|---------|----------|----------|\n| &#10004; | |  |0.799|0.256|2.154|0.002|5.01e-7|4.207|\n| &#10004; | &#10004; |  |0.471|0.160|1.583|0.716|1.64e-4|2.517|\n| &#10004; | | &#10004; |0.462|0.167|1.863|0.751|1.83e-4|2.703|\n| &#10004; | &#10004; | &#10004; |0.480|0.165|1.495|0.723|1.48e-4|2.626|\n\nWith only time-domain and commitment loss, the decoder outputs collapsed into near‑constant signals. Introducing the frequency‑domain loss greatly improved spectral detail (lower MAE/AMP/PHASE), but led to spurious high‑frequency impulses. The PCC term mitigates these impulses by aligning overall waveform trends, yet on its own provides weaker spectral reconstruction. Finally, we chose to employ both loss functions simultaneously, suppressing the impulse while preserving as much frequency-domain detail as possible.\n\nWe sincerely appreciate your feedback and hope that our responses adequately address your concerns."}]

Author rebuttal:

Meta-review: This paper presents BrainOmni, a foundation model that unifies EEG and MEG signals through a BrainTokenizer module and SensorEncoder, enabling robust cross-modality and cross-device generalization. The model is pretrained on an unprecedentedly large corpus of 1997 hours of EEG and 656 hours of MEG data, and evaluated on 10 diverse downstream tasks. Results show that BrainOmni consistently outperforms existing foundation models and supervised baselines, with joint EEG–MEG pretraining yielding further gains.

The paper’s key strengths lie in its scale, novelty, and potential impact. Reviewers highlighted the significance of being the first foundation model to integrate EEG and MEG (73i4, 5FEv, VHcX), the thorough experimental validation across clinical, motor, and cognitive domains (ThgG), and the innovative design of BrainTokenizer, which draws inspiration from classical source estimation methods. The commitment to open-sourcing data and pretrained models further strengthens its value for the community.

The main weaknesses are the lack of detailed discussion on computational efficiency, the limited MEG downstream evaluation, and the absence of comparisons to some domain-specific baselines. Reviewers also requested more ablation studies, which the rebuttal addressed with extensive additional analyses. Clarifications on training/inference costs, ablations on loss components, and new MEG datasets helped resolve most concerns.

Overall, the contributions are timely and technically solid, offering a promising step toward general-purpose brain foundation models. I recommend acceptance.
Decision: Accept (poster)