Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning
Authors: Zhisheng Chen
Yingwei Zhang
Qizhen Lan
Tianyu Liu
Huacan Wang
Yi Ding
Ziyu Jia
Ronghao Chen
Kun Wang
Xinliang Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Brain Computer interface
Foundation Model
Electroencephalography
Primary area: applications to neuroscience & cognitive science

Abstract: Current foundation models for electroencephalography (EEG) rely on architectures adapted from computer vision or natural language processing, typically treating neural signals as pixel grids or token sequences. This approach overlooks that the neural activity is activated by diverse sparse coding across a complex geometric topological cortex. Inspired by biological neural mechanisms, we propose the Unified Neural Topological Foundation Model (Uni-NTFM), an architecture rooted in three core neuroscience principles. In detail, to align with the brain's decoupled coding mechanism, we design the Heterogeneous Feature Projection Module. This module simultaneously encodes both time-domain non-stationary transients and frequency-domain steady-state rhythms, ensuring high quality in both waveform morphology and spectral rhythms. Moreover, we introduce a Topological Embedding mechanism to inject structured spatial priors and align different sensor configurations onto a unified latent functional topography, effectively reconstructing the geometry of brain regions. Furthermore, we achieve functional modularization and sparse coding efficiency of biological networks by constructing the Mixture-of-Experts Transformer network. This dynamic routing mechanism assigns different signal patterns and tasks to specialized neural subnetworks, and effectively preventing task interference while increasing the model capacity to record-breaking 1.9 billion parameters. Uni-NTFM is pre-trained on a diverse corpus comprising 28,000 hours of EEG data, and outperforms existing models across nine distinct downstream tasks under both linear probing and fine-tuning settings, demonstrating that aligning model architecture with neural mechanisms is significant to learn universal representations and achieve generalizable brain decoding.} Our code is available at \url{https://anonymous.4open.science/r/Uni-NTFM-0924}

Published as a conference paper at ICLR 2026
UNI-NTFM: A UNIFIED FOUNDATION MODEL FOR EEG SIGNAL REPRESENTATION LEARNING
Zhisheng Chen Yingwei Zhang Qizhen Lan', Tianyu Liu Huacan Wang', Yi Ding', Ziyu Jia Ronghao Chen Kun Wang' & Xinliang Zhou
Beijing Key Laboratory for Multimodal Collaboration and Advanced Application, Institute of Computing Technology, Chinese Academy of Sciences ?University the Chinese Academy of Sciences "Nnyyang Technological University *Institute of Automation, Chinese Academy of Sciences University of Alabama at Birmingham 'Peking University emaihis
chenzhisheng2 @mails ucas, ac. en, zhangyingwei ac. cn, chenronghao@al umnni. pku. edu. en, xinliang001@e nt.. edu. ag.
ABSTRACT
Current foundation models for eleetreencephalograpyy (EEG) rely on architec- tures adapted from computer vision or natural language processing, typically treating neural signals as pixel grids or token sequences. This approach over- looks that the neural activity is activated by diverse sparse coding across com- plex geometric topological cortex. Inspired by biological neural mechanisms, we propose the Unified Neural Topological Foundation Model (Uni-NTFM), an ar- chitecture rooted in three core neuroscience principles. In detail, to align with the brain's 's decoupled coding mechanism, we design the Heterogeneous Feature Projection Module. This module simultancously encodes both time-domain non- stationary transients and frequency-domain steady-state rhythms, ensuring high quality in both waveform morphology and spectral rhythms. Moreover, we in- troduce a Topological Embedding mechanism to inject structured spatial priors and align different sensor configurations onto unified latent functional topog- raphy, effectively reconstructing the geometry of brain regions. Furthermore, we achieve functional modularization and sparse coding efficiency of biologi- Transformer network. This
cal networks by constructing the Mixture-of- dynamic routing mechanism assigns different signal patterns and tasks to spe- cialized neural subnetworks, and effectively preventing task interference while increasing the model capacity to record breakdbb 1.9 billion parameters. Uni- NTFM is pre-trained on a diverse corpus comprising 28,000 hours of EEG data, and outperforms existing models across nine distinct downstream tasks under both linear probing and fine-tuning settings, demonstrating that aligning model architecture with neural mechanisms is significant to learn universal representa- tions and achieve generalizable brain decoding. Our code is available at ht https: / / a/nnymoouu. 4open. sciesciiin cance/r/Unet -
INTRODUCTION
Electroenceephalograahy (EEG), an effective observational window with high temporal resolution, provides a critical technological means for real-time monitoring of brain activity, has indispensable value in fields such as clinical diagnosis, neuroscience research, and Brain-Compuus (BCIs) (Pfurtscheller & Neuper, 2001; Flesher et al., 2021; Ieracitano et al., 2019). However, with the increasing ability for data acquisition, researchers are confronted with complex and massively scaled EEG data. In this context, traditional task-specific modeling approaches, limited by their generalization capabilities, are no longer sufficient to fully uncover the universal neural encoding principles embedded within (Banville et al., 2021).
Interfaces
Corresponding Authors Project Leader

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Published as a conference paper at ICLR 2026
Uni-NTFM: Unified Neural Topological Foundation Model
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Figure I: The overview of Uni-NTFM's core concepts and superior performance. The left panel summarizes the model's "Unified" principle, spatial topology strategy, and method of performance validation in a Q&& format. The two radar charts on the right demonstrate that Uni-NTFM compre- hensively outperforms baseline models across nine distinct downstream tasks in both linear probing and fine-tuning settings.
Concurrently, the field of artificial intelligence has witnessed the success of the foundation model paradigm, which acquires general- peneral-purpose data representations through large-scale self-supervised learning. And this paradigm has achieved breakthrough progress in natural language processing and computer vision (Devlin al., 2019; Radford al., 2021; Kirillov et al.. 2023; Achiam et al., 2023). Inspired by this, the researchers are actively exploring the migration of this paradigm to the EEG do- main (Jiang al., 2024a; Wang et al., 2024b). The goal is to fully unlock the potential of large-scale EEG data through superviee learning, thereby discovering universal neural representations and fundamentally elevating our understanding of the brain's complex dynamics (Zhou et al., 2025a). The work related to brain foundation models (BFMs) is described in detail in Appendix A. However, current BFMs largely inherit the general- purpose paradigm designed for language or vision: seg- menting continuous signals into discrete, fixed-scale units (patches or tokens) and then employing a dense attention network for global information interaction. While this approach formally unifies the processing pipeline by directly transferring model paradigm based on symbols and pixels to the physiological signal domain, its basic design philosophy contradicts the fundamental physical properties of EEG signals, thereby imposing limitations on the model's representation ability. We argue that this mismatch of existing architectures and neural activities creates three critical barriers to learning universal brain representations:
1) Inability to Capture Decoupled Neural Coding: Standard architectures process information as single, homogeneous stream, which contradicts the brain's 's decoupled coding mechanism. Forcing time-domain and frequency-domal modalities into unified processing channel conflates waveform morphology with spectral structure.
2) Failure to Reconstruct Unified Functional Topography: Existing models typically treat EEG
electrodes as invariant sequences, ignoring that they are discrete spatial samplers of a continuous, complex geometric topological cortex. This neglect of the functional topography prevents models from aligning different montage configurations into a consistent semantic space.
3) Lack of Functional Modularity and Sparse Efficiency: Biological networks achieve special- ized corresponding for specific stimuli through functional modularization and sparse coding. In contrast, standard dense Transformers activate all parameters for each input, which is prone to task interference when processing the highly heterogeneous patterns of EEG signals.
In summary, existing models fundamentally lack analysis of the intrinsic structure of EEG sig- nals. Therefore, constructing a new architecture capable of synergistically modeling the brain's decoupled coding mechanisms, geometric functional topology, and sparse modularity has become
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an imperative for the advancement of the field. As Figure 1, to address these challenges, propose the Unified Neural Topological Foundation Model (Uni-NTFM), a foundation model designed for generalized EEG decoding that is deeply aligned with the brain's ' principles. We design a multi- domain and structure-aware information processing framework that computationally emulates three fundamental neural processes: 1) dual-stream neural perception, 2) spatial topology construction, 3) modular cognitive specialization. Our core contributions are as follows:
We emulate the brain's parallel processing architecture by designing a Heeierogeneous Feature Projection Module (HFPM). This module physically decouples neural information into complementary streams, which aims cap- ture non-stationary transients by the time path and steady-state rhythms by the frequency path. Fur- thermore, a Dual-domain Cross-attention Module (DCM) is established facilitate a cooperative interaction between these representations, generating a deeply fused representation through bidirec- tional information enhancement.
1) GioooueellyyGrroundeed Transient-Sustained Dual Coding:
2) Montage Alignment via Topological Embedding: To reconstruct the brain's geometry from different sensors, we introduce a hierarchical Topological Embedding (TE) scheme. By encoding spatial semantics at the intra-region level, region level, and absolute sequence level, this mechanism injects a precise neural coordinate system into the latent space and aligns heterogeneous electrode montages onto unified functional topography.
3) Functional Modularity with Sparse MoE: We simulate the functional specialization and task decoupling of biological networks by replacing the dense FFN with Soxtureeof-Expertt (MoE) architecture. This design enables dynamic functional routing, where specific signal patterns are processed by specialized expert subnetworks to relieve interferences between different tasks.
4) Multi-view Self-Supervised Learning:
We propose pre-training objective that forces the model master the generative rules of neural activity. By simultaneously reconstructing the masked time- domain waveform patterns, frequency-domain spectral rhythms, and MoE auxiliary loss, this multi- view paradigm ensures that the model learns generalizable and robust representations of brain dy- namics, rather than merely superficial features.
We pre- pre-trained Uni-NTFM on massive corpus containing 28,000 hours of EEG recordings using our specifically designed dual-domain reconstruction self-supervised objective. Extensive experi- ments on downstream tasks robustly demonstrate the superiority of our new paradigm. Under linear probing setting without any fine-tuning, the model already exhibits powerful general-purpose representation capabilities. After fine-tuned, Uni-NTFM's performance on public datasets across more than nine different BCI tasks not only comprehensively surpasses existing task-specific mod- els but also significantly outperforms other mainstream foundation models.
2 METHODOLOGY
This section explains the paradigm of Uni-NTFM, self-supervised foundation model proposed to address the challenges of large-scale EEG representation learning. We believe that it is essential to synergistically analyze three intrinsic properties of EEG signals: waveform morphology, spec- tral rhythms, and spatial topology. Furthermore, to efficiently process these complex multi-domain representations, a sparsely-activated MoE pipeline is required. Thus, we design a hierarchical infor- mation processing framework, shown in Figure 2, which simulates the neural processing: from multi-domain perception and cross-domain fusion to high-level cognitive specialization.
2.1 INPUT DATA PREPROCESSING
The raw EEG data is processd into tensor shapes of X BXRXXXXT where B is the batch size, R and E are the number of predefined brain regions and the maximum number of electrodes per region, respectively, and is the length of the time series. To enhance the model's robustness to common variations in real-world signals, we also simulate noise, channel loss, and temporal drift in signal acquisition through a series of probabilistic data augmentations (fang + Xang) before being entered into the model.
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The end-to-end architecture of UniNNTFM in detail. The data processing flow begins with the data augmentation of raw EEG signals, followed by Heterogeneous Feature Projection Module (HFPM) which parallelly decomposes the input EEG signals into three domain streams: time, frequency, and raw. Next, Dual-domain Cross-attention Module (DCM) performs cross fusion of time domain and frequency domain features, combined with Topological Embedding (TE) to encode the spatial prior information of the electrodes. Finally, the processed representations are sent the core Trans.... Block to learn universal semantic features.
Figure 2:
2.2 DECOUPLING AND SYNERGY OF HETEROGENEOUS FEATURES
Unlike images or text, which have natural discrete units (pixels, words), continuous EEG signals can't be tokenized in this way. A common approach that segments the time series into temporal "patches" ignores the continuous characteristic of the signal and fails to capture features at different temporal scales. To address this, we propose paradigm: instead of segmenting the time axis, we treat the entire time series 2; R (where i is the global electrode index) from each individual electrode as holistic "patch". Our Heterogeneous Feature Projection Module (HFPM) is designed to transform these holistic, channel-wise patches into a set of multi-domain feature embeddings. This module simultancously projects each channel's signals into three distinct fcature domains.
2.2. DYNAMICS WAVEFORM ENCODER (TIME PATH)
To capture the local waveform structures and non-stationary events of the signal, we employ a one- dimensional convolutional encoder, Φ (-), to map into temporal feature vector hiT E D;
h4r = (z,)))
This encoder consists of multiple convolutional blocks, where the operation of each block can be formalized as:
h (1+)) ReLU(BN(W()) * 1 + b()))
2.2.2 FREQUENCY DECOMPOSITION ENCODER (FREQUENCY PATI))
To extract the crucial steady-state rhythmic information from the signal, we first decompose the signal into the frequency domain via the Discrete Fourier Transform compute the Power Spectral Density. Then parameterize it into mean power vector Pb E No for N, core frequency bands. This vector is subsequently projected into frequency feature vector h p R by MLP, ()))))
h (p((p(x)))
(3)
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The mean power Poù for the j-th frequency band is defined as:
Po.
Σ
c:(e)e -2akt/T
(4)
JC
kEK,
where KG is the of discrete frequency indices corresponding the j-th frequency band.
2.2.3 STANDARD PROJECTION ENCODER (RaW PATHI)
While the time and frequency paths perform non-linear transformations that selectively amplify partial features, the raw path establishes a high-quality and information-losiles reference for the self-supervised reconstruction task. This reference representation serves as the "ground truth" target Ha in our reconstruction loss (Section 2.5), ensuring that the model is trained to recover the full signal, not just a specific domain. This design is critical for maintaining the clarity and integrity of the pre-training objective. The explaination for this process is shown in Appendix E.
2.2.4 DUAL-DOMAIN CROSS-ATTENTION MODULE
Decomposing the signal into parallel temporal and frequency representations, the next critical step to achieve a unified understanding by modeling their interdependencies. To this end, we introduce the Dual-domain Pross-attentic Module (DCM). Distinct from standard self-attention where Query, Key, and Value are from the same source, our module forces a cross- cross-domain dialogue: the temporal feature sequence H1 serves the Query probe for relevant Key and Value of frequency features Hr, and vice versa.
H = LayerNorm( Hy + CrossAutn(Q = Hr, K = HF, V Hr))
(5)
H = La f CrossAttn(Q = HF, HT, V Hr))
(6)
The final fused feature Hused FFN(Conca((HH,, H)) contains deep interaction information be- tween the two domains.
2.3 EXPLICIT AND UNIFICATION EMBEDDING OF SPATIAL TOPOLOGY
A critical bottleneck in developing generalist BFM is the montage heterogeneity problem: unlike the fixed pixel grid computer vision, EEG datasets exhibit substantial variability in sensor con- figurations (c.g., clinical 19-channel 10-20 systems and high-density 64-channel 10-10 systems). Standard Transformers, which utilize learnable ID position encodings, treat electrodes as the simple sequence, thereby discarding the geometric structure of the cortical surface. To overcome this, in- troduce a hierarchical Topological Embedding (TE) mechanism that projects diverse sensor layouts onto unified, biologically grounded standardized neural coordinate system, which deconstructs the spatial identity of each electrode into three levels of neuroanatomical semantics.
(1) Region Embedding (Fregion): To capture the functional modularity of the cortex, partition the scalp topology into five canonical brain regions based on the international 10-20 system standards.
We define a learnable embedding matrix Exogion 5xxD corresponding to: (a) Frontal: Encoding
executive functions and decision; (b) Central: Encoding sensorimotor rhythms: (c) Temporal:
Encoding auditory processing and memory; (d) Parietal: Encoding spatial attention and sensory; (e)
Occipital: Encoding visual processing features. By explicitly anchoring signals to these functional
domains, the model learns to generalize features regardless of the specific channel index used particular dataset.
(2) Intra-Region Embedding (Finma): Within each region, electrode density varies across standards. To resolve this, introduce intra-region coordinates that encode the relative spatial orientation. This embedding ensures that the model understands the geometric relationship between neighbors, such C3 and C1 are spatially adjacent in the motor cortex. This mechanism provides the structural basis for robustness against missing channels.
(3) Global Absolute Embedding (Eats): To preserve precise channel-specific identities for stan- dard clinical montages, we assign a unique global identifier to standard electrodes defined in the International Federation of Clinical Neurophysiology (IFCN) guidelines.
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For imput token x; originating from specific electrode, its final spatial representation is the summation of these hierarchical priors:
(1) + H + Eregion (rroglool + Eintra [20 inntral
+
Eabs [arats]
(7)
where Hused denotes the moal-domain features integrated by the DCM module, and H represents the raw signal reference features. The Exegion. Eutaa and Eabs refer the learnable embedding
matrices for the region, intra-region, and global absolute position, respectively. The indices "region" (i) Fintra and aaas correspond to the specific neuroanatomical coordinates of the i-th electrode within the standardized coordinate system.
(1)
This hierarchical design allows Uni-NTFM function geometry-aware encoder. When eval- uating on the incomplete data, the model utilizes the Fregion and Fintra embeddings to correctly map these signals their respective cortical sources in the latent space. This effectively solves the cross-montage transfer challenge without requiring specific re-training.
2.4 FUNCTIONALLY MOE-BASED NEURAL TRANSFORMER
The challenge in scaling the Transformer block that increasing capacity via dense FFN leads to quadratic increase in computational cost. To overcome this, we replace the dense FFN in each Transformer block with sparsely-activated MoE mechanism. This architecture is particularly suit- able for EEG modeling, as it allows the model to learn specialized subnetworks for distinct signal patterns (e.g., specific neural rhythms, artifacts, or pathological events) through its gating mecha- nism. This functional specialization not only enhances modeling accuracy but also offers significant advantages in downstream adaptation, where fine-tuning a small subset relevant experts can lead to highly efficient and robust transfer learning.
Hi = 1 + +MltiieeuSSS//tmnaaaaaooo///// Hi = H MoE(LaycrNorm(4))
(8)
(9)
2.4.1 NEURAL MULTI-HEAD HEAD SELF-ATTENTION
The self-attention mechanism is designed to learn long-range functional connections in the brain. To enable to perceive the relative spatial order of electrodes, we introduce Rotary Position Encoding (RoPE). For d-dimensional vector 2. at position m. its rotated form x' is obtained by grouping the vector into pairs (2m,2i 2m,2i) and applying a rotation matrix Re,,
x 26,21-11
(co((m0,) - -in(mo0.) (Im.26-1) (si((m))) cos(m0;) Tim,2i
(10)
m.2i
where 0; = 10000 2//d A property RoPE is that the attention score intrinsically depends only on the relative position m
(o' k.) (R Ro,nkn) = (ym, Ro,n-mka))
(1I)
2.4.2 SPARSELY-ACTIVATEDD ACTIVATED MoE MODULE
MoE allows the model to learn function-specifi subnetworks. A gating network g(hx) W, computes logits for each input token h; across Ne expert networks. Through Top-k gating, the final output for token is the weighted sum of the outputs from the k expert networks E-(.):
MoE(h;)
=
Σ
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(12)
e T'Tppk (Softmax( (N))))
To encourage load balancing, we introduce an auxiliary loss Lax:
N.
Laux = a N.
f Dj
(13)
where f, is the fraction of tokens routed to expert j, and pú is the average probability assigned to expert j for those tokens.

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2.5 DUAL-DOMAIN SELP-SUPERVISED RECONSTRUCTION OBJECTIVE
The entire learning process is driven by the masked autoencoding self-supervised task (He et al,, 2022). For input sequence Hi. we randomly select an index subset M and replace its tokens with a shared, learnable embedding E[MASK]. The model's optimization objective is to reconstruct the original features of the masked tokens. To this end, we designed Dual-Domain Loss Function, Lunal, which forces the model to maintain consistency in both the time and frequency domains simultaneously:
Lixaal = AT + Xp + Caxx
(14)
The temporal reconstruction loss Lime and frequency reconstruction loss Lrog are calculated as the Mean Squared Error over the masked positions M:
Crime
[Head (Hom,.) - Ri,
(15)
FM
Lireq
|Heady (Head((Hom,.)) - 14. 112
(16)
J
IEM
where Hou is the final output of the Transformer. This learning paradigm enables Uni-NTFM to learn the profound internal structures and regularities of EEG signals without explicit labels.
EXPERIMENTS
3.1 PRE-TRAINING SETTINGS
1) Data Summary. We aggregated large-scale pre-training corpus by amalgamating nine distinct, publicly available EEG datasets to train a foundation model capable of learning truly generaliz- able neural representations. As detailed in Appendix Table 6, this corpus comprises data from over 17,000 subjects and amounts to approximately 28,000 hours of recordings. It includes record- ings from resting-state conditions (e.g., REEG-BACA (Getzmann et al., 2024), Resting State EEG (Trjjillo et al., 2017)), emotion induction tasks (c.g., Emobrain (Savran et al., 2006). SEED-series (Zheng al., 2018; Liu al., 2021; 2022)). cognitive classification tasks (c.g.. Raw EEG Data (Tru- jillo, 2020)), BCI paradigms (BCI Competition IV-1 (Blankertz et al., 2007)), )), and extensive clinical recordings from hospital environments (e.g., TUEG (Obeid & Picone, 20,6), CAUEEG (Kim al., 2023), Siena Scalp EEG Database (Detti et al., 2020)).
2) Data Preprocessing, Initially, a zero-phase filter was applied with passband of 0.5-50 Hz, and notch filter suppressed powerline interference at 50 Hz and its harmonics. To ensure uniform temporal resolution, all signals were then downsampled to a consistent 200 Hz Hz sampling rate. Before normalization, all channel amplitudes were uniformly scaled millivolts (mV).
3) Training Settings. We designed four versions of Uni-NTFM at different scales, with 57M, 427M, 912M, and 1.9B parameters, respectively. All experiments were conducted on NVIDIA A A100-80G GPUs, using Python 3.9.23 and Py Torch 2.3.1 with CUDA 11.8. The specific configurations for each model and other detailed hyperparameter settings provided in Appendix Table 8.
3.2 DOWNSTREAM DATASETS
1) Data Summary. To comprehensively evaluate the performance of Uni-NTFM, we collected nine
public EEG datasets across various paradigms and tasks, as detailed Appendix Table 7: 1) TUAB
(Harati et al., 2015), 5), 2) TUEV (Harati et al., 2015), 3) SEED (Zheng & Lu, 2015), 4) TDBrain (Van Dijk et al., 2022), ), 5) ADFTD (Miltiadous et al., 2023), 6) BCIC-IV-2a (Brunner et al., 2008),
7) Workload (Zyma et al., 2019), 8) HMC (Alvarez-Ester Estevez & Rijsman, 2021), and 9) TUSL (von Weltin et al., 2017). We employed two evaluation strategies: first, we used linear probing to directly evaluate the quality of the representations learned by the pre-trained model; second, conducted full fine-tuning on downstream tasks to check the model's generalization and adaptation abilities.
2) Evaluation Metrics. We choose the following metrics to evaluate the model's generalization abilitics across various tasks: A) Balanced Accuracy: The metric is defined as the arithmetic mean of each class's recall. B) AUROC: The metric represents the model's ability to distinguish between
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classes, which is independent of the chosen classification threshold. C) AUC-PR: the area under the curve that plots precision against recall for different classification thresholds. D) Cohen's Kappa (x): The metric is used to measure the agreement between classifier's ' predictions and the ground truth. E) Fi-Score: The metric is the harmonic mean of precision and recall. In the subsequent experiments,
The metric is
select Balanced Accuracy, AUROC, and AUC-PR the evaluation metric for binary classification tasks. And we adopt Balanced Accuracy, Cohen's Kappa, and FI- Score to evaluate the performance on multi-class classification tasks. The detailed introduction of the formulas is in Appendix C.
3.3
RESULTS ON DOWNSTREAM DATASETS
Table 1: Best performances on TUAB, TUEV, and SEED.
Mithed
TUAB (2-clane
TUEV (6-chase)
SEED (3-elass
Ballanoed Ace.
AUC FR
AUROC
Unlanced Acc. Coben's Kappo
Weighted Fi
Balanced Acc. Cohen's Kappa Weighned ITI
Foutitiont Toskopuryje- Madioos (Sngpeedrok)
SPaRCNet Gling 2003)
17,49
83.14_09
86.3 86.31an-w
43
43.79
68.14
57, 57.36gam
35.27
26.35
EBGNet (Lanbenn es dl. 2015)
17.12
82.51
85001
39.75
34,93
63.84
02 29
44.73
61.772244
CNN Transformer (Pah et al. 2023)
84.86
85.88
41.94a1a
39.52.132
67.82
61.61ass
42.62amm
61.50
IRGGonformer (Song ot al, 2022)
37
84.51 uo
8503.
41.34azas
40.27
69:72
67. 67.42.2m
18.8
64.31
FFCL (1i at, 2022)
27.94_.10
84.33
85944
40.02.1.55
22.97
67.99_13
57.76u2m
37.66.5.2
58.33
ST. Tranfoeee (Sou et cl, 2021)
79.25 2017
85.36 a7.
80.89
19.59
28.33
09.11
50.39.1m7
37.44.1.32
56.27
CoutrW ( Clang et al, 20238)
84.60_a.m
84.61
43.5812.71
68.36_144 42
60.34_aav
41.31,1.22
60.99.1.33
Only Pretsstee Poundaaton Models (Moltr-moss)
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45.00
73.59.2.1
60.31...10
40
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22.22... 4.
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62.32
63.51
61.87
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47.36
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71.5
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17.46us os
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14.50.12 22
70.31
70:15
10.9
76.71
81
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78.44
82.59 .1.7
82.78a3.35
62.44.19
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71.42..31
Peetroiond and Free- Raucdation Moaily (Stegiestak))
BIOT (Yang etal., 20133)
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87.92
88.15
42,81
52.73
74.93
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56.82
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$1,40 10
89.60 65
90.22
64.09
66.57 anss
R3.12
59.94
73.54.m2
CBiMMod (Wang et 202386)
78.91
85 36
86 26-05a
66.71
67.72mm
83.42.00
72
57 17
7300
CShmin (Phen al, 20255)
61.77
90,5 aam
89 89.5710-4s
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68.33 6
61.33
75.02
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Uni-NTFMMNo
76, 76.49
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83.25.1.
65 65.8221
81.74_0m
71. 71.18a904
57 57.22114
71:97
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85. 41
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50.81
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88337
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60.22.1:31
84.09..
72 72.90.a:1
59 22222.0
73.42.041
Uni NTFMaurg
89.82.9.30
8966113.8
69.91.1.0
70.30_1.4
84.66.133
79.37.945
59.7610.34
A The CSBrain code is not open-souree, and the data the table all provided from paper.
To comprehensively evaluate the generalization abilities of Uni-NTFM, we assessed its performance across nine diverse downstream tasks using two distinct strategies: linear probing of the frozen pre- trained model and full fine-tuning. Results are presented as the mean + standard deviation, calcu- lated over five independent trials, and comprehensive table summarizing model size and perfor- mance is provided in the Appendix D. As detailed in Table 1, 2, 3, our results consistently demon- strate the superiority of the Uni-NTFN, paradigm, establishing a new of-te-of (SOTA) across a wide field of EEG analysis application. In addition, we also conducted scaling law experiments on Uni-NTFM, and the detailed results can be found in Appendix H.
1) Linear Probing Performance: Under the linear probing setting, which directly measures the in- trinsic quality of the learned representations, Uni-NTFM exhibits remarkable transferability and rep- resentation quality. Even without fine-tuning, the model consistently outperforms traditional task- specific methods and other pretrained foundation models across the majority of tasks. Specifically, on the TUAB abnormal detection task, the Uni-NTFMiarge model achieves Balanced Accuracy of 0.7844, significantly surpassing the task-specific SPARCNet model. This performance across varied tasks, from clinical event detection to cognitive state classification, highlights the universal representations learned through our proposed dual-domain, structure- pretraining objective.
2) Fine-tuned Performance: By full fine-tuning, Uni-NTFM's performance is further promoted, highlighting its strong ability for task-specific adaptation. On all nine datasets, the fine-tuned Uni- NTFM variants consistently set new performance benchmarks. Especially on complex multi-class tasks such as SEED (Emotion Recognition) and TUEV (Event Type Classification), Uni-NTEM demonstrates substantial benefits over both task-specific models and other foundation models like LaBraM- BaBreM-Base and CBraMod. For example, on the 3-class SEED task, Uni-NTFMaarge achieves a Balanced Accuracy of 0.7337, indicating its robust ability to decode different cognitive states.
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Published as a conference paper at ICLR 2026
Table 2: Best performances on TDBrain, ADFTD, and BCIC-IV-2a.
Methed
TDirain (2eelass)
ADFTD (3elpo))
BCIC--VV2a (declas))
Itllanced Acc.
AUC-PR
AUROC
Bolanced Arc.
Cohen's Kappo
Weighted Fi
Balanced Acc. Coben's Kappa Weighted Fi
Fraditiona Tosk-puryjic Madiooh (Stgle-dns))
SPaRCNet Oling ala 2023
82.66_...0
81.7913.16
11.32.1.55
22.88
73.37 1:
46.97.141
2..71
44.60.1.37
LiGGNet (Lawhern et al. 2018)
75. 1.10011. 30
82. 12.23:9.m
85.1413.05
74.3221.55
76.56_1.55
74.42_1
44.02.1115
26.42a3m
43.02.1.34
Tan-frraformrr (Pth et al. 2022)
13.13
82.94.2a7
86.6231.00
67.89
20
67.29 m
m
25.25
48.73a3.25
FRRO Cnnfrmer (Sing et al, 2010)
79.25u.1 10
8381 o
8531
33
23.9
73.01 06
46
25.8221.3
(1 atal,, 1022)
99
56 in
15
68.25
70:0 14
14
ST-TTandeemer (long all, 2021)
L.
1..32
75.041155
2344.230
82 2:00
11.2
27.09
44.6311.2
CantraWR ( Chang all 20236)
80.17 80.17
84.2 84.22.1
26.04
72.01g100
25.22
73.75__2
46.82g1.77
28.94
44.3421.53
Only Preteatced Fowndiation Models (Afult) moka)
Nesrol Uhing 2004g)
75.522114
67 a7
72.57-a1 in
51 71
49.2220
EROPT (Wing 2006g)
an
8561
81:92 14
$2.81
11.29 20
Uni NITM...
$1,00
85.19
85.33
06.5
66,25
70.29
51 51.22
39.84
50.25 2002
K2.00 a0
85.62
16.18
67 25.3
67.4
71.51 15
$2.00 32.09 am
40.4
11.23
Shi-NTFM
k2.12_aa
85.70.a.
86.7211.19
67
67.80
68.51
72.08
52 12.60.3.0
41.07 an.2
50.77
UIi-N
82.46
85.84_1
86.91 16.79
68 23
72.00
82.56 2256
41.1811.4
51.44.1.5
Pretronted und Fine nssed Foundation Mosels (Stegie
BIOT (Yong etal. 2013s)
00,06
94 2o.
85. 25.31
77.22 27:21:0m T1
47. 27.08
29.92
46.07.3. 25
LaliraM-Basee Oling et 20046
$1.25 in
22:3 ar
86 88.00.0
74 34.92
73, 13
77.47 10
55 95977a in
56.28en
CRinMad (Wang et 20340)
85, 37
87 87:4440.4
76. 11
75, 50
75. 75.33
51 M
85 18
CSBran * (Zhou et 201250)
56.57asii
42 42.0
56,37
Uni MoNoo
62,25
56us
86 1a
24,70.n02
75,17
76
S4. S4.23aasa
41 41
Uni-NTTMMaaa
63.1 63.
95
87 17.06
75.68 18.5
76. 26.3
76
55. $9.500
42 42001
54.65
Uni--TTMM..ses
69.37
87 9aa
76.202nss
77.
55, BRasor
90
N5.07
87.48....a
76.81
76.54.n u
77.58
56.08,mm
42.66.0a
55.33
# "The CSBrain code is not open- source, the data the table all provided
paper.
Table 3: Best performances Workload, HMC, and TUSL.
Mothed
Workhund (2-dhee)
HMC (5-dasi)
TUSL (3-chass)
Balanced Acc.
AUC PR
AUROC
Belanced Acc. Cobes's Kappa
Weighted FJ Balanced Ace. Cohen's Kappa Weighted F1
Pradiional Tast-ggergte Methoos (Sipple-nsk)
SPARC Ulng atal. 200.)
67.26.... cs
45.37
47.89_2277
24
40.85.2.1
58.40 c4.s
EBGNer (Lawben et 2018)
58.17.2m
12.38
62.10.2.12
16.14.1.5
47.37
49.33
52.92.6.4
Transoomer (Psh 2002)
59.11.1.e
57.23 33.7
593111206
66.19.2.1
58.22.241
12.66...5
48.72.2.w
56.441244 12.45
EBGConiomme (Song al,, 2022)
233
69.22.1.41
68.773..2
69.78.141
62.16.231
59.72.s11
51.3314.75
60.02.4.3
ata 2012)
16.09_1.15
75.6013m
63.6813:45
38.76.1mm
47.08
51.36.1.52
Tranfemer (Song all, 2021)
60.43:1.2
25.81:144
62.55.3.77
59.4925.11
50:07.2m
28.
30.16
41.20....s
ContraWR Chng atal, 200
67,55 45
75.91.2 w
76.33an.
61 29
62.31
58.33am
42.67a544
Pasinsiond Reundation Modalb (Mulli-noks)
NearoLM Olang al., 2024g)
63.45
58,89.4
61.30.7:44
17.61
47.55
68.45.301
5199
68.39
DBGPT (Wing atal. 2024a)
62.99.1.7
07.92.9
69.2813.06
10.29..04
65.84int1
73.23
72 72.85:1.3
59.7212.09
12.35.1.51
UTE-NTEM....
Uni-NIFM.....
63.0621 44
64.11am
on
69.55.1.47
20
69 65:35
62.55gamn
68.734
71.38
F2:
Uni NIEM......
63.79.118
67.86..
21.34.mm
65.50.1
11
72.50
63.52
Uni MITMarrge
64.16.2.e
68.48:1.0
7087:1
21.35:2:1
66.11 4.
72.44-11
73.14:1:1
64.11:2.00
73.13as.::
Pietroned and Pine nesdd Foundation Medels (Slngic suak)
BIOT (Yang atal, 2033g)
06.55 c4
71.89.1.2
784221..3
68.0
63.05_1.1
20.91 _1
12
13.94.0.43
LaBraM-BBscc (linng ei 2004))
72.724144
72.56.1a:
68. 12
75.54-n
76 26.25.121
64.07.2.0
76.34222
CBuMaa (Wang et 211400
63.37
10.39.1.3
700774..2
12.69...1
66.85.1
7335
73 73.88.150
61.49...0
74.53
CSBnia 4 (Chou et 201500
13.45
68.1
75.95.04
25.71
78.2H...0
S5.6....3
UTF-N
65.80 65.00
70844.211
72.621..9
72.44.1.3
66.91
74.41.m
76 76.69
66:11
7645..277
Uni-NTFMMmoo
66.72_171
71.75,1.0
7388
12.37
67.00.13
75.51_a
78 78.00.171
67.52 67.52.1131
77.10.1131
66.16
71.66.1.1
75466402
12.58.1.
77.79..m
67.75...14
76.91 22:1
66.44_1
72.28_..0
74
73.11,017
68.32
75.72 04
78.44_200
68.53gg.3
17.46g1
V The CSBrain code is not open-source, and the data the table all provided from paper.
3.4 ABLATION STUDY
To systematically validate the effect of each core component of the Uni-NTFM architecture, we conducted comprehensive ablation study on the TUAB and TUEV downstream tasks, and the results are detailed in Table 4, and the visualization of ablation study on the TUAB and TUEV downstream tasks are shown in Fig 3.
The baseline model (A1, BI), which removes all our proposed modules and represents a standard Vision Transformer, establishes the lowest performance benchmark. The introduction of the HFPM (A2, B2) yields the most substantial single-component performance gain, increasing AUROC from 0.7116 to 0.7805 on TUAB. This highlights the critical importance of decoupling and encoding het- erogeneous features. Subsequently, the addition of the DCM (A3, B3) and the TE (A8, B8) further improves performance, confirming their respective functions in fusing multi-domain information and inserting spatial priors. Besides, the MoE module provides limited improvement when added in alone (A5, B5), its contribution becomes significantly more pronounced when combined with other components (A6, A7, B6, B7). The full model (A10, B B10), which integrates all components,
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Published as a conference paper at ICLR 2026
Table 4: Ablation study on TUAB and TUEV datasets.
Modules
TUAB (2-class)
TUEV (6-class)
No.
HFPM
DCM
TE
MoE
Balanced Ace.
AUC-PR
AUROC
Balanced Ace.
Cohen's Kappa
Weighted F1
X
x
X
X
62.52
68.71
71.6615.7
58.74
62.29
73.6612.4
69.95
77.78-2.90
78.05
61.45=2.56
63.34
a3.51
3
V
K
K
73 73.64-22.11
79.92-2.11
79 79.7642.85
62.88
63.93
78.72
V
67.40
74.05
76. 16.144
60.60
64.11
77.81
5
X
66.23
73.87
78.62
59.86_3.41
62.79
12.52
6
X
+1.81
78.49=1.46
80.03
61.50
64.28
78.94
23.34
79.33_1.86
81.10+2.55
63.3522.49
65.3921.88
80.81
8
V
74 1499
81.37
82.39 82.39
63. 16
64,80 14.80=1.0
80. 1..1
11/63
80.94_2.24
11.40
63.53
64.41 21:75
19.39
10
76.49
82.30
83.25
64.05
65.8221.45
81.74
HF PM: Heterogeneons Feature Projection Module; DCM: Dusi-domain Cross-attention Medule, TE: Topological Emhedding, and Mok: Mantae--f-Exgeet.
A18
A1
B10
BI
$32
$1.7
RK
625
AR
TUAB Dataset
5.9
A3
m
TUEV Dateset
Compenreh
A4
Compusrass
B4
BCM
Belanced Are. AUC--P
DCM
Balanced
TT:
TK
Cohon's Kappa Weighted Ft
Mol
(a) Ablation Study the TUAB Dataset.
(b) Ablation Study the TUEV Dataset.
Figure 3: The visualization of ablation study on the TUAB and TUEV downstream tasks.
achieves the highest performance across all metrics on both datasets. Especially on the TUEV, the full model achieves a significant improvement over the baseline and other combination of modules.
4 CONCLUSIONS
This paper introduces the Unified Neural Topological Foundation Model (Uni-NTFM), which coun- ters the limitations of traditional models by adopting an architecture designed from the first prin- ciples of neuroscience. Specifically, dard properties of EEG signals, and insert the spatial priors of multi-domain representations before transfor into the scalable MoE neural Transformer for specialized feature processing. Moreover, pretrained on over 28,000 hours of data, Uni-NTFM sets the standard across nine downstream tasks, demonstrating superior generalization. The success of model validates the structure-aware mod- eling theory that respects the characteristics of neural signals is necessary for releasing the potential of large-scale brain foundation models. We hope this work provides a method for the next generation of efficient and interpretable brain- intelligeeeecee interfaces in neuroscience applications.
simultancously process the temporal, frequency, and stan-
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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENT
This work is supported by the Natural Science Foundation of China NN.662324887, Improvement Project of Chinese Academy Sciences (No.G522XKY222025000), and the Science and Technol- ogy Innovation Program of Hunan Province (No.2024119031).
ETHICS STATEMENT
This research conducted exclusively using publicly available and fully anonymized EEG datasets. Our model, Uni-NTFM,, is intended for foundational research purposes. We acknowledge that potential biases may be inherited from the training data, and addressing fairness a critical step for future real-worid applications. The overarching goal of this work is to contribute positively to the fields of neuroscience and medicine.
REPRODUCIBILITY
To ensure full reproducibility, we will provide the following:
Code and Models: All source code will be released under an open-source license at https: pettps:/anonnee anonymooss 4oper. science/eeeeee Bttpe////nnNNNMM---
Datasets: All datasets for pre-training and evaluation are publicly available and are detailed in Appendix Table and Table 7, respectively.
Detailed configurations and hyperparameters for all model variants and experiments are provided in the Appendix (Table 8, 9, 10, 11).
Hyperparameters:
Environment: The computing environment is specified in Section 3.1.
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W. Zheng, W. Liu, Lu, B. Lu, and A. Cichocki. Emotionmeter: multimodal framework for recognizing human emotions. IEEE Transactions on Cybernetics, pp. 1-13, 2018. ISSN 2168- 2267. doi: 10. 20.1100/TCB...218227777
Wei-Long Zheng and Bao-Liang Lu. Investigating critical frequency bands and channels for eeg- based emotion recognition with deep neural networks. IEEE Transactions on autonomous mental development, 7(3):162-175, 2015.
Xinliang Zhou, Chenyu Liu, Zhisheng Chen, Kun Wang, Yi Ding, Ziyu Jia, and Qingsong Wen. Brain foundation models: survey on advancements neural signal processing and brain dis- covery, 2025a. URL
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Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariia Chernykh, and Oleksii Shpenkov. Electroencephaaograns during mental arithmetic task performance. Data, 4(1):14, 2019.
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A RELATED WORK
A.1 MOTIVATION FOR BRAIN FOUNDATION MODELS
Traditional models for elcctreencephalograhyy (EEG) decoding were mainly task-specific, leading to several core limitations that motivated the shift towards foundation models. These challenges include (Kuruppu et al., 2025; Xiong et al., 2025; Wang et al., 2024c): 1) Poor Generalizability: Models trained for specific task, dataset, individual struggled to transfer knowledge new contexts. 2) Data Heterogeneity: EEG data varies significantly across studies in terms of elec- trode configurations, signal lengths, and sampling rates, creating barriers cross-dataset learning. 3) Expensive Annotation: Labeling EEG data requires significant domain expertise and is time- consuming, making large-scale supervised datasets scarce. To overcome these issues, researchers have turned to developing Brain Foundation Models (BFMs), which learn universal and transferable neural representations through self-supervised pre-training on large, diverse datasets (Li al., 2025; Babu et al., 2025; Aristimunha et al., 2025).
A.2 INNOVATIONS IN BRAIN FOUNDATION MODELS
Existing BFMs have advanced the field through innovations in architecture and pre-training strate- gies, primarily centered around self-supervised learning.
A.2.1 RECONSTRUCTION- BASED PRE-TRAINING
This paradigm, inspired by masked autoencoders, the most common approach. Models are trained to reconstruct masked portions of the EEG signal. LaBraM (Jiang et al., 2024b) pioneered the use of a pector-quantize neural tokenizer to convert continuous EEG signals into discrete neural codes. By pre-training the model to predict these masked codes, it effectively mitigates signal noise. It has been applied to tasks such as abnormal detection and emotion recognition. EEGPT (Wang et al,, 2024a) introduced dual self-supervised learning strategy that combines masked reconstruction with spatio-temporal representation alignment. Instead of reconstructing the raw signal, it aligns the features of masked segments with those of the complete signal, thereby improving representation quality. Brain Wave (Yuan et al., 2024) is the first foundation model jointly pre-trained on both invasive (iEEG) and non-invasive (EEG) neural signals. It has demonstrated strong zero-shot and few-shot classification abilities in the diagnosis and identification of various neurological disorders. CBraMod (Wang et al., 2024b) employs a crisscross transformer backbone to model the EEG sig- nals by processing spatial and temporal dependencies in parallel and uses an asymmetric conditional positional encoding to adapt to diverse EEG formats. has been evaluated on over 10 BCI tasks, including emotion recognition and seizure detection.
A.2.2 ARCHITECTURES TAILORED TO EEG STRUCTURE
CSBrain (Zhou et al., 2025b) addresses the intrinsic crossscale nature EEG signals by introduc- ing Cross-scale Spatiotemporal Tokenization and Structured Sparse Attention. This design explic- itly models neural patterns at multiple resolutions to suit diverse task requirements. CodeBrain (Ma al., 2025 jinrrouoss a TFDual- Fookei-Toeenizer to independently encode temporal and frequency com- ponents. Its backbone is an efficient State Space Model designed to capture the sparse, long-range dependencies characteristic of brain.
A.2.3 DOMAIN-SPECIHIC AND LLM-INTEGRATED MODELS
NeuroLM (Jiang et al., 2024a) and UniMind (Lu al., 2025) are pioneering models that integrate EEG encoders with Large Language Models (LLMs) to create unified, multi-task decoders that op- erate via instruction tuning. To bridge the significant modality gap, NeuroLM uses adversarial training to align EEG and text embedding spaces, while UniMind designs a Neuro-Languag Con- nector and a Task-aware Query Selection module distill neural patterns into LLM- -i--interprettalle representations. These models are applied to a wide range of tasks including sleep staging and clinical event classification.
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B THE USE OF LARGE LANGUAGE MODELS(LLMS)
During the preparation of this manuscript, used Google's Gemini, writing-assistacee tool. The use of the LLM was strictly limited to improving the language and readability of our text. Key applications included:
Proofreading for grammatical errors, spelling mistakes, and incorrect punctuation.
Rephrasing sentences to enhance clarity and conciseness.
Ensuring a consistent and formal academic tone throughout the document.
Crucially, the LLM was not used for any core scientific aspects of this work. All conceptual con- tributions, including the formulation of the research problem, the development of the methodology, and the execution of experiments, are exclusively the work the human authors. The authors have reviewed and edited all text, and take full responsibility for the scientific integrity and final content of this paper.
C DESCRIPTIONS OF EVALUATION METRICS
In a classification context, confusion matrix is used visualize the performance of algorithm.
For binary classification problem, the matrix consists of four fundamental quantities:
True Positives (TP): The number of positive instances that were correctly classified as positive.
True Negatives (TN): The number of negative instances that were correctly classified as negative.
False Positives (FP): The number of negative instances that were incorrectly classified as positive. This also known as a Type error.
False Negatives (FN): The number of positive instances that were incorrectly classified as negative. This is also known Type error.
Based on these four values, we can define several key evaluation metrics.
DETAILED EVALUATION METRICS
C.1 BALANCED ACCURACY
Balanced Accuracy is the arithmetic of the recall for each class. It is particularly useful for datasets with imbalanced class distributions as it avoids inflated performance estimates.
For problem with C' classes, the recall for class is first calculated
TP: Recall, TP; + FN,
(17)
where TP; and FN; are the true positives and false negatives for class i, respectively. The Balanced Accuracy is then computed by averaging these recall values:
C
TP: TP. T +N
Balanced Accuracy
Recall;
(18)
C.2 AUROC (AREA UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE)
The AUROC metric evaluates model's ability to distinguish between classes across all possible classification thresholds. The ROC curve is plot of the True Positive Rate (TPR) against the False
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Positive Rate (FPR).
TP
TPR (Sensitivity)
(19)
TP + FN FP
FPR (1 Specificity)
(20)
FP + TN
Mathematically, the area under this curve is calculated by integrating the TPR function with respect the FPR:
AUROC = L. TPR((PR) d(FPR)
(21)
AUROC the area under this curve, with a value ranging from to A value of indicates perfect classifier, while 0.5 suggests performance no better than random chance.
C.3 AUC-PR (AREA UNDER THE PRECISION RECALL CURVE)
CHECISION-RECALL
The AUC-PR metric is the area under the curve that plots Precision against Recall at various thresh- olds.
TP
Precision
(22)
TP
TP
Recall
(23)
TP + FN
The area is computed by integrating the precision function P(r) with respect to recall r:
AUC-PR- - S. P(r) dr
(24)
This metric is especially informative for imbalanced datasets, as its calculation does not depend on the number of True Negatives. higher AUC-PR value indicates better model performance.
C.4 COHEN'S 'S KAPPA (R)
Cohen's Kappa coefficient (x) measures the agreement between classifier's predictions and the ground truth, correcting for the probability of agreement occurring by chance. It is a more robust metric than simple accuracy on imbalanced datasets. The formula
Po Pe
(25)
1 - Pe
where:
Po is the observed agreement (i.e., overall accuracy):
TP+ TN
P. TP + +N + F
Pe is the expected probability of chance agreement. For a binary case, is calculated as:
(TP FP) + FN) +FN (FP TN)
Pe
(Total Samples)²
C.5 F1-SCORE
The F1-Score is the harmonic mean of Precision and Recall. It provides single that balances both concerns, making it useful metric when both false positives and false negatives are important.
Precision Recall
2TP
F 2 Precision + Recall
(26)
2TP + FN
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D COMPREHENSIVE COMPARISON OF COMPLEXITY AND PERFORMANCE
Table 5: Comprehensive Comparison of Complexity and Performance.
Model Complexity
Binary Classification
Multi-class Classificatinn
Model
(Parameters)
Total
Infer.
Acc.
AUC-PR
AUROC
Bal. Acc.
Kuppa
W-FI
Traditional Task-specifi Methods
SPARCNet (Jing et all,, 2023)
0.79 M
0.79 M
71.16
76.79
78.83
55.17
59.65
40.41
BEGNet (Lawhern 2018)
2.8
71.04
74.40
78.40
56.79
48.13
59.79
CNN Transformer (Peh 2022)
3.2M
71.83
75.01
76.04
56.18
48.01
60.86
EFCConformer (Song al,, 2022)
L
1
74.19
62.63
FFCL (Li et al.,, 2022)
2.4
2.M
74.74
79.80
80.52
55.99
45.41
59.68
ST TT-rassformrr (Song al., 2021)
3
3.5
72.36
75.19
77.60
54.17
42.75
57.92
ContraWR (Yang et ul., 2023b)
M
1.6
75.08
81.58
80.33
57.13
47.49
60.81
Foundation Models
M--L (Tiang 2024a)
1.7
L.
73.14
68.63
71.89
58.77
48.91
63.86
EEGPT (Wang et al., 2024a)
25 M
74.72
75.41
80.50
65.81
58.28
68.94
BIOT (Yang ol,, 2023a)
75.60
81.58
81,29
62.52
60.73
LaBraM- Base (Jiang et 2024b)
5.8
76.25
81.87
82.07
69.55
62.25
23.67
CBraMod (Wang et al,, 2024b)
4.0
M.
75.70
80.71
81.19
68.96
60.75
71.68
Proposed Models (Uni-NTFM)
Uni-NTFMaion
M
74.78
79.57
80.66
68.88
62.19
72.48
Uni-NTEM, Amonl
M
76.35
81.07
82.31
70.10
63.35
73.34
Uni-NTFM middle
M
76.78
73.65
Uni-NTFMarrp
1.9
M
77.37
82.69
84.01
71.25
64.36
74.26
Note:
Infer:
Inference Parameters; Bal. Acc.: Balanced Accuracy; W-FI: Weighted FI.
Table 6 presents comparative analysis of model complexity, explicitly distinguishing between Total Parameters and Inference Parameters, which are indicative of the model's learning capacity and the actual computational cost during deployment, respectively.
A fundamental constraint biological brains is that the cortex operates on a "sparse coding" prin- ciple, where only some of the neurons activate for any given stimulus to minimize metabolic cost. Standard dense foundation models violate this principle by forcing full network activation for every input. Uni-NTFM addresses this by implementing sparse activation mechanism via the Mixture- of-Experts architecture. As shown in Table 6, while Uni-NTFMaarge scales to 1.9 billion parameters capture the immense heterogeneity of human neurophysiology. its dynamic routing ensures that only 307 million parameters are active per signal during inference. This effectively decouples the model's knowledge capacity from its execution cost, achieving considerable efficiency improve- ment compared to dense basclines.
We argue that model scale corresponds to the diversity of functional experts required decode the complex signal of the brain. Traditional lightweight models (e.g.. EEGNet, 2.8K params) function like specialized reflex circuits, which is efficient but limited in scope. In contrast, Uni-NTFM func- tions like the neacortex: it has various specialized experts to handle rare epilepsies, subtle emotional shifts, or artifacts. Our Uni-NTFMrsny exemplifies this balance: it leverages 57M total capacity to store generalized representations while maintaining inference of just 19M parameters (lower than the dense EEGPT). Thus, our architecture effectively balances the conflicting biological demands of high-capacity storage for generalization and rapid neural activation for real-time execution.
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E PSEUDOCODE
E. DETAILED EXPLAINATION OF HETEROGENEOUS FEATURE PROJECTION MODEL
Algorithm Heterogeneous Feature Projection Model
Input: preprocessed EEG data E RBXRRXXT
Output: three feature sequence matrices Hr, Hr, HR E RBxLTT
Xreshaped < reshapeeee [X, (B L,))
R
2: Initialize Hr, F H as empty lists
3: for E {l..... B L) do
4: Ij + X reshaped [
5:
hG, f- (T(x;)
> Dynamics Waveform Encoder (Time Path)
6:
Po(x4) + CaleulatcBundPwwer(z))
7:
h +- (Pr(((()))
b Frequency Decomposition Encoder (Frequency Path)
8: hi + (2((2))
Standard Projection Encoder (Raw Path)
9: Append hi, R to Hy, Hr, HR respectively
end for
11:
HT, Hp, HR + stack and reshape each list to BXLD
return HT, HF, HR
Input X: with dimensions X E BxREExT where B is the batch size, R is the number of regions, E is the number of electrodes per region, and T is the number of time steps.
Line 1: Reshape Data This step reshapes the original 4D tensor X into a 2D matrix. merges the region (R) and electrode (E) dimensions into a dimension L and flattens the batch (B) dimension into it. This is done to treat the time series each electrode as an independent sample, facilitating subsequent individual processing.
rreshoped E (BBL))XT where = R x
(27)
After reshaping, we obtain B x L independent time-series sequences, each of length T.
Lines 3-10: Iterative Feature Extraction The algorithm iterates through all B x L time-series sequences, performing three independent feature encoding steps for each sequence 2; R.::
The time series J; imported into a Dynamics Waveform Encoder TT. This encoder is typically neural network designed to capture the dynamic characteristics of the signal the time domain.
1) TEMPORAL FEATURE EXTRACTION (LINE. 5)
h (T(2)), where क R²
R
(28)
It maps the sequence of length T to feature vector har of dimension D.
2) FREQUENCY FEATURE EXTRACTION (LINES 6-7) This process is divided into two steps. First, the "CaleulateBanndPoeer" function computes the power of the time series x; across N₂ pre- defined frequency bands (e.g.. 5, f, a). After obtaining the band power vector pi. it is fed into the Frequency Decomposition Encoder DF.
= CalculateBanddowwer(ss...
E R.
(29)
h 2 where 4y IR + RD
(30)
This process results in feature vector h of dimension D.
3) STANDARD FEATURE EXTRACTION (LINE 8) The original time series x; also passed to a third, independent Standard Projection Encoder &2z- This encoder can use a different network architecture from Φ to provide a complementary feature perspective.
h4 R where 인R IR RD
(31)
The output is also feature vector hi, of dimension D.
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Line 11:
Stack and Reshape After the loop completes, the three lists of representations
stacked into matrices of shape (B-L) x and then reshaped into the final tensor shape of R BxIxX, restoring the batch dimension.
E.2 DETAILED EXPLAINATION OF TOPOLOGICAL EMBEDDING
Algorithm 2 Topological Embedding
Require: Temporal, frequency. and standard features Hr, HF, H8 E BRLLD Ensure: caapporal-frequency-tooooooooolo
features Hin E RBxLxD
1: rregion &- Generate Region Indices B, I., E)
2: lintra t Generate Intra Region Indices B, L,
3: Las &- (al.aaraagg())xppppe -1)
4: Eregion.enb
Bregion|4region
5: Eintraemb &- Emural Lintral
6: Eabs emb Eaba Fatsl
7: Hin <- Huseed + HR + Eregion emb + Eintra emb + Eavs emh
8:
return Hín
Lines 1-3: Generate Positional Indices The algorithm first generates three types of integer in- dices, all resulting in tensors of shape (B, L), to encode the spatial hierarchy of the electrodes:
I Region Index, which identifies the brain region (O,... R- 1) that each of the electrodes belongs to.
Fittra: RegranRegion Index, which indicates the relative position (0...., F 1) of an electrode within its specific region.
Jabs: Absolute Position Index, which gives the absolute position (O....,I. 1) of each electrode in the flattened sequence.
For any given electrode at absolute position j (O...., L- 1}. its indices are formally generated as:
(f)
Li/E]
(32)
region
(i) intra
(mod E)
(33)
where |-] is the floor operation and (mod E) gives the remainder of division by E.
Lines 4-6: Lookup Embeddings Using the generated indices, the algorithm retrieves correspond- ing feature vectors from three distinct, learnable embedding matrices. This process maps the discrete integer indices to dense, continuous vector representations. The learnable matrices are:
Erogion E RR An embedding matrix for the R brain regions.
Emnra E RREDD An embedding matrix for the E intra-region positions.
Eabs E R LD An embedding matrix for the absolute positions.
The lookup operation for an electrode at absolute position can be expressed as:
Eli
regionncmb
= EEegiia (2)
(34)
This operation is performed for all indices and all three embedding matrices, resulting in three embedding tensors (Eregion caggs emb. Emara emb- Ests,ent),, each of shape TR. BxLx
Line 7: Final Feature Fusion Finally, the algorithm performs an element addition to com- bine the time-frequency fused features (Hrssed) the standard projection features (Hr), and the three structural prior embeddings.
Hmm Huseed + HR + Ereiion.mb + Einta.smb + Eabs.omb
(35)
The result, Hm. is a unified representation that incorporates temporal, frequency, raw signal, and spatial information, ready for downstream model.
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F DATASETS
Table 6 provides detailed record of the nine public EEG datasets used to construct the large-scale pre-training corpus, a critical prerequisite for training a universal foundation model. The table not only lists basic parameters such as the name, sampling rate, and channel count for each dataset but also highlights the diversity of their origins in the "Description" column. This includes record- ings from resting-state conditions (e.g., LEEG-BACA, emotion induction tasks (e.g., Emobrain, SEED-series), and large-scale clinical data (e.g.. TUEG, CAUREG). This significant heterogeneity in recording equipment, experimental paradigms, and subject populations provides a robust data foundation for the model to learn truly generalizable and resilient neural representations.
Table 6:
Information of datasets used for pre-training.
Dataset
Rate (Hz)
Channels Time (H)
Subjects Description
Emobrain
1024
64
4.94
16
The multimodal emotion dataset contains record- ings from 16 participants. Emotional states were induced by presenting the subjects with curated selection of stimuli from the International Affec- tive Picture System (LAPS) dataset.
(Savran' et al.
2006)
REEG-BACA (Getzmann al., 2024)
1000
64
121.6
608
The dataset composed of 64-channel resting- state EEG recordings from initial cohort of 608 participants, of whom 61.8% were female, with age range of 20 to 70 years. Furthermore. longitudinal component of the study involved follow-up measurements for 208 of these partici- pants.
SEED-scries
1000
62
170.54
51 This series includes SEED-IV, SEED-V, SEED- GER, and SEED-FRA, with subject counts of 15, 20, 8, and 8, respectively.
(Zheng et al. 2018; Liu et al., 2021: 2022)
CAUEEG (Kim ct ol., 2023)
200
19
306
1388
The CAUEBG dataset is recorded at Chung-Ang University Hospital from August 24, 2012, to March 12, 2020. recordings adhered the International 10-20 system, utilizing linked carlobe referencing method.
TUEG (Obeid & Picone, 2016)
250-1024
17-23
27100
14987
Temple University Hospital EBG corpus has over 40 distinct channel setups and inconsistent recording lengths, and most data use sampling frequency of 256 Hz.
Raw EEG Data (Trujillo, 2020)
256
64
34.35
Datasets in BioSemi Data Format (BDF), which were recorded during the reported Infornation-Integratio Integration calegorization task and reported multidimensional Rulc- Bascd catego- rization task.
BCI Competition IV-1 (Blankeriz ct al., 2007)
1000
59
8.21
7 The EEG data were acquired using multi-channel amplifiers, sampled at 1000 Hz, and band-pass filtered from 0.05 to 200 Hz.
Resting State EEG Data (Tru- jillo et al., 2017)
256
64
3.04
22
A total of 22 undergraduate students from Texas State University (11 female, 11 male; mean age: 21.1 = 0.52 years; age range: 18-26) took part in this research.
Siena Scalp EEG Databuse (Detti 2020)
512
31
30.47
14 Data for this study were soureed from epilep- tic subjects, whose cerebral activity was recorded video scalp EEG. The signals were sampled 512 Hi, and the electrodes cording the international 10-20 system.
arranged ac-
Table serves as the core reference for validating the generalization abilities of the Uni-NTFM model, systematically organizing the nine benchmark datasets used for downstream task evaluation. To comprehensively assess the model's / performance, the selected tasks cover a range of impor- tant domains from clinical diagnostics EEG detection, TDBrain for psychiatric disorder classification, and BCIC-IV-2a for motor imagery recognition. The table clearly specifies the dataset for each task, the number of subjects, and the clas-
Brain-Compin Interfaces, such TUAB for abnormal
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Table Information of datasets used for downstream evaluation.
Task
Dataset
Rate (Hz)
Channels Subjects
Label
Abnormal Detection
TUAB (Harati al., 2015)
256
2,383
2-class
Event Type Classificution
TUEV (Haruti 2015)
256
370
6-class
Emotioo Recognition
SKKD (Zheng & L.,, 2015)
1000
62
16
5-class
Psychiatric Dysfunction Classification
TDBrain (Van Dijk et 2022)
500
1274
2-class
Neurodegenerative Disorder Classification
ADFTD (Miltiadous
2023)
500
65
3-class
Motor Imagery Clussification
BCIC-IV-2a (Brunner et al, 2008)
250
22
9
4-class
Cognitive Workloud Clussification
Workload (Zyma et ul., 2019)
500
36
2-class
Sleep Staging
HMC. (Alvarez Estevez & Rijsman, 2021)
256
5-class
Slowing Event Classification
TUSL (von Weltin al., 2017)
256
23
28
3-class
sification target (number of labels), providing clear context for the rigorous evaluation protocols, which include both Linear Probing and full Fine-tuning.
G MODEL SETTINGS OF DIFFERENT SCALES
Table 8 offers detailed configuration settings for the four core Uni-NTFM model variants designed in this research, demonstrating the progressive scaling from the 77M--arameter Uni-NTFM to the 1.9B-arrmettr Uni-NTFMaarge- It clearly outlines the differences key architectural parame- ters among the variants, including embedding dimension, Transformer network depth, and the num- ber of experts in the MoE module. Furthermore, the table specifies the base hyperparameters, such as learning rate, optimizer weight decay, and gradient clipping threshold, to ensure a fair comparison between models different scales and to support the reproducibility of the experiments.
Table 8: Configurations and hyperparameters for different variants of Uni-NTFM.
Settings
Uni-NTFM
Uni-NTFM small
Uni-NTFM middle
Uni-NTFM Marge
Model size
57M
427M
912M
1.9B
Emb dim *
256
512
512
768
Transformer depth
12
26
24
Number of experts
8
16
16
16
Batch size
128
128
64
32
Numbers GPU
16
GPU
NVIDIA A100-SXM4-80G
Number regions
Sequence of length
1600
Dropout ratio
0.1
Mask ratio
0.25
Number of frequency bands
Freqency loss weight
0.2
Time loss weight
0.8
Learning rate
3e-5
Weight decay
le-4
Total epochs
50
Warmup epochs
5
Gradient clipping
1.0
Use AMP
True
Emm_dim refers to Transformer Embedding dimension.
Table 9, 10, 11 collectively form the detailed technical appendix for the model scaling law experi- ments, ensuring the transparency and reproducibility this part of the research. They systematically record the precise architectural settings for a series of twelve models ranging in size from 10M to IB parameters. Readers can clearly observe how key parameters like embedding dimension, Trans- former depth, and the number of experts were carefully adjusted to achieve specific model sizes. This series of detailed configurations ensures that the investigation into the relationship between model performance and parameter count conducted under controlled and systematic conditions.
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Table 9: Configurations and hyperparameters for 10M ~ 200M of Uni-NTFM.
Settings
Uni-NTFM...s
URi-TTEM..G2
Uni-NTFM
Uni-NTFM
Model size
10M
50M
100M
200M
Embudii
256
256
256
Transforace depth
10
16
32
Number of experts
8
12
16
size Numbers of GPU
128
GPU
NVIDIA A100-SXM4-80
Number regions
Sequence of length
1600
Drapout ratio
0.1
Mask ratio
0.25
Number frequency
Freugaayy loss weight
0.2
Time weight
0.8
Learning
Weight decay
le-4
Total epocits
50
Warmup cpochss
epochs
Gradient clipping
1.0
Use AMP
True
* Emb dim refers Transformer Embedding dimension.
Table 10: Configurations and hyperparameters for 300M ~ 600M of Uni-NTFM.
Settings
Uni-NTFM
UnL-NTEM,ins
Uni-NTFM,
Uni-NTIM
Model
500M
Emb dim.
512
512
512
512
Trunsformer depth
11
11
14
17
Number experts
16
Batch size
128
128
128
Numbers of GPU
8
8
GPU
NVIDIA L100-XXM4-800
regions
Sequence of longth
1600
Dropost ratio
D.1
Mask ratio
Number of frequency bands
Fregency loss weight
0.2
Time weight
Learning rate
36-5
Weight decay
le-1
Total epochs
50
Warmup epochs
Gradient clipping
1.0
Use AMP
True
Emb dim refers Transtormer Embedding dimension.
Table 11: Configurations and hyperparameters for 700M ~ 1B of Uni-NTFM.
Settings
Uni-NTFM Uni-NTFMM...s
Uni-NTFM...oo
Uni-NTFM
Model
800M
1B
Einbulim
512
512
768
Transformer depti
20
23
26
13
Number of experts
16
16
16
16
Butch
64
GPU
GPU
NVIDIA A 4001-XM44NIIC
Number of regions
Sequence length
1600
Dropout
Mark ratio
0.25
Number of frequency hands
5
loss weight
0.2
Time weight
Learning rate
30-5
Weight decay
le-1
Total epuchs
Warmup epochs
clipping
Use AMP
Tree
Emb fim refers to Transformer Embedding dimension.
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H DETAILED RESULTS OF SCALING LAW
To systematically discuss the impact of model and data scale on the performance of Uni-NTFM, we conducted two controlled scaling law experiments. The specific configurations for each model and other detailed hyperparameter settings are provided in Table 9, 10. 11. The results visualized in Figure 4 intuitively demonstrate that the representational quality of Uni-NTFM scales positively with both the number of model parameters and the volume of pre-training data. The detailed data results are shown in Table and 13.
Performance of Scaling Laws: Model Size Data Size
325
1/0
2:000
80
200
477
18
Dat Shard
Saa
(31m))
Pretutiuad Only
Pretesised Finc-tuned
Figure 4: This figure shows the scaling laws of the Uni-NTFM model, revealing a positive correla- tion between performance and both model size and data volume. The top row of plots indicates that with fixed pre- pre-training data size, model performance steadily improves as the number of parame- ters increases. The bottom row shows that for fixed model size, performance also scales positively with more of pre-training data. All plots contrast the performance under "Pretrained Only" (blue curve) and "Pretrained Fine-tuned" (red curve) evaluation settings.
1) Impact of Model Size: We first evaluated the effect of model scale by training models ranging from approximately 10M to IB parameters on fixed pre-training corpus of 10,000 hours. As shown in Table 12, downstream performance on both TUAB and TUEV tasks exhibits clear and positive relationship with model size. Specifically, in the fine-tuned setting on the TUAB task, the AUROC score consistently improves from 0.7146 for the 10M model peak of 0.7536 for the 800M model. This trend holds across all metrics for both linear probing and fine-tuning detection, confirming that larger models can learn more powerful and universal representations. However, performance appears to saturate and slightly reduce for models larger than 800M, suggesting that the corpus of 10,000 hours may be insufficient to fully release the potential of models with Aallion-parameter
We fixed the model size to our 57M parameter variant (Uni-NTFMaing) and varied the pre-training corpus from 1,500 12,000 hours. The results in Figure 4 and Table 13 indicate strong dependence of performance on data size. On the TUEV task, for example, the fine-tuned Balanced Accuracy steadily increases from 0.5613 with 1, 1,500 hours of data to 0.5934 with 12,000 hours. The performance curves do not show signs of saturation, indicating that even the smaller 57M model could benefit from further pre- pre-training on a larger corpus.
2) Impact of Data Size:
Table 12 presents the core experimental data from the scaling law analysis, focusing the impact of model size. It quantitatively demonstrates the specific performance of Uni-NTFM on the TUAB and TUEV downstream tasks as its parameter count increases from approximately 10M to IB. The data clearly show that, in both linear probing and tine-tuning settings, larger models generally learn more powerful universal representations, reflected by the steady improvement across most evaluation
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metrics. The data in this table also reveal an interesting phenomenon: performance slightly saturates or declines when model size exceeds 800M, and we infer that the training corpus may be insufficient to fully unlock the potential of the largest models.
Table 12: Quantitative analysis of the impact of model size on TUAB and TUEV.
(2-class)
(6-cluss
Model Size
Balanced Acc.
AUC-PR
AUROC
Balenced Acc.
Coben's Kappa
Weighted Fl
Only Pretrained Foundation Madels (Multi tasks)
10.301,200 (~10M)
61.3221.47
67 21.2621
68, g1.79
52.37
56.88_1.o
71.92
48,335,760 (-50M)
61.7911.62
67.44 11.17
69.12.1.9
53. 33.1511:45
57.31 L3.01
72.32
108,808.599 (-100M)
62.68
68.81
70.42:1.56
53.40
57.55
72.91
203,197,584 (~200M)
63.05
69.54.1.m
70.98.am1
13.99
27.97_1.85
73.34gm.27
299,943,056 (~300M)
63.72.1.21
10.13
71.59
54.2612.18
58.53_1.52
73.59
392.352,912 (~-400M)
64.83+0.07
70.75-2.12
71.90=1.4
54.58+1.26
58.96-1.09
74.26=1.8t
496.317.072 (~500M)
65.57 11.37
71.31
72.64.2.41
54.92
39.32 1.11
74.63
800,281,232 (~-00M)
66.01+1.27
71.96-1.74
73.02
55.63++2.0
59.56-1.28
74.90
704,245, (~700M)
66.34_1.99 11:95
72.23 19.05
73.14:1.0
55.81
39.60) 22.31
74.84.1.75
808,209 (~-000M)
+3.12
72.31=1.31
=3.37
55.70+1.a2
59.29-1.05
74.87=1.18
912,173.712 (--90M)
65.99
72.07
73.03
$5.72
19.14_2.11
74.65
1,88,695,760 (x-1))
68 go
70.82 11
g2.85
12.00
58.71
74.30g1.10
Pretruined and Fine-tuned Foundation Modeis (Single-tant)
10,301.200 (~10M)
64.59 64.5910.51 10.51
70.81 jo.as
71.461048
10
60.10_1..s
78.15
48.335.760 (-50M)
64.92
71.32
71.91
58 58.6440.0s
601.49-1.35
78.43
108,808,50 (~100M)
65.74
71.95=0.75
72.33=0.0
58.73+0097
60.54-1.99
78.62
203, 584 (~2000M)
66.31 20
72.60.0.0:
22.57
59.15
60.778 601.7_.1.1
78.71 g1.7111.50
(~300M)
66.77+1..6
73.39=1.82
72.74=1.24
59.33+0..9
61.32-1.84
78.96=1.27
392,352, 912 (~-400M)
66.95 66.93
73.54
73.66:1.15
59.80 09:8021.0
61.81=1.05
79.33
496,317.07 (~500M)
67.22
73.86 os
73.82.0.17
60.1
62,25
79.51
600,281 232 (~600M)
67.49
74.03=0082
74.53=000
60.27
62.44-1.2
79.60=1.21
704,245, (~700M)
74,22
g0.1
60.22:1107
62.76
79.6221.47
808,209 (~800M)
67.80
74.16
75.36
60.09
62.68-1.95
79.55
912.173,712 (~900M)
67.45+a..2
73.77
75.20=1.0
39.8640.79
62.37=1.25
79.20s1.41
00356695.760-1 (~11B)
60.28
73.64.0.76
74.85g0.71
59.62,1.05
62.29
78.97
Table 13: Quantitative analysis of the impact of data size on n TUAB and TUEV.
TUAB (2-class)
TUEV (6-class)
Data Size
Balanced Acc.
AUC-PR
AUROC
Balanced Acc.
Cohen's Kappa
Weighted Fl
Only Pretrained Foundation Models (Muti-
1500H
$2.88 88
2.09
61.74_2.44
50.7614.97
55.82
71.39 11.39-..m
30000
a3.96
61.57+a.04
62.81=1.78
51.14+a.6l
56.1442.00
71.52-2.81
4500H
55.43
62.80
63.59_2.a7
51.533a.44
56.2843.80
71.86
60000
56.79
64.22
65.20
$2.70
56.51
72.01 72.01_2.55
7500H
58.0222.47
65. 45.179
66.57_1.94
53.2619.07
57.4212.03
72.44_1.84
~0000
59.45
66.02.12.5
67.92
53.9212.71
57.86
73.16-2.18
100000
61.32=1.90
66.75 66.75+2.60
68.61 -2.2
$4.30+2.16
58.49+2.50
73.74
~12000HH
61.95 2:222.00
67.41
69.13
54.54
58.77 29.05
74.16 50
Pretrained Fine-tuned Foundation Models (Single-task)
1500H
61 21.422..7
37.60.n.es
69.51
13.
58.36
76.52
30000
62.01
68. 68.44+4..8
70.27=0.97
56.42+1.67
58.44+1.99
76.41=1.4
4500H
62.88 02.83go
69. 69.11.. 10.85
70.98 70.98_1.85
$6.8621.82
58.67
77.03
60000
63.75=1.04
70.02+#n.ca
71.43=0.76
57.69
59,43+1.66
77.42=1.59
7500H
64.28
71.24g0.54
71.90_0.66
58.5611.40
59.97.1-47
77.75_1.55
9000H
64.80 64.802o..s
71.96
72.2120.0m
58.84
60.31
78.26
10500H
=5.3990..2
72.32+H..6
72.45-0.01
59.12+1.22
60.72+1164
78.43-1.04
1200HH
65.67 20.s5
4u.59
72.58
59.34:1.44
60.94:1.75
78.72
Table 13 complements the scaling law analysis by validating its other critical dimension: the im- portance of data volume. With the model size fixed to the 57M-parameter Uni-NTFMaing variant, the table documents how model performance on the TUAB and TUEV tasks evolves as the amount of pre-training data increases from approximately 1,500 12,000 hours. The results show clear and consistent upward trend in performance with more data, for both linear probing and fine-tuning evaluations.
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I T-SNE VISUALIZATION OF LEARNED FEATURE REPRESENTATIONS
To provide a qualitative and intuitive assessment of our model's representation learning abilities, we employed the t-SNE dimensionality reduction technique to visualize the learned feature spaces. Figure 5 shows this analysis on two distinct downstream tasks: the 4-class motor imagery task (BCIC-IV-2a) and the more complex 6-class clinical event detection task (TUEV).
t-SNE Visualization of Learned Feature Representations
(a) Raw PSD Features
(b) EEGNet F Features
(c) Uni-NTFM Features
hand
hand right
hand
night bott feut
rght
both fert
bott feet
BCIC-IV-2a
(d) Raw PSD Features
(6) BEGNet Features
(0 Uni-NTFM Features
apow
gual
good
gpod
pled
pled
pled
syom
att
ant
bsk
bcke
bska
TUBN
Figure 5: t-SNE Visualization of Learned Feature Representations, This figure provides qualitative comparison of feature spaces learned on the BCIC-IV-2a (top row) and TUEV (bottom row) datasets. The columns represent features from different sources: (a. d) Raw Power Spectral Density (PSD) features, which serve as baseline; (b, e) features extracted from trained EEGNet model, repre- senting standard deep learning approach; and (c, f) features from our only pre-trained Uni-NTFM model. Each color corresponds to a distinct class within the respective dataset. The clear forma- tion of well-separated and compact clusters in the rightmost column (c, f) visually demonstrates Uni-NTFM's superior ability to learn discriminative and generalizable neural representations.
As expected, (a, d) exhibit no discernible class structure, with points from all classes aggregated in a single and messy cloud. This establishes the inherent difficulty of separating these classes directly from traditional features. The middle column (b, e) shows the feature space after processing by trained, task-specific HEGNet model. They remain diffuse and suffer from significant overlap at their boundaries. This indicates that while standard deep learning architectures can learn some useful patterns, they struggle to create truly separable representations, especially for the more challenging TUEV dataset.
In contrast, the column (c. f) shows the feature space learned by our pre-trained Uni-NTFM. Even for the challenging 6-class TUEV task, Uni-NTFM effectively disentangles the different clinical event types into distinct regions of the feature space. This visual evidence corroborates quanti- tative results, confirming that the paradigm of Uni-NTFM enables it to learn far more powerful and generalizable representations than standard methods.
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J EFFICIENCY ANALYSIS OF UNI-NTEM AND DENSE BASELINES
Table 14: Efficiency Comparison between Uni-NTFMsmon (MoE) and Dense Baselines. Same-
FLOPS denotes matched inference compute, while Same-Parame denotes matched total capacity.
Model Variant
Architecture
Total Params (Capacity)
Active Params (Inference Cost)
Total GFL.PSs
Inference GFLOPs
Dense-Suin Small (Sune-FLOPs)
Dense Transformer
74 M
74 M
16.08
16.08
Dense-Large (Same-Params)
Dense Transformer
421 M
421 M
93.15
93.15
Uni-NTFM smail (Ours)
Sparse MoF.
427 M
7
100.40
15.87
Note: Active Parcams for MoE assumes Top-2 gating with 16 experts.
To evaluate the effectiveness the proposed MoE architecture, we conducted a controlled compar- ison between Uni-NTFMamad and dense baselines: Dense-Small (matched for inference com- pute, denoted as Same-FLOPss and Dense-Large (matched for total parameter capacity, denoted as Same-Params). As illustrated in Table 14, Uni-NTFMsmall successfully decouples model capacity from computational cost. The model possesses total parameter count of 427 M, comparable to the Dense-Large bascline (421 M). This ensures the model retains high capacity for knowledge encoding. Besides, through sparse activation (Top-2 gating). the active parameters during inference are limited to 74 M, resulting an inference cost of 15.87 GFLOPs. Notably, this is even lower than the Dense-Small baseline (16.08 GFLOPs).THE This structural advantage confirms that Uni-NTFM allows for the scaling of total parameters without the increase in deployment latency.
Table 15: Performance Comparison on Downstream Tasks.
Worklod (2-claws
(3-clas)
BCIC-IV-2a (4-cass)
Acc.
AUC-PR
AUROC
Kappa Weighted
Balanced
Kappu Weighted
Pretratedd Models (Mallt (askr)
Donse Small (Sune Lage (Smm--ammms)
70:04
71.91
Uni NTFM
61.11
67.71
70.26
67.35
67.44
71.31
$2.09
40.44
51.23
Pretraised and Fine nieed Foundation Models (Smgle kash)
Dense Small (Sane ELOPS)
63,97
68.11
70
74,35
73:97
76.57
51.83
60.18
$2.06
Dems--Lage (Same-Paran Paramn-
67.35
73.22
75 06
76.63
75.84
77.02
55.75
42:13
53.61
Umi-NTFM,naa
66. 71.73
88
75.68
76.32
76.80
55.59
42.01
54.65
Table details the performance on downstream tasks. Under the same computational budget, Uni-NTFM consistently outperforms Dense-Small across all metrics. For instance, in the fine- tuning setting for the ADFTD task, Uni-NTFM achieves Balanced Accuracy of 75.68% sur- passing Dense-Small (74.35%) clearly. Similarly, in the BCIC-IV-2a task, Uni-NTFM (54.65%) significantly exceeds Dense-Small (52.06%) in Weighted F1. This demonstrates that the additional inactive parameters in the MoE architecture contribute significantly to representation quality without increasing inference costs. Besides, despite activating only 17% of its parameters, Eni-NTFMsmaad achieves performance highly comparable to the fully activated Dense-Large model. In the challeng- ing BCIC-IV-2a tne-tuning task, Uni-NTFM even surpasses Lense-Large in terms of Weighted Fl (54.65% vs. 53.61%). While there performance gap in some tasks, it acceptable compared to the reduction in inference GFLOPs (15.87 vs. 93.15). These results shows that Uni-NTFM ef- fectively uses the learning capacity of large-scale models while maintaining the inference agility of small-scale models, validating the necessity of the MoE design for EEG foundation model.
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K
IMPACT OF DATA AUGMENTATION
Table 16: Performance Comparison on Workload, ADFTD, and BCIC-IV-2a.
Method
Workinad (2-4lass)
ADFTD (3-cless)
BCIC-IV-2a (4-class)
Baleneed
AUROC
Ralenced Ace. Colice's Keppa
Weighted
Balanced Calien's Kappa Weighted
Medilt (Mullistusss)
Uni-NTF Aug-) NTVMarat (ur) Ang-)
67 26.06
70.10
Prefrained Foundation (Slegle
Moa-at Aup-) Uni-TTFM.... (oy) Augi)
71.73
75.68
76.80
To quantify the contribution of the data augmentation strategy (faug) introduced in Section 2.1, conducted an ablation study across three distinct downstream tasks: Workload, ADFTD, and BCIC-IV-2a. The results are summarized Table 16.
The exclusion of augmentation results in noticeable reduction in feature quality in the Only Pre- trained setting. For instance, on the Workload dataset, the Balanced Accuracy decreases from 64.11% to 62.83%, and on the BCIC-IV-2a dataset, it drops from 52.09% 51.44%. This in- dicates that simulating noise, channel loss, and temporal drift during pre-training is essential for learning representations that are invariant to common EEG signals. In the Fine-tuned setting, the Weighted Fl score on BCIC-IV-2a drops from 54.65% to 54.06%, and the Balanced Accuracy on the Workload task declines by 1.33%. These results domenstrate augmentation enhances general- ization and robustness, and removing data augmentation leads to performance degradation across all datasets and evaluation settings. Notably, on the ADFTD dataset, the model without augmen- tation still achieves a Balanced Accuracy of 75.20% of Balanced Accuracy, which is similer to the 75.68% achieved with augmentation. This suggests that while our data augmentation module (Sang) provides valuable performance boost by enhancing robustness against signal variations, the core abilitics of the model are primarily from the proposed MoE architecture.
The results confirm that the proposed data augmentation strategy effectively improves the model's generalization ability. Besides, the high baseline performance of the unaugmented model serves as strong evidence for the effectiveness of the Uni-NTFM framework.
L ABLATION STUDY OF TOPOLOGGICAL EMBEDDING COMPONENTS
Table 17: Detailed Ablation Study of Topological Embedding Components on TUEV dataset.
Variant
Esbs
Evegion
Eintra
Balanced Acc. (%)
Cohen's 's Kappa (%)
Weighted F1 (%)
Variant A
X
x
X
63.16
64.80
80.19
Variant
/
x
x
63.57
65.12
80.70
Variant
X
x
62.72
64.98
80.35
Variant
X
v
/
63.81
65.63
81.24
V
V
64.05
65.82
81.74
We performed a fine-grained ablation study to decouple the contributions of the three components within our Topological Embedding (TE) module: Region Embedding (Eregion). Intra-region Em- bedding (Esnera), and Global Absolute Embedding (Eube). The results on the TUEV dataset are presented in Table 17.
Variant B with only the global absolute index obtains a Balanced Accuracy of 63.57%, but the im- provement is limited than the baseline Variant A. This suggests that treating EEG electrodes merely as a flat sequence fails to capture the intricate spatial relationships required for effective decod- ing. Notably, employing region-level embeddings alone results performance reduce to 62.72%, which is lower than the baseline. We think that providing only regional information introduces am- biguity, as the model cannot distinguish between different electrodes within the same brain region, leading to a loss of channel-specific resolution. However, when intra-region embeddings intro- duced to resolve the ambiguity of Variant C, performance significantly improves to 63.81%. This highlights the necessity of the hierarchical structure: Freeion provides the macroscopic functional
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context, while Emttre restores the microscopic spatial resolution. This combination validates that structured spatial priors are more effective than arbitrary sequence indices.
Uni-NTFMamal achieves the highest performance across all metrics by integrating all three com- ponents. This demonstrates that Eabs provides a unique global identifier to ensure absolute channel independence, while the Pregion and Eintra injects neuroanatomical topology. The complete TE module establishes a robust neural coordinate system that is essential for representation ability.
Table 18: Stress Test: Robustness to Missing Channels.
Original (k 0)
Randomly Dropped Channels (k)
Dataset
Metric
Method
A:
A:
5
A 7
Balanced Accuracy
Uni-NTFM (wla TE)
Uni NTFM pouli (w/ TE)
55.59
52.39
52.46
55.31
vomes
54.52
50.14
51.86
45.93
(4.0%)
40.40 (11
47.25
BCIC-IV-2a
Cohen's Kappa
Uni-NTFM (w/o TE)
40.87
40.50
37.68
33.93
32.12 680999
Uni-NIPMM,mdl
42.01
41.83
40.46
(1.991
38.61
(2.409)
36.10
(8809)
Weighted Fl
Uni NTFM (wla TF)
53.23
52.81 (0:19)
51.36 (1909)
18.12 (111
44.97 (1308)
Uni-NTPM ymali (w/ TE)
54.65
54.71
53.93
(87291
51.70
(3.99)
49.24 454m0
Balanced Accuracy
Uni-NIPM..oo (w/a TB)
66.85
66.97
66,42 (4.00)
65.10
(1799)
63,77 c3.090
Uni NTFM mooli (w/ TE))
72.02
71.98 (03-5)
71.67
(0.399)
71.10
(6901))
70.18 (1100)
SBED
Cohen's Kappa
Uni-NTFM small (wlo TE)
55.42
55.3 parna
55.12
54.01
614.9
$3.30
(2.1299
Uni-NIPM (w/ TE)
58.59
58.68 c0085))
58.55
58.51
60288)
57.93 63.9950)
Weighted Fl
Uni NTFM mmnli (wla TE)
Uni NmlM (wi TE)
68.98
72.74
72.79
68.87
68.84
72.66
(4079)
72.50
(0249)
cosess
71.93
67.03 (1899)
(3.099)
Note: (w/ TE) denotes the model equipped with TE module, (w/o TE) means without the TE module.
Table 19: Cross- Generalization on SEED.
Training
Inference
Performance Metrics (%)
Method
Montage
Montage
Balanced Accuracy
Cohen's Kappa
Weighted F1
Uni-NTFM smalt (w/ TE)
62 Channels
62 Channels
72.02
58.59
72.74
Uni-NTFManna (w/o TE)
62 Channels
19 Channels
61.45
45.52
62.41
(w/ TE)
62 Channels
19 Channels
65,88
51.16
66.73
To simulate real-world sensor malfunctions or signal interruptions, we randomly masked lt E (1.3,5, 5, 7 channels during the inference phase on both the BCIC-IV-2a (22 channels) and SEED (62 channels) datasets. We compare the performance reduction rate of Uni-NTFFsmaate (w/ TE) against Uni-NTFM Mmrt/M/maa///// TE) to quantify robustness. Bssides, to evaluate the model's ability to generalize across different layouts without retraining, we fine-tuned the model on the high-density SEED dataset (62 channels) but evaluated exclusively on the sparse sub-montage corresponding to the international 10-20 system (19 channels).
In Table 18, the Uni-NTFMsmatte TE) model consistently exhibits slower rate of performance decay compared to the Uni-NTFMemand (w/o TE). For instance, on the BCIC-IV-2a dataset, when 7 channels are dropped (representing 30% information loss for 22-channel setup), the Balanced Accuracy of the baseline plummets by 11.99%. In contrast, the model with TE mitigates this loss to 8.34%, effectively alleviating performance reduction. On the 62-channel SEED dataset, the effect of TE is obvious. Even with 7 channels missing, the Weighted Fl score of Uni-NTFM drops by only 0.81%, while the Uni-NTFMemal((WWi TE) drops by nearly 2%. These results con- firms that TE successfully encodes latent functional topography. Instead of treating channel loss as the sequence loss, the model uses spatial priors (Enttr and Eregion) to infer missing information from neighboring electrodes.
TE)
Table 19 evaluates a realistic transfer scenario, where a model trained on a high-density re- search montage is deployed on sparse standard clinical montage without re-training. The Uni- NTFMemat(W/o TE) suffers a dramatic performance reduction, with Cohen's is Kappa dropping to 45.52%. This indicates that standard positional encodings fail to adapt to the spatial change caused by the different montage. By explicitly injecting topological identity, Uni-NTFM,maltte tains a robust Cohen's Kappa of 51.16% and a Balanced Accuracy of 65.88%. This result strongly supports our claim that TE effectively unifies diverse montages by anchoring them to a common neural coordinate system.
TE) main-
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M COMPARISON WITH STFT BASELINE ON WORKLOAD AND TUEV
Table 20: Comparison with STFT Baseline.
Workload (2-elass)
TUEV (6-class)
Method
Inpul Design
Infer: GFLOPS
Ral. Acc.
AUC-PR
AUROC
Bal. Acc.
Kappa
W-FI
STFT-MoB (Baseline)
SIFT (Mag. Phase)
16.14
62.32
68.39
68.81
52.37
$4.26
76.92
Uni-NTEMemod (Ours) Decoupled (Time Freq)
15.87
66.72
71.73
73,88
66.94
67.96
83.25
Note: The STFT-MoF baseline feeds concatenated STFT magnitude and phase spectrograms into the MoF: Transformer. Results correspond the fine-tuned setting. Bal. Acc.: Balanced Accuracy; Kappa: Cohen's 's Kappa; W-FI: Weighted FI.
To rigorously validate the necessity of our proposed multi-stream architecture (HFPM) and dual- domain fusion (DCM), implemented STFT-MoE the baseline. This baseline feeds stacked STFT magnitude and phase spectrograms into the same MoE backbone used by Uni-NTFM, with patch size and embedding dimensions adjusted to match inference GFLOPs (#=16 GFLOPs).
As shown in Table 20, while maintaining a lower computational cost, Uni-NTFM small significantly outperforms the STFT-MoE baseline. On the TUEV dataset, Uni-NTFM achieves a Balanced Ac- curacy of 66,94% surpassing the STFT-MoE baseline by a remarkable 14.57%. Similarly, Cohen's ' Kappa improves by 13.7%. On the Workload dataset, our model maintains superior performance, improving Balanced Accuracy by 4.4% and AUROC by 5.07% compared to the STFT-MoE. The results means that our HFPM can preserve the high temporal resolution of waveform morphology of time-domain, and capture pectral rhythms of frequency-domain Furthermore, the STFT-MoE baseline relies on irregular fusion, while our DCM enables context-aware fusion. This allows the model to demonstrate the superior robustness across diverse tasks.
N LABEL EFFICIENCY ANALYSIS
Table 21: Label Efficiency Analysis.
Labeled Data
Dataset
Metric
10%
30%
50%
%
Balanced Acc.
67.04
68.85
70.97
71.60
72.02
Cohen's Kappa
52.38
56.23
57.63
58.28
58.59
SEED (3-class)
Weighted
67.40
69.90
71.77
72.53
72.74
Balanced Aec.
61.34
64.21
65.69
66.31
66.72
AUC-PR
64.52
67.66
70.65
71.26
71.73
Workload (2-class)
AUROC
66.19
70.35
72.47
73.30
73.88
To evaluate the label efficiency of Uni-NTFM, we conducted fine-tuning experiments on the SEED and Workload datasets using varying fractions of labeled data, ranging from 1% to 70%. The results presented in Table 21 demonstrate that our model significantly reduces the dependency scale annotations. The model exhibits remarkable robustness in extreme low-resource settings. On the Workload dataset, with only 10% of the labeled data, Uni-NTFM achieves a Balanced Accuracy of 64.21% %. When compared to the performance at 70% data (66.72%), the model retains approxi- mately 92% of its peak performance using merely a portion of the training samples. This suggests that the pre-trained backbone has already learned highly discriminative representations, requiring very few examples to align with the downstream task. These results validate that the pre-training of Uni-NTFM successfully instills universal neural priors, enabling the model to generalize effectively even when labeled data is limited. This characteristic is particularly valuable for BCI applications where data labeling is expensive and cime-consuming.
large-
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Published as a conference paper at ICLR 2026
O
VISUAL ANALYSIS OF MoE
Expart Pacgeccy uceu
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Pe Caporry
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Tranfernn
(b) Expert Selection Diversity across Layers
(a) Expert Usage Frequency across Layers
Brain Region Export Affinily Molrix
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(c) Brain Region- Expert Affinity Matrix
Figure 6: Visual analysis of MoE on the Workload dataset.
To validate the functional specialization of our Mixture-of Hixture-of-Experts (MoE) module and address con- cerns regarding expert redundancy, conducted diagnostic analyses three diverse downstream datasets: Workload, TUEV, and BCIC-IV-2a, and the results are shown in Fig 6, Fig 7, and 8. The visualizations reveal highly consistent and interpretable patterns of expert behavior across all tasks. In detail, Expert Usage Frequency heatmap (subfigures a) displays the activation frequency of each expert across different Transformer layers. A darker color indicates higher usage, revealing whether specific experts dominate processing at certain depths. Furthermore, Expert Selection Diversity curve (subfigures b) tracks the Shannon Entropy of the expert gating distribution layer- by-layer. High entropy signifies broad collaboration, while low entropy indicates concentrated and specialized processing. Moreover, Brain Region Expert Affinity Matrix (subfigures c) quantifies the routing probability of signals from specific brain regions to specific experts. It serves as direct evidence of whether experts specialize in processing spatially distinct neural patterns.
Across all three datasets, deep layers of the Expert Usage Frequency heatmaps (subfigures a) con- sistently show concentrated activation of specific experts. Some experts are heavily activated (dark red), while others remain inactive, which indicates that the router has learned to selectively assigned tokens to specific experts based on their features, rather than randomly distributing the load.
In Expert Selection Diversity curve (subfigures b), high entropy in shallow layers demonstrates that experts collaborate to extract features, and the reduction entropy in middle layers signifies a transition towards specialized processing, where specific experts responsible for distinct signal
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Published as a conference paper at ICLR 2026
Expar Progeay acess Legirs
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(b) Expert Selection Diversity across Layers
(a) Expert Usage Frequency across Layers
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Export Affinity Matrix
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Central
8.2
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Porietal
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K.9
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Figure 7: Visual analysis of MoE on the TUEV dataset.
components. Besides, the Brain Region-Exper Affinity matrix (subfigures c) confirm that the MoE effectively uses the spatial priors injected by our Topological Embedding module, and routes signals from different functional brain areas experts forspecialized processing. These results confirm that Uni-NTFM's experts are functionally specialized modules that dynamically adapt to both the hicrarchical depth of the network and the task-relevant spatial topology of the brain.
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Published as a conference paper at ICLR 2026
Depart Usage Proquccy acoss Layera
Sdlatioo
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(b) Expert Selection Diversity across Layers
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Brain Region Expent Affinity Matrix
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(c) Brain Region- Expert Affinity Matrix
Figure Visual analysis of MoE on the BCIC-IV-2a dataset.
33


Reviews:
[{"review_id": "yumGT436TG", "reviewer": "Reviewer_BGmc", "summary": "This paper introduces Uni-NTFM, a foundation model for EEG that addresses domain-specific challenges through a decoupled time-frequency architecture, topological electrode embeddings across different standards, and a Mixture-of-Experts Transformer, pretrained on 28,000+ hours of diverse EEG data. The model demonstrates \"superior performance\" over existing task-specific methods and foundation models across nine downstream tasks under both linear probing and fine-tuning settings.", "questions": "- Given that you have already provided different model sizes, would it be possible to create a performance scaling plot versus the number of parameters?\n\nDo we really need billions of parameters for the current size of EEG data?\n\n- Would it be better to plot the ablation tables for a clearer and faster understanding?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The decoupled time-frequency architecture, topological electrode embeddings, and MoE-based routing represent thoughtfu\n\n- Extensive experiments across nine diverse downstream tasks under both linear probing and fine-tuning.\n\nOutstanding presentation featuring clear writing and accurate usage of technical terminology, effectively applying concepts such as foundation models, zero-shot learning, and fine-tuning, which are often misused in the community.\n\n- The 1.9B parameter scale, 28,000+ hours of diverse pretraining data, and superior performance establish a meaningful benchmark for EEG foundation models with potential clinical and neuroscience applications.\n\nAll necessary code, data, and information are provided to ensure reproducibility.", "weaknesses": "- The ablation study is conducted on a near-saturation task [1](e.g., TUAB), where the baseline model shows low performance; even a lightweight model such as EEGNet (~2k parameters) could outperform it (see [2]).\n\n- The reported improvement over existing methods is marginal, limiting the practical significance of the contribution.\n\nThe novelty of this approach is limited, as it appears to be a combination of existing methods [3,4].\n\n[1] Kiessner, A. K., Schirrmeister, R. T., Boedecker, J., & Ball, T. (2024). Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification. Computers in Biology and Medicine, 178, 108681.\n\n[2] Darvishi-Bayazi, M. J., Ghaemi, M. S., Lesort, T., Arefin, M. R., Faubert, J., & Rish, I. (2024). Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning. Computers in biology and medicine, 169, 107893.\n\n[3] Zhang, X., Zhao, Z., Tsiligkaridis, T., & Zitnik, M. (2022). Self-supervised contrastive pre-training for time series via time-frequency consistency. Advances in neural information processing systems, 35, 3988-4003.\n\n[4] Kuruppu, G., Wagh, N., & Varatharajah, Y. (2025). Eeg foundation models: A critical review of current progress and future directions. arXiv preprint arXiv:2507.11783.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZxRJkcSb6H", "reviewer": "Reviewer_zMGJ", "summary": "This paper presents Uni-NTFM, a large-scale EEG foundation model designed to unify temporal, spectral, and spatial representations of brain signals. The model incorporates three key innovations: (1) a Heterogeneous Feature Projection Module that decouples time-, frequency-, and raw-signal representations; (2) a Topological Embedding mechanism that encodes spatial priors across different electrode standards; and (3) a Mixture-of-Experts (MoE) Transformer that scales efficiently and captures heterogeneous EEG patterns. Uni-NTFM achieves state-of-the-art performance across nine downstream tasks (clinical, cognitive, and BCI datasets) under both linear probing and fine-tuning.", "questions": "1. How would performance change without EEG data augmentation?\n2. Could you provide ablations on the different position embedding components (channel-wise, region-wise, relative index within region) combination?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-organized and clearly written, making it easy to follow and understand.\n2. The experimental evaluation is comprehensive, with systematic ablations of each module and thorough benchmarking against strong baselines under both linear-probing and full fine-tuning settings.\n3. Overall the model achieved better performance compared with other baselines.", "weaknesses": "1. The paper claims “zero-shot transfer” performance, but the evaluation setup corresponds to linear probing, where a supervised linear classifier is trained on the downstream data with the pretrained backbone frozen. While this setup measures representation quality and transferability, it does not constitute true zero-shot inference. The terminology should be clarified to avoid confusion.\n2. The paper claims that the proposed MoE design enables efficient scaling of model capacity. However, this claim is not well substantiated by the current ablation study (if I understand correctly, the current ablation simply removes the MoE module). I would recommend replacing the MoE with a comparably sized dense Transformer and conducting a matched-compute comparison to more convincingly demonstrate the efficiency benefit. Since efficient scaling is listed as one of the main contributions, a more detailed analysis or ablation is necessary to support this claim.\n3. As mentioned above, it would be more informative to include the parameter counts of both the proposed model and the baselines in Tables 1, 2, and 3. This would make it easier to assess model efficiency and fairly support the efficiency-related claims.\n4. From an architectural perspective, although the current design and combination of each module reflects thoughtful consideration, the novelty of the proposed framework appears limited. The overall design builds upon standard Transformer and MoE backbones, and the introduced modules (e.g., heterogeneous feature projection, topological embedding, domain calibration) largely combine existing ideas from temporal–spectral decomposition and spatial encoding. While the integration is well executed and practically useful, it represents more of a system-level consolidation rather than a fundamentally new architectural contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Hhf1hQhGJ7", "reviewer": "Reviewer_YF8s", "summary": "This work proposes Uni-NTFM, which is a neuro-inspired foundation model on EEG brain signal data, which separates and then fuses time-signal, frequency-signal and raw-signal views, and then injects explicit spatial topology, which makes the electrodes from different montages become unified and then tries to scale capacity with a MoE Transformer. Uni-NTFM is a pretrained model with a dual-domain masked reconstruction objective, which is the combination of reconstructing time and frequency targets on around 28k hours of EEG signals. Across 9 downstream datasets, this model beats task-specific models and prior EEG foundation models under both probing and fine-tuning.", "questions": "I'd suggest that the authors engage with the points that I raised in the Weaknesses section. As to the scaling part, I'd suggest downstream accuracy vs pretraining hours (e.g., 10%, 25%, 50%, 100%) plots and vs labeled fraction used in fine-tuning (1%, 5%, 10%, 100%) on a few tasks, so there would be a possibility to see if benefits come from data, model size or both and how label-efficient Uni-NTFM really is. \n\n---\nI have another question and would be curious to know the authors' comments. Do you really need 3 separate streams, or would a single well-designed stream be enough? I was thinking maybe adding an equal-compute baseline that feeds stacked Short-Time Fourier Transform (STFT) patches (magnitude + phase) into one Transformer with MoE, plus early-fusion and late-fusion variants, and compare head-to-head with Heterogeneous Feature Projection Module (HFPM) + Dual-domain Cross-attention Module (DCM). Also, I'd suggest reporting matched parameters/FLOPs and an identical training budget for a fair test.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I found the originality of this work very good, which rethinks Foundation models on EEG around the physics of the signal and tries to decouple time, frequency and raw views cleanly, and they're also explicitly unifying electrode montages with a hierarchical topological embedding, and also scaling with a sparsely-activated MoE, instead of importing a single-stream and dense transformer. \nAs to the quality, I found the proposed method interesting and strong, as the pretraining corpus is large and heterogeneous, and the evaluation spans 9 downstream tasks with both linear probing and fine-tuning. \nThe clarity of the work is also easy to follow, and the significance is notable.", "weaknesses": "While I found the proposed method strong and interesting, I'd like to raise some weaknesses that came to my mind after reading the work. From my understanding, the authors already claimed the montage unification, but I don't see any direct test on unseen layouts or on systematically missing channels, so at this point, I'd suggest considering a small cross-montage transfer and k-missing-channel stress test, which could make the proposed topology more concrete. \n\n---\nAnother point that I'd like to make is about the Mixture of Experts (MoE), which I think is well-motivated, but from my understanding, the work does not show that different experts specialize on distinct EEG patterns, so I'd suggest considering some diagnostics, such as gate distributions and entropy by class, per-expert token-routing heatmaps, or maybe an ablation that freezes the most-used experts, which will help in demonstrating real specialization and justifying your MoE claim. \n\n---\nOne additional thing that I'd like to raise is about the scaling part, which I found super interesting, and the authors have done a great job on it. I just suggest making it more explicit by showing how accuracy changes as size grows, normalized by compute and/or data, and try to report training cost and inference speed. \n\n---\nOne last thing that I'd like to raise. I see that the pretraining mixes 9 EEG datasets, then the authors evaluate on datasets from the same families. From my understanding, this can blur whether gains come from true cross-corpus generalization or from seeing very similar data. I'd suggest considering leave-one-corpus-out, somehow similar to leave-one-out cross-validation (LOOCV), but I don't mean LOOCV at this point; I mean removing one family during pretraining and then testing on e.g., TUAB to prove the robustness to unseen corpora, which could rule out easy distribution matches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ABRPIikEu4", "reviewer": "Reviewer_HBFG", "summary": "The authors propose UNI-NTFM which consists of double branch encoding in both frequency- and time-domain, which then merge again with the embedded raw data stream for computing correspondences across natural and spectral features. This is further enhanced by topological embedding where brain region level variations are explicitly modeled. The final MOE / RoPE augmented Transformer performs token mixing. These architectural contributions are evaluated across several representative EEG databases, against both task-specific and foundation baselines, revealing competitive performance of the proposed method.", "questions": "- I like the topological embedding idea. However, analyses and the written information on this invention are somewhat limited. What are the brain area / region that you considered? Depending on the disease type and its characteristic functional connectivity the embedding might vary a lot. Have you tried to differentiate this?\n\n- I appreciate the confidence intervals that show clearly the gain from the proposed method. However, most results don't seem to be statistically significant compared to second best, on both baseline comparison and ablations. How is the confidence computed? Perhaps it's in the paper but I cannot identify it. Can you show the P-values, for instance, the best performance against the second best performance? If this is not significant under like 95% confidence interval, then the authors may have to tone down the performance gain.\n\n- On a similar line, statistical significant tests within different model sizes might reveal that they are not very different. Would this reveal a model / data side scalability?\n\n- Did you use the MU transfer? How do you know the convergence in model size scalability at the billion parameter regime is due to limited data, or suboptimal hyperparameter tuning?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- Extensive experiments with confidence level clearly shown.\n- Well motivated set of modules used as the backbone, proved to be quite effective.\n- Beautiful figures\n- Complete ablation studies and comparisons.", "weaknesses": "- Limited novelty: joint frequency- and time-domain encoding is not new. MoE and RoPE are not new in the EEG foundation model domain either. Topological embedding adds novelty, yet with this alone, model side contribution might be weak.\n\n- Limited performance gains: performance difference across baselines and within model size variations is statistically not significant in most cases. I'd suggest to tone down the significance statement in the introduction and conclusion.\n\n- Model-size scalability is limited. The reason for this in unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)