THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations
Authors: Wenchao Yang
Weidong Yan
Wenkang Liu
Yulan Ma
Yang Li
Venue: NeurIPS 2025 poster ()
Keywords: Electroencephalogram
brain autoregressive modeling
self-supervised learning
brain-computer interfaces
Primary area: neuroscience_and_cognitive_science
TL;DR: THD-BAR introduces a brain topology hierarchy and a novel "next-scale-time prediction" autoregressive strategy to improve EEG generic representation learning.

Abstract: Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as a "next-scale-time prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning 5 distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities of our proposed approach.

THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations
Wenchao Yang
Weidong Yan
Wenkang Liu
Yulan Ma
Yang Li3.4,
"School of Automation Science and Electrical Engineering, Beihang University the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University 37 Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University) Advanced Institute of Information Technology, Peking University
Abstract
Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi- channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as "next-scale-timg prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design Autoencoder (THVQ- (TE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for pre- diction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities our proposed approach. Our code available at httpp:///ggtt /ttggttt....... com/ Hadbbar/THD-BRR
Heoology-Hicrarhhinn Vector Quantized- Variiztiiiiiiii
Introduction
Eleetrreencephalograaly (EEG) is a fundamental tool in Brain-Computer Interfaces (BCIs) due to its non-invasive nature, relatively low cost, and high temporal resolution [I]. EEG-based BCI tech- nologies demonstrate significant potential across diverse applications, including emotion recognition, motor imagery classification, mental workload assessment, sleep stage classification, and epilepsy detection. However, developing large-scale models that can effectively generalize across different tasks and datasets remains a sigmificant challenge.
"Teee authors contributed equally. Conresponding author. Email: Liyangobuaa.ede edu. cn
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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The
him
Autoregressive Transformer (GPT. LLaMa, etc.)
(a) generation by next-token prediction
Fine
Aulorogresive Transfomner (NeumoL.M,
Brain Autoregressive Transformer (Our BAR)
(b) AR: generation by next-time prediction
BAR: ERG genemtion next-scale-time prediction
Figure I: Conceptual comparison of autoregressive prediction strategies.
Recent advancements in artificial intelligence have opened new opportunities for EEG analysis. Specifically, motivated by the transformative success of large-scale pre-training techniques in natural language processing (e.g., autoregressive (AR) frameworks like GPT (2) and computer vision (e.g.. MAE 3. the field is increasingly adopting similar strategies for EEG analysis. Initial efforts focused on tackling superficial data heterogeneity, such inconsistent electrode configurations and sampling rates. Models such as MMM 44, LaBraM [5], and NeuroLM 66] among others 77H33 employed techniques like masked autoencoding, advanced tokenization, and AR architectures to improve robustness to these variations. While these advancements, particularly the adoption of AR frameworks 6 g 9) have improved robustness superficial data heterogeneity, they encounter more fundamental challenge: defining a sequence order for EEG that truly reflects brain dynamics. Current AR models typically emulate language processing by arranging multi-channel EEG data temporally and performing "next-time prediction" (Figure (b)). However, this temporal-centrie ordering struggles to adequately capture the inherent variability of brain activity. Neural activation patterns and their corresponding spatial topology on the scalp dynamically shift depending on the underlying cognitive task (e.g.. processing visual stimuli versus experiencing emotions) [14. This dependepnddnn spatial variability represents a deeper layer of heterogeneity that a purely temporal sequence fails to effectively address. Consequently, even sophisticated AR models are limited in their ability to model fundamental shifts in spatial brain dynamics, hindering the development of truly universal and generalizable EEG representations.
Given these limitations of current AR approaches, which struggle to model dynamic spatial topologies due to their predominantly temporal order assumption, fundamental rethinking necessary. This raises a crucial question: How can we define the spatial order for EEG signals? Inspired by hierarchical information processing human perception and vision modeling (e.g.. VAR [5)), we propose novel concept called the Brain Topology Hierarchy (BTH), which establishes nested "whole brain brain region channel" relationship grounded in physiological structure, thereby defining our proposed spatial order. Based this hierarchy, we redefine AR learning for EEG as "next-scale-time prediction" (C), and design novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) framework, facilitating the simultaneous modeling of dependencies across both spatial scales and temporal sequences. The THD-BAR framework realized through several key components working in synergy: First, the BTH itself provides the foundational multi-scale mapping hierarchy, structuring the spatial context for EEG analysis. Second, to effectively capture the deep hierarchical features of EEG signals, we introduce the Topology- Hierarchical Vector Quantized- Variatioal Autoencoder (THVQ- VAE), which tokenizes EEG signals into discrete hierarchical representations by incorporating modified multi-scale quantization layer into the standard VQ-VAE. Third, these multi-scale tokens generated by THVQ-VA VAE are then flattened according defined spatio-temporal order (hierarchically in space, then sequentially in time), preparing them for the autoregressive model. Fourth, the "next-scale-ting prediction" strategy is performed by Brain Autoregressive (BAR) module, which is enhanced AR architecture integrating both scale-wise and time-wise masking. To evaluate the effectiveness of THD-BAR, we pre-train large-scale dataset comprising 17 EEG tasks to learn universal representations. We then assess its generalization by fine-tuning on 10 downstream datasets covering major EEG applications. Experimental results show that our proposed method significantly outperforms existing approaches across diverse EEG tasks. Our contributions are listed below:
We propose THD-BAR framework, generic foundation model for EEG generic representation learning. Pre-trained on diverse datasets, captures complex spatio-temporal dynamies, yielding significant performance various downstream tasks over existing methods.

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We introduce the BTH, which establishes "whole brain brain region channel" relationship grounded physiological structure. Building on this hierarchy, we develop the THVQ-VAR VAE to generate discrete, multi-scale quantized tokens.
A nested "next-scale-time prediction" strategy is employed for pre-training. This strategy compels the BAR module to learn complex spatio-temporal dependencies by predicting tokens hierarchically across scales within each time step before progressing to the next time step, thus modeling both intra-time hierarchical relationships and inter-time dynamics.
2 Method
In this section, we detail the comprchensive framework of THD-BAR. We begin by constructing BTH based on the location of channels and the function of brain regions. Following this, the model development involves three key stages (as depicted in Figure : (1) training an THVQ-VAE as a specialized neural tokenizer generate tokens; (2) pre-training our BAR model using these tokens with a novel autoregressive strategy; (3) fine-tuning the pre-trained BAR model on diverse downstream EEG tasks.
THVQ-VAE
Neural
Neural
Tokenizer
Brain Autoregression
Tokenizer
Label
Encoder
EEG Encoder
EEG Encoder
Brain
EEG
THVO
VAE
THVQ
TIVO
Stage.1 1 Neural Tokenizer Training
Stage. BAR Pretraining
Stage. Fine-tuning
Figure 2: The stree-stage pipeline THD-BAR framework. (1) An THVQ- VAF tokenizes unlaheled ERG
data. (2) The BAR model undergoes autoregressive pre-training using these tokens. (3) The pre-trained BAR model fine-tuned for specific downstream tasks with labeled data.
2.1 Brain Topology Hierarchy
Bleetroencephalograahyy (EEG) signals inherently capture brain activity across multiple spatial scales. However, the non-uniform distribution of EEG electrodes poses a challenge in defining a structured hierarchy for analysis. To address this, propose the Brain Topology Hierarchy (BTH), a framework that imposes a multi-scale spatial order on EEG channels, enabling a more nuanced modeling of brain activity. The BTH is conceptually constructed by referencing standard electrode placement systems (e.g., the 10-10 system) and grouping channels based on principles such as spatial proximity and their association with underlying brain regions 16). This process establishes series of progressively finer topological scales, ranging from a holistic representation of the entire brain scalp the coarsest level, through intermediate groupings corresponding to broad brain regions, down to near-individual channel resolution at its most granular stages [14]. An exemplary implementation, utilized in our experiments, employs specific number of distinct scales (e.g.. five scales, $1 through S5, progressing from whole-brain to individual channels). A detailed description of the specific five-scale configuration used in our work, including the rationale for channel groupings each level, is provided in Appendix D Within this hierarchical structure, denote the channel grouping at specific scale s as ch,, Specifically, chş refers to the finest scale, representing the original individual channel resolution. By establishing this BTH, we provide structured framework for analyzing the spatial distribution of brain activity. This hicrarchical organization is crucial for our model to learn dependencies and representations not just across time, but also across different physiologically relevant spatial scales, from global patterns to fine- aeeeggraied local activity.
2.2 Neural Tokenizer Training
To transform continuous EEG signals into a sequence of discrete, hierarchically structured tokens, we train our proposed THVQ- VAE as a specialized neural tokenizer. The training process begins with preparing the input EEG data: Given arbitrary EEG signals EEG E NN,, where N represents the number of channels and I. denotes the length of the signal, we consider an EEG sample as EEG cample B NW where W is the window size. This leads to a total of [W]
& samples for each
3

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Qutee Pack
Quea Pop
IN
Devn
000000
Sharal Codchook
Losksp
Repkae
EBG signals
VAE Tomeseal
Tempori
AF9
EBG Ensoder
000000
AF7
Davk Scal
Fregientt
FPI
mDG
Whole brain
Brain regton
VAL Ereourt Dhookkr
Channel
EEG Decoding
Topology Hicrarchical Vector Quantization
VAF-Docoding
Figure 3: Topology- Hichrgcccaaa Vector Quantized-Variin Variational Autoencoder (THVQ- Q--V)))..
signal segment. Then, we segment the EEG samples along temporal domain into non-overlapping patches eeg E RNTxxP where P represents the patch size, and 'T' is the number of patches, calculated as T T= -.
Preliminary: Vector Quantized- Vaaritttiin Autoencoder. A vector guantized-variation autoen- coder (VQ-VAB), described in works such (5 61 is commonly applied encode EEG signal eeg E NxxxxP into EEG feature maps / E R x XC where C' the dimension the learned feature representation for each patch. These feature maps f are then quantized into discrete tokens q E [V]NTT, dequantized discrete tokens q back into quantized feature maps f E NxTX and finally decode the quantized feature maps ff into reconstructed EEG signal eêg E RR NXTXP, The complete process is as follows:
= E(eeg); g 9= DDDQ(Z,,), eēg = D(}),
where E() denotes encoder, Q(-) quantizer, DQ(-) dequantizer and D(.) decoder. Both the quantizer and dequantizer typically share a learnable codebook Z E VxC which contains V code vectors. The quantization process = 2(f) will map each feature vector f(n.t) to the code index g( of its nearest code in the Fuclidean sense, and dequantization process f = (2)
will lookup quantization feature vector f of the code index g(n..) from codebook Z:
((n.)) arg min [lookup(Z, "E[V]
f(rn)) lookup (( (n.0)),
(2)
where lookup(Z, u) refers to selecting the v-th vector from codebook Z. Following the approach in [6], we predict both the original signals and the frequency magnitude of EEG signals. The Discrete Fourier Transformer (DFT) is applied to transformer each EEG patch eeg(t.t) into their corresponding frequency patch fre( using Euler's 's formula. A frequency decoder is employed to decode reconstructed frequency patch fre (n.f) = D(J). Additionally, as in [6]. we introduce a domain classifier C to predict whether the embeddings from EEG or text. Finally, a compound loss L is minimized:
L -=leeg- eégl2 +| - + > di log C(f).
11
where d; denotes the label from either the EEG or text domain, while scaling factor that smoothly transitions from 0 to I as the step progresses.
10step/Stepp is
Hierarchhcl Vector Quantized-Vannt Variational Autoencoder. As shown in Figure design a THVQ- VAE to encode EEG into multi-scale discrete token maps R = (ri, "r.,,2....,7 rs). which are essential for our BAR. Building upon (6), our architecture incorporates modified multi- scale vector quantization and dequantization. These modifications leverage downscale operations to aggregate features from finer coarser scales during quantization, and upscale operations distribute features from coarser to finer scales during dequantization. These multi-scale procedures, which include residual designs on ff or f with S extra convolution layers { applied to the upscaled quantized feature vectors =, (at the finest scale ch.)) for refinement, are detailed in Algorithm and A A shared codebook Z is employed across all scales to ensure that each r. 's tokens belong to the same vocabulary VV. Once fully trained, the autoencoder (E, 2, DQ, D) is frozen to tokenize EEG data for subsequent unidirectional autoregressive model training.

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Reconstructed EEG tokens
Time-wise causal mask
m-
Tx
BAR: Brain Autoregression
(Causal Transformer)
mm
mmi
S
(m=1
Scale-wise causal mask
EEG signals
Neural Tokenizer
trainable
frozen
EEG Encoder
THVO
mask
unmask
next time
next scale
871
prodiction
prediction
Tp. S,t Sy....
Figure 4: A BAR causal transformer is trained via "next-scale time prediction".
Algorithm THVVQ-VA VAE Encoding and Quantization
Algorithm 2: THVQ-VAL VAE Dequantization Decoding
1: Inputs: EEG signal eeg R x
1: Inputs: multi-scale token maps R;
2: Hyperparameters: scales S, multi-scale channel
Hyperparameters: scales S, multi scall channel hierarchy (ch,)
hierarchy (ch.. )²_1
3: f Eleeg) E NXXXX R
f = 0: for 1, ..... S do r₂ queue :oecue
4: reshape(f. (N T,
for = 1,...,
6: Fa O(downscale, chs, cha));
6:
DQ(2,ro);
7: R queue push R?, rx);
7:
uppccal(E2,, chs,
8:
2, lookup(Z,, rs):
8: = f 6.(2);
2, upscalo(2,, cha, chs);
9: end for
10: f == f - (z.)):
10: = reshope
11: end for;
11: eêg D(f);
12: return multi-scale tokens R;
12: return reconstructed EEG signals eěg;
2.3 Brain Autoregressive Pre-training
Preliminary: Autoregressive Modeling via Next-Time Prediction. Consider discrete token maps q' = (g = 1, 2, ...,.. N) of the VQ-VAF, where each (m,)) E [V] is an integer from a vocabulary of size The "next-time" autoregressive model assumes that the probability of observing the current
token g¹ depends on its prefix q"" ..."""."'* -1.). This unidirectional token dependency enables the factorization of the sequence likelihood of g:
P(g q²
II P(g' !".."".....q"" t=1
(4)
The "next-time prediction" means training the autoregressive model Pa through optimizing P( .42 ....."--11 over datasets. During the implementation, define time-wise mask that allows each EEG token to attend to tokens of all channels at the previous 1 (L (6-1) 1) times.
Prediction. Consider multi-scale discrete token maps q = (r' [s = 1, 2, ..., S}. where each element of r is an integer from a vocabulary of size V. Our BAR module,
Autoregressive Modeling via Next-Scale-Tiin
shown in Figure employs a "next-scale-time prediction" strategy and assumes that the probability of observing the current token r depends on its prefix
q'...gg-) and (rf, ri r..,... r 1) This causal token dependency enables the factorization of the sequence likelihood of q:
Plgt.g2..... .".....g)) IT -T. P(rf 4l.......
(5)
(~((-1) 1) times
(~-a-1) scales
The "next-scale-time prediction" means training the autoregressive model Po through optimizing P(r qt ...,...'-1, --rr. rf ...,... ...,!__) over datasets. During the implementation, we define scale-time-
wise mask that allows each EEG token attend to tokens all scales the previous (1 1)
times and tokens of 1 1) scales at current t time.
5

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2.4 Multi-task Instruction Fine-tuning
We employ joint multi-task instruction fine-tuning to handle various EEG downstream datasets, adopting a fine-tuning strategy similar to that used in [6]. The specific design of instructions for each dataset is detailed in Appendix EEG and text data are concatenated using special token, [SEP], to distinguish between the two modalities. The loss is computed based on the answer portion of the text, which corresponds to the classification result. Let prom represent the instruction prompt and 1 the answer to the instruction. Let I. denote the sequence length of tt. This procedure can be expressed as follows:
p(t (prom)
II p(t prom, t <..
i=1 where ee represents the answer tokens that occur before the current prediction token E
3 Datasets and Implementation Details
3.1
Datasets
The THD-BAR framework is evaluated through a two-stage process: pre-training and subsequent fine-tuning on downstream tasks. For pre-training our THVQQVAE tokenizer and BAR module, we
utilize a comprehensive corpus 17 diverse EEG datasets, which are detailed in Appendix B To comprehensively assess the generalization capabilities of the pre-trained THD-BAR framework, we then employ 10 distinct EEG datasets for fine-tuning and evaluation across 5 major downstream tasks, summarized in Table These five downstream task categories are: emotion recognition (DEAP [17], (EED(18), motor imagery recognition [MI BCIC4- 220), mental workload recognition (EEGMat 211, STEW(22)) sleeping stage recognition (EDF 23 HMC and epilepsy recognition
(TUAB 251. TUEV 225)). Further descriptions and processing details for these downstream datasets
available in Appendix C
Table I: Summary of EEG datasets used in downstream tasks.
Task
Dataset
Rate
Subject
Electrode
Sample
Time
Class
Emotion
DEAP
128Hz
32
32
19.2k
60s
Recognition
SEED
1000Hz
62
36.2k
Motor Imagery
MIBCI
512Hz
52
64
10.5k
7s
2
Recognition
BCIC4-1
100Hz
7
38
1.4k
8s
Mental Workload
EEGMat
500Hz
34
19
1.0k
60s
Recognition
STEW
128Hz
45
14
3.3k
4s
Sleeping Stage
EDF
100Hz
78
19.5k
Recognition
HMC
256Hz
151
4
22.6k
30s
Epilepsy
TUAB
256Hz
2383
409.5k
10s
Recognition
TUEV
256Hz
370
112.5k
3.2 Implementation Details
Data Preprocessing, Due to variations in data collection equipment, sampling parameters, and noise interference, we used essential preprocessing steps. A band-pass filter with cutoff frequencies of 0.1 Hz and 75 Hz is applied to remove low and high-frequency noise, while a 50/60 Hz notch filter is employed to eliminate power-line interference. All EEG signals are sampled to 200 Hz, and the interquartile range (IQR) is used for robust scaling to reduce the outlier influence and ensure stable data normalization.
Model Configurations. The implementation of the encoder and decoder in THVQ VAE adopts the vanilla Transformer (26) following LaBraM (5] and MeuroLM [6]. We predict both EEG signals and their frequency magnitude using two identical decoders. BAR module adopts GPT-2 series as large language model, which is compatible with any causal LLM. We have developed three architecture configurations: HHD--AR-Baal THD-BAR-Large, THD-BAR- 1555M parameters, respectively. The patch size P and window size W are set to 200 and 1024. Sequences shorter than 1024 are padded with zeros to reach this length during tokenizer training and autoregressive pre-training, with attention values for the padding masked.
which have 124M 354M,

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Table 2: Comparative performance (halanced accuracy %) and model parameters emotion recognition and motor imagery tasks. "General Model?" indicates if the model is pre-trained for general representations, and "Multi-Tase Task?" indicates it's designed for or evaluated on multiple tasks. Best results are bold.
General
Multi-
Model
Emotion
Motor Imagery
Methods
Year
Model?
Task?
Parameter
DEAP
SEED
MIBC1 BCIC4-1
EEGNet 27
2018
X
x
35.2=9.4
69.3+2.1
63.3-7.2
11.9
TSception 28
2020
x
34.3+8.1
68.6+1.3
61.4+6.5
52.2+1.6
LGGNee
2024
x
33.5+8.5
6..5+114
56.7+3.7
50.0+0.4
BIOT 7
2023
X
3.2M
35.2.18.9
71.0.1.2
53.2.12.0
51.110..5
LaBraM 5
2024
v
X
5.8M
34.319.9
73.210.2
50.51111
50.310.4
EEGPT 3
2024
/
1.46M
41.4=2.7
62.2#2.88
56,9#1.6
NeuroLM 6
2024
V
254M
40.1=114
70.2+..3
62.1+2.6
17.1
HHID-BAR-BAR
2025
/
124M
42,3=1.2
73.5+0.3
62.9
57,5
THDD-BRR-Large
2025
354M
43,611.6
73.4104
63.311.7
$8.111.2
THD-BAR-Huge 2025
1555M
43.9117
73.9:0.3
63.621.88
58,9
Table Comparative Performance (balanced accuracy %) on Mental Workload. Sleep Staging, and Epilepsy Detection Tasks. Best results are in bold.
General
Multi-
Mental Workloud
Sleeping Stage
Epilepsy
Methods
Model?
Task?
EEGMat
STEW
EDF
HMC
TUAB
TUEV
EBGNet
60.028.7
52.3:17.66
84.01444
54.548.7
76.3+155
53.5100.2
TSception 28
50.3+1.2
6388+330
68.644555
36.4+988
74.344422
51.3400.4
LGGNet 29
x
10.2+1.1
46.7+:22.5
68.644.5
17.049.5
75.5+3.1
$2.8:0.3
50.2+1.11
51.3++199
79.6++0.6
$2.80033
LaBraM S
V
1.34
52.5:12.4
69.343..8
68.11:0.7
81.41:022
64.110.7
EEGIT g
/
66.0+%.6
63.24+10.6
85.2433.4
65.5++4.0
MeuroLM
65.717..
59.315.8
85.3433.7
67.44:5.6
78.3+00.5
45.61:06
THD-BAR-Base
66.546.2
62.1
85,544.66
68.0+13.5
81.910.4
64.310.2
THD-BAL Large
66.417.1
62.47.55
85.7+4.77
68.0433.2
82.0:10.3
64.90033
THD-BAR-Huge
67.12588
62.927.9
85.7114.
68.414..3
82.210.4
65.3 =0.5
Training Settings. All experiments are conducted using Python 3.12.9, Pytorch 2.5.0, and CUDA 12.2 on a system equipped with 8 NVIDIA L40s-48G GPUs. Further experimental configuration details are available in Appendix
Experimental Results
4.1 Comparative Study
We conducted comprehensive evaluation experiments for our THD-BAR framework on 10 down- stream EEG datasets, encompassing 5 diverse tasks. As shown in Table Table our proposed THD-BAR models consistently demonstrate superior performance, outperforming both multi-task and single-task baselines across 9 of the 10 evaluated downstream EEG datasets. This highlights THD-BAR as a highly competitive method, often surpassing state-of-the-art approaches across a variety of EEG tasks. Notably, compared to the multi-task baseline NeuroLM (6). our THD-BAR- Base model achieves improved balanced accuracy across all evaluated datasets, especially achieving significant balanced accuracy improvements of 2.2% DEAP and 3.3% on SEED, highlighting the advancements THD-BAN brings to existing AR capabilities. Our models perform slightly worse than EEGPT 8 on the STEW dataset. This can be attributed to EEGPT benefiting from both pre-training and fine-tuning on the STEW dataset, whereas our approach was only fine-tuned on STEW after general pre-training. Overall, the results demonstrate that THD-BAR is highly competitive method. Furthermore, our experiments consistently show that increasing the number of parameters within the THD-BAR architecture (from Base Large Huge) generally leads improved performance.
4.2 Ablation Study
Multi-scale ablation. To validate the necessity of our multi-scale design and identify optimal BTH scale utilization, we conducted an ablation study examining the impact of various scale combinations. We evaluated the performance the THVQ-VAE tokenizer and BAR model across distinct scale configurations (O, to Og). Figure displays the BTH scales (S1 to Ss)

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for each configuration (02 to Og) (center), and the corre- sponding THVQ VAE (PCC, Loss; top) and BAR module (ACC, Loss; bottom) performance, where configuration Os is optimal for both. These results consistently indi- cate that configuration Og. which incorporates all scales from S₁ to S5. yields the optimal performance for both the THVQ-VAE and BAR under the evaluated metrics, Comparing configurations using only single scales (e.g.. O1 using only the coarsest scale S1. or Og using only the finest scale S5) resulted in significantly lower performance than configurations using multiple scales, suggesting that relying on a single granularity level fails to capture the full complexity of EEG signals that multi-scale repre- sentation can provide. This highlights that the integration of information across the full spectrum of defined scales, as achieved in configuration Og, is crucial for achieving optimal performance in both the tokenization and autore- gressive modeling stages.
Results of TAVO
0.6
P00
64
10
0
O
0
0,
0,
0
O
0+
S
8
5
0
0
0
0
00
00
0
Results of BAR
Figure S: Performance comparison of T-VQQ VAE and BAR across different multi scale configurations.
To determine the optimal masking strategy for our "next-scale- time prediction" within the THD-BAR framework, we examined three mask variants, as illustrated in Figure (a-c): (a-c): Scale-wise Mask conceals all tokens from other time steps and finer- scale tokens within the current time step, compelling prediction based only on revealed coarser-scale tokens within that same time step. Time-wise Mask enforces strict temporal causality by concealing tokens from all future time steps, prompting the model primarily learn from tokens in previous time steps. Scale-Time-wee combining scale-wise spatial prediction within the current time step and masking all future time steps. The results, presented in Figure demonstrate that the mask design significantly impacts autoregressive modeling, with the Scale-Time-wist mask yielding the best overall performance. This suggests that our proposed nested mexx-scale--ime prediction' strategy is effective for learning the complex spatio-temporal dependencies inherent in EEG data.
Mask design ablation.
Mask implements our full "next-scale-time prediction" strategy by
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(a) Scale-wise mask
(b) Time wise maakk
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scole time wise
scale-stee -ame- acale
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Figure 6: Mask design ablation study. (a-c) Illustrations of Scale-wise, Time-wise, and Scale-Time-wise masking strategies. (d-e) Pretraining accuracy and loss curves for the three mask variants. (f) Fine-tuning accuracy comparison across various downstream tasks. The Scale- Miile-Tie-werr wise mask consistently yields the best performance.
8

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1.0
0.8
Temporaal domain Lets
VQ-VAE THIQ-VAI
0.8
Frequency domuin
6.8
THQQAVA
100
94
VAB THVQ-VAE
0.2
0.0
20k
40%
60k
20k
40k
60%
20k
4.0k
60k
Stap
Stup
Stap
Neural training
Velldeston perplesity
THD-BAR-Lag
RAR
0.0
200
0.4
IHD-BAll-Ba:: DAR:
0.2
1.00
11.0
60k
Step
Step
Epoch
autoregressive pre pre-tminnin
Figure 7: Training performance comparison. (a) Neural tokenizer training dynamics comparing THVQ- VAE and VAE on PCC, temporal domain loss, and frequency domain loss. (b) Brain autoregressive pre-training performance comparing THD-BAR Bee--BAR-Baaae Base, THD-BAR LAR-Large, and THD--AR--HU models on accuracy, training loss, and validation perplexity.
Original Rac (GroundPmnthh
Original (GroundFrtth)
VQ-VEE Inconnbruction
VO-VAE
THNG-VAL
THNQUVAE
Tex2
la-3
(a) MIBCI Dalaset
(b) SEED Dataset
Figure 8: Qualitative comparison of spatio-temporaa EEG reconstruction quality. Spatial heatmaps depict original ERG (Ground-truth), VQ-VAE reconstructed FEG, and TTHQQVVRK reconstructed EEG across four consecutive time steps (fm-1, frn fm=+1, fm+))
4.3 Visualization Study
In Figure (a). we visualize the PCC, temporal domain loss, and frequency domain loss during neural tokenizer training. Our comparison between THVQ-VAE and VQ-VAE used NeuroLM (6] shows that the PCC improved by over 8%. Additionally, Figure (b) illustrates the accuracy, loss, and validation perplexity brain autoregressive pre- pre-training. Generally, larger models tend to achieve lower loss and perplexity.
To validate the spatio-tempora feature extraction capabilities of our framework. Figure g visualizes THVQ-VAE's ability to capture the dynamic evolution of brain activity patterns across consecutive time steps (fm-1 2 for representative samples from the MIBCI and SEED datasets. THVQ- VAE's reconstructions more faithfully mirror the temporal progression and transformation of spatial topographies compared to baseline VQ-VAE. This superior correspondence in evolving patterns suggests our method extracts more comprehensive spatio-temporal features, effectively representing the underlying dynamic characteristics EEG signals. More visualization results can be found in Appendix G

---

5 Conclusion
This paper proposes THD--BR, a novel autoregressive framework for EEG signals based on "next- scale-time prediction". We first established BTH, rooted in physiological principles. Subsequently, THVQ-VAE encodes EEG signals into BTH-allgnedd multi-scale discrete tokens. THD-BAR leverages sscal--timewiie mask" to facilitate spatio-temporal prediction. After pre-training on 17 datasets and validating on 10 downstream datasets across 5 tasks, our proposed method demonstrated superior performance and generalization. Unlike conventional "next-token prediction" approaches, THD- BAR significantly improves spatial feature modeling while maintaining robust temporal dynamics. This hierarchical framework presents perspectives for BCI research, and the open-source code is intended to support further EEG foundation model development.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62325301, Grant 623B2011, I, and Grant U24B20186; in part the Beijing Natural Science Foundation under Grant Z220017; in part by the National Key Research and Development Program of China under Grant 2023YFC24 16600; in part by Natural Science Foundation Key Project of Zhejiang Province, China under Grant L.Z23F030001; and in part by the Chongqing Municipal Health Commission, China under Grant 2025GGXM005. It also supported by the Academic Excellence Foundation of BUAA for PhD students.
References
[1] Sharlene N Flesher, John E Downey, Jeffrey M Weiss, Christopher L Hughes, Angelica J Herrera, Eliza- beth "Tyler-Kabare Michael Boninger, Jennifer Collinger. and Robert A Gaunt. brain-computer interface that evokes tactile sensations improves robotic arm control. Science, 372(6544)8331-866 2021.
[2] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAl blog. 188)9, 2019.
[3] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision leamners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022.
[4] Ke Yi, Yansen Wang. Kan Ren, and Dongsheng Li. Learning topology agnnosicc eeg representations with aoaae
geometry aware modeling, Advances in Neural Information Processing Systems, 16:53875-53891, 2023.
[5] Wei-Bang Jiang, Li-Ming Zhao, and Bao-Liang Lu. Large brain model for learning generic representations with tremendous eeg data in bei. arXiv preprint arXiv:2405.18760 2024.
Wei- Rei-Bang Jiang, Yansen Wang, Bao-Liang Lu, and Dongsheng Li. Neurolm: A universal multi-tare foundation model for bridging the gap between language and ceg signals. arXiv preprint arXiv:2409.0010, 2024.
[7] Chaoqi Yang. M Westover, and Jimeng Sun. Biot: Biosignal transformer for cross-data learning in the wild. Advances in Neural Information Processing Systems, 36:78240-78260, 2023.
[8] Tongtian Yue, Shuning Xue, Xuange Gao, Yepeng Tang, Longteng Guo, Jie Jiang, and Jing Liu. Eegpt: Unleashing the potential of eeg generalist foundation model by autoregressive pre-training. arXiv preprint arXiv:2410. 19779, 2024.
[9] Guangyu Wang, Wenchao Liu, Yuhong He, Cong Xu, Lin Ma, and Haifeng Li. Eegpt: Pretrained transformer for universal and reliable representation of eeg signals. Advances in Neural Information Processing Systems, 37:39249-3999 392800 2025.
[10] Daoze Zhang, Zhizhang Yuan, Yang Yang, Junru Chen, Jingjin Wang, and Yafeng Li. Brant: Foundation model for intracranial neural signal. Advances in Neural Information Processing Systems, 36:26304-266 263211 2023.
[11] Zhizhang Yuan, Daoze Zhang, Junru Chen, Gefei Gu. and Yang Yang. Brant-2; Foundation model for brain signals. CoRR, 2024.
10

---

[12] Daoze Zhang, Zhizhang Yuan, Junru Chen, Kerui Chen, and Yang Yang. Brant-x: A unified physiological signal alignment framework. In Proceedings of the 30th ACM SIGKDD Conference Knowledge Discovery and Data Mining, pages 4155-4166, 2024.
[13] Christopher Wang. Vighnesh Subramaniam, Adam Uri Yaari, Gabriel Kreiman, Boris Katz, Ignacio Cases, and Andrei Barbu, Brainhert: Self-supervised representation learning for intracranial recordings. arXiv preprint or Xiv: 14367, 2023.
[14] Mengqing Ye, CL. Philip Chen, and Tong Zhang. Hierarchical dynamic graph convolutional network with interpretahility for eeg-based emotion recognition. IEEE transactions on neural networks and learning systems, 2022.
[15] Keyu Tian, Jiang, Zehuan Yuan, Bingyue Peng, and Liwei Wang. Visual autoregressive modeling: Scalable image generation via next-scale prediction. Advances in neural information processing systems, 37:84839-8444 84865, 2025.
[16] Mikail Rubinov and Olaf Spors. Complex network measures of brain connectivity: uses and interpretations. Neuroimage,
2010.
[17] Sander Koelstra, Christian Muhl, Mohammad Soleymani. Jong-Seol Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Anton Nijholt, and Joannis Patras. Deap: A database for emotion analysis; using physiological signals. IEFF transactions affective computinng 3(1):18-31, 2011.
[18] Wei-Long Zheng and Bao-Liang Lu. Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Trunsactions autonomous mental development, 7(3):162-175, 2015.
[19] Hohyun Cho. Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, and Sung Chan Jun. Eeg datasets for motor imagery brain- compute interface. GigaScience, 6(7):gix034, 2017.
[20] Benjamin Blankertz, Guido Dornhege. Matthias Krauledat, Klaus- Robert Müller, and Gabriel Curio. The non-invasive berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neurolmage, 37(2):33--550, 2007.
[21] Igor Zyma, Sergii Tukaev, Ivan Seleznov, Ken Kiyono, Anton Popov, Mariaa Chemnyk,, and Oleksii Shpenkov. Eleetroencephaloggrms during mental arithmetic task performance. Data, 4(1):14, 2019.
[22] Wei Lun Lim, Olga Sourina. and Lipo P Wang. Stew: Simultaneous task eeg workload data set. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(1)):210--2114, 2018
[23] Bob Kemp. Aeilko H Zwinderman, Bert Tuk, Hilbert AC Kamphuisen, and Josetien JL Oberye. Analysis of dleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg. IEEE Transactions on Biomedical Engineering. 47(9): 1111118591944 2000.
[24] Diego Alvarez-Estevez and RM Rijsman. Haaglanden medisch centrum sleep staging database (version 1.0. 1). PhysioNet, 2021.
[25] Amir Harati, Meysam Golmohammadi, Silvia Lopez, lyad Obeid, and Joseph Picone. Improved eog event classification using differential energy. In 2015 IEEE Signal Processing Medicine and Biology Symposium (SPMB). pages 1-4. IEEE, 2015.
[26] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances neural information processing systems. 30, 2017.
[27] Vernon Lawher, Amelia Solon, Nicholas Waytowich, Stephen Gordon, Chou Hung, Brent Lance Eegnet: a compact convolutional neural network for eeg. eagedased brain computer interfaces. Journal of Neural Engineering, 15(5):056013, jul 2018.
[28] Yi Ding, Neethu Robinson, Qiuhao Zeng, Duo Chen, Aung Aung Phyo Wai, Tih-Shih Lec, and Cuntai Guan. Tsception:a deep learning framework for emotion detection using eeg. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1-7, 2020.
[29] Yi Ding, Neethu Robinson, Chengxuan Tong. Qiuhao Zeng, and Cuntai Guan. Lggnet: Learning from local local-global-graphh representations for compuer interface. IEEE Transactions Neural Networks and Learning Systems, 35(7):9733-9786, 2024.
[30] Wei-Long Zheng, Wei Liu, Yifei Lu, Bao-Liang Lu, and Andrzej Cichocki. Emotionmeter; A multimodal framework for recognizing human emotions. IEEE transactions on cybernetics, 1110)1110--112,, 2018.
11

---

[31] Wei Liu. Jin Qiu, Wei-Long Zheng, and Bao-Liang Lu. Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, 14(2)715-729. 2021.
[32] Wei Liu, Wei-Long Zheng. Ziyi Li. Si-Yuan Wu, Lu Gan, and Bao-Liang Lu. Identifying similarities and differences emotion recognition with eeg and eye movements among chinese, german, and french people. Journal of Neural Engineering. 19(2):026012, 2022.
[33] Arman Savran', Koray Ciftei', Guillame Chanel, Javier Cruz Mota, Luong Hong Viet, Bülent Sankur Lale Akarun', ; Alice Caplier, and Michele Rombaut. Emotiondetection the loop from brain signals and facial images. 2006.
[34] Matthew D Luciw, Ewa Jarocka, and Benoni B Edin. Multi-channel eeg recordings during 3,936 grasp and lift trials with varying weight and friction. Scientific data, 1(1):1-11, 2014.
[35] Gerwin Schalk, Dennis McFarland, Thilo Hinterberger. Niels Birbaumer, and Jonathan R Wolpaw. Bei2000: general-purpose brain-computer interface (bci) system. IEEE Transactions on biomedical engineering, 1034))11344044,, 2004.
[36] Logan Trujillo, Raw ceg data, 2020.
[37] Logan T Trujillo, Candice T Stanfield, and Ruben D Vela. The effect of electroencephallgramm (eeg) reference choice on cetemaatioo-thhoreti measures of the complexity and integration of eeg signals. Frontiers in neuroscience, 1::25,, 2017.
[38] Paolo Detti, Giampaolo Vatti, and Garazi Zabalo Manrique de Lara. Eeg synchronization analysis for seizure prediction: A study data of noninvasive recordings. Processes, 8(7):846, 2020.
[39] Mastaneh Torkamani- Azar, Sumeyra Demir Kanik, Serap Aydin, and Mujdat Cetin. Prediction of reaction time and vigilance variability from spetitl features of resting-state eeg a long sustained attention task. IEEE journal of biomedical and health informatics, 24(9):2550- 2558, 2020.
[40] Louis Korczowski, Martine Cederhout, Anton Andreev, Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Violette Gautheret, and Marco Congedo. Bruin Invaders calibration-les: P300-based BCI with modulation of flash duration Dataset (bi20/5a). PhD thesis, GIPSA-lab, 2019.
[41] G Buckwalter, Chhin, S Rahman, Obeid, and Picone. Recent advances tuh eeg corpus: improving the interrater agreement for artifacts and epileptiform events. In 2021 IEEE signal processing in medicine and biology symposium (SPMB), pages 1-3. IEEF, 2021.
[42] L Veloso, McHugh, Eva von Weltin, Sebas Lopez, Obeid, and Joseph Picone. Big data resources for cogs: Enabling deep learning research. In 2017 IEEE signal processing in medicine and biology symposium (SPMB), pages 1-3. JEEE, 2017.
[43] Vinit Shah, Eva Von Weltin, Silvia Lopez, James Riley McHugh, Lillian Veloso, Meysam Golmohammadi, Iyad Obeid, and Joseph Picone. The temple university hospital scizure detection corpus. Frontiers in neuroinformatics, 12:83, 2018.
[44] Eva von Weltin, Tameem Ahsan, Vinit Shah, Dawer Jamshed, Meysam Golmohammadi, Iyad Obeid, and Joseph Picone. Electmeenephaalggrahhiic slowing: primary source of emror in automatic seizure detection. In 2017 IEFE signal processing in medicine and biology synposium (SPMB), pages 1-5. IEEF, 2017.
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A Related Work
The pursuit of generalizable representations from Electroeneepaalgraphyy (EEG) has spurred significant research into large-scale mre-training drawing inspiration from successes other domains. This section elaborates on the evolution of these efforts and the specific challenges that persist, particularly concerning the modeling of EEG's inherent spatio-temporal complexity. Early large-scale EEG models primarily focused on mitigating superficial data inconsistencies. For instance, MMM pioneered techniques to handle variable electrode configurations by employing masked auloencoder with region-wise tokenization, aiming to learn robust spatial features irrespective of specific montage details. This work highlighted the importance of addressing spatial heterogeneity directly the pre-training phase. Subsequent research broadened the scope, incorporating architectural innovations and more sophisticated pre- pre-training objectives. BIOT(Z]. for example, introduced a versatile tokenization scheme to adapt biosignals of varying lengths and types into a unified "sentence- "entenceellkee" structure, facilitating cross task learnin.. LaBraM[[] advanced the application of Masked Autoencoders (MAE) by focusing on spectral prediction in the quantized space, tokenizing raw EEG through vector-quantized representations of their frequency content. This line of work underscored the potential self-supervision in learning meaningful EEG features without explicit lahels. The development of autoregressive (AR) models marked another significant step. Neurol scaled up model capacity substantially by adopting GPT- 2 series architecture and introduced multi-modal alignment with text, enhancing the model's ability generalize across wider arry of EEG tasks. Concurrently, EEGPT[ explored dual-alignment strategies within self-supervised frameworks, combining spatio-temporal representation alignment with mask-based signal reconstruction to improve feature quality and training stability. These AR- -R-baedd approaches demonstrated impressive capabilities in learning from large unlabeled EEG corpora. Despite these advancements in handling imput variability, integrating modalities, and scaling model size, fundamental aspect often remains under- explored existing AR frameworks: the optimal sequential representation of multi-channel EEG that respects its underlying physiological organization. While powerful, the predominant "next-time prediction" paradigm, where multi-channel data is typically processed as sequence of temporal snapshots, does not inherently account for the brain's 's hierarchical topological structure or the dynamic, multi-scale interactions across different brain regions. Capturing these intricate spatio-tempen dependencies how activity at different spatial granularities co-evolves and influences future states across both scales and time- presents an ongoing challenge. This nuanced requirement for more pyyssiooggicall-grommme sequential ordering forms the primary motivation for exploring novel hierarchical autoregressive approaches, such as the one proposed in this work.
B Pre-training Dataset Description
The comprehensive description of the 17 datasets utilized for pre-training in our study is as follows:
SEED-IV [00: This dataset includes 15 subjects, with EEG signals recorded using the ESI NeuroSean system with 62 channels at a sampling rate of 1000 Hz. SEED-IV included four categories: happy, sad, fear, and neutral, totaling 8.43 hours.
SEED-V 3T : Including 15 subjects, this dataset was collected using the same sctup as SEED-V (62 channels, 1000 Hz, ESI NeuroScan system), SEED-V expanded categorics to five distinct emotions: happy, sad, fear, disgust and neutral, totaling 41.55 hours.
This dataset contains EEG recordings from 8 German subjects who viewed emotionally charged video stimuli. EEG data were collected using the ESI NeuroScan system, with emotions classified positive, negative, and neutral, totaling 25.92 hours.
SEED-GER B2]
EEG signals from French participants were recorded using the same methodology as GER. While watching emotion-related videos. subjects' EEG data were captured via the ESI NeuroScan system, with emotions categorized into positive, negative, and neutral, totaling 25.48 hours.
SEED-FRA 32):
This multimodal emotion dataset consists of EEG recordings from 16 participants. captured with 64 channels at 1024 Hz using the Biosemi Active 2 system. Emotional stimuli were derived from selected subset of the IAPS dataset, totaling 4.94 hours.
EmoBrain [3]:
Grasp and Lift EEG Challenge B. The dataset contains EEG signals (32 channels, 500 Hz) from 12 subjects engaged in grasp-and-lig lift (GAL) trials. EEG recordings were acquired using a BrainAmp EEG signal amplifier, totaling 11.72 hours.
Inria BCI Challenge 19] dataset focusing on P300-based spelling, featuring EEG signals (56 channels, 600 Hz) collected from 26 individuals. EEG data were recorded using Ag/AgCI EEG sensors with a VSM-CTF compatible system, totaling 29.98 hours.
EEG Motor Movement/Imagery Dataset [35): This dataset includes motor imagery EEG recordings from 109 volunteers using 64 channels at 160 Hz. The experiment involved baseline tasks (eyes open/closed), motor movements, and imagery tasks (both fists or both feet), recorded with the BC12000 system, totaling 47.3 hours.
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EEG signals from categorization task, recorded at 64 channels with a 256 Hz sampling rate. The dataset includes data from Information Integration innteaationnnteeraatoon categorization task and multidimensional Rule-Based categorization task, totaling 34.35 hours.
Raw EEG Data [6]
Resting State EEG Data [37] EEG data from participants, who engaged in an 8-minute resting task (4 minutes with eyes closed, 4 minutes with eyes open). The data were recorded with EEG channels 256
Hz, using BioSemi caps or freestanding Ag/AgCI electrodes, totaling 3.04 hours.
Siena Scalp EEG Database [38] EEG recordings from 14 patients, collected using EB Neuro and Natus Quantum LTM amplifiers with reusable silver/gold cup electrodes. The dataset consists of 31 31-channel recordings at 512 Hz, totaling 30.47 hours.
SPIS Resting State Dataset [39 This dataset features EEG recordings from 10 individuals, captured with 64 channels 2048 Hz. Participants completed 2.5- eyes-closed and eyes-open sessions before engaging a 105-minute Sustained Attention to Response Task (fixed sequence with varying ISIs), totaling 0.83 hours.
Target Versus Non-Target 40 EBG recordings from 50 participants playing Brain Invaders, a visual P300 brain-computer interface game employing an oddball paradigm with adaptive Riemannian geometry and no calibration requirement. EEG signals were collected from 32 channels at 512 Hz using g. USBamp amplifier and g. GAMMAcap, totaling 16 hours.
TUAR :
A dataset consisting of EEG recordings annotated with five types of artifacts, recorded with 23 channels at 256 Hz, totaling 92.22 hours.
TUEP 42
This dataset contains EEG recordings from 200 participants 100 diagnosed with epilepsy and 100 without. Data were verified by a certified neurologist, with recordings captured using 19-23 channels 256 Hz, totaling 591.22 hours.
TUSZ 43):
A mamually annotated EEG dataset for seizure detection, including precise start and stop times, affected channels, and seizure classifications. EEG recordings were obtained using 19-23 channels at 256 Hz, totaling 138.53 hours.
TUSL 44
EEG recordings annotated for slowing events, using 23 channels at 256 Hz. This dataset has been employed in research common errors in automated seizure detection, totaling 20.59 hours.
C Multi-task Dataset Description
comprehensive description of datasets utilized for multi-task instruction fine-tuning in our study as follows:
DEAP 17
This dataset an cmotion recognition dataset with 32-channel EEG recordings from 32 participants at 128 Hz. Emotions are classified into four categories based on high/low valence and arousal.
SEED [8]: This dataset comprises EEG recordings from 15 participants, captured using the ESI NeuroScan system with 62 channels at a 1000 Hz sampling rate. Participants watched emotion-related videos to induce three emotional states: positive, negative, and neutral.
MIBCI [9] MIBCI is motor imagery EEG dataset with 64-channel recordings from 52 participants, sampled 512 Hz. supports binary classification based motor imagery of the left and right hands.
BCIC4-1 20 : A motor imagery EEG dataset featuring recordings from seven individuals. EEG data collected BrainAmp MR plus amplifiers and Ag/AgCI electrode caps, with 59 EEG channels at 1000 Hz. sampling rate. The study included motor imagery tasks the left hand, right hand, foot, and an idle state, totaling 8.21 hours.
EEGMat 211:
EEGMat is a mental workload dataset featuring 23-channel EEG recordings from 36 participants. sampled at 500 Hz. It includes two states: rest and task performance, supporting binary classification for mental workload detection.
STEW (22]:
STEW includes 14-channel EEG data recorded at 128 Hz from 45 participants. It covers three levels of mental workload: low, medium, and high. This allows for three-class classification task detect mental workload based on EEG signals.
EDF This dataset a sleep stage classification dataset with 2-channel EEG recordings from 78 participants, sampled at 100 Hz. includes five sleep stages: wake, N1, N2, N3, and movement, supporting a five-class classification task.
HMC [24]: This dataset is a 4 4-channel EEG dataset for automatic sleep stage classification, recorded at 256 Hz from 151 participants. It covers five sleep stages: wake, NI, N2, N3, and REM, supporting five-class classification task.
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TUAB (25]:
This dataset is 32-channel EEG dataset for epilepsy abnormality detection, sampled at 256 Hz. It consists of 10-second segments and supports binary classification of clinically normal and abnormal signals.
TUEV [25]: This dataset is EEG dataset for event type classification, sampled at 256 Hz. It consists of 5-second segments and supports six six-class classification, covering periodic lateralized epileptiform discharge. generalized periodic epileptiform discharge, spike/sharp wave discharges, artifacts, eye movement, and background.
D Brain Topology Hierarchy
This appendix provides further details on the Brain Topology Hierarchy (BTH) introduced in Section 2 (specifi- cally, subsection 2.1), with focus on the exemplary 5-scale configuration illustrated in Figure and and utilized throughout experiments. The BTH designed to provide structured, multi-scale spatial ordering for EEG channels, moving from global brain representations to individual channel details.
The 5-scale BTH depieted in Figure constructed as follows:
This represents the coarsest level of the hicrarchy, At S1, all EEG channels are considered as a single unit, providing global representation overall brain activity. This scale allows the model to capture widespread, synchronous neural events or global brain state features.
Scale S1 (Whole Brain):
Scale S2 (Major Brain Regions): At S2, the brain parcellated into few broad regions. These regions typically defined based the spatial proximity of channels and often align with general anatomical divisions. For example, common groupings might approximate anterior (frontal). central (parietal/enssorimttorr and posterior (occipita/temppraal pariittal areas. This level enables the modeling of large-scale inter-regional interactions and broader functional specializations. In our specific implementation shown, S2 divides the channels into thrce primary horizontal bands reflecting these coarse regional distinctions.
Scale S3 (Sub-regions): Seale S3 further refines the hroad regions defined in S2 into smaller, localized zones. Each major region from $2 is subdivided, allowing activity. For instance, an anterior region might he split into left-frontal, mid-frontal, and right-frontal sub-regions. This level helps in capturing more specific regional activations and their interplay. Figure g illustrates this further parcellation.
more granular analysis brain
This scale continues the hierarchical decomposition, providing even finer spatial granularity. The sub-regions from S3 are divided into smaller clusters of channels. These clusters might correspond to more specific functional parcels or highly localized groups of electrodes, enabling the model to learn features reflecting very localized neural processing.
Scale S4 (Channel Clusters):
Scale S5 (Individual Channels): Scale S5 represents the finest level of the hicrarchy used in our model. At this seale, each node or unit effectively corresponds to an individual EEG channel, or a very small, highly localized group if the original channel density is extremely high. This allows for the most detailed spatial resolution, capturing channel-specific information and fine-grained spatial patterns.
The rationale behind this 5-scale structure is to provide rich, hierarchical representation that allows the THD-BAR framework learn and integrate features across multiple spatial resolutions simultaneously. This progressive refinement from global (S1) to local (S5) information hypothesized to be crucial for capturing the complex and multi-facete nature brain dynamics, where different different cognitive processes might manifest at
different spatial scales.
The specific groupings of channels at each intermediate scale (S2-S4) are primarily guided by the spatial adjacency of channels based standard EEG montages (c.g., extensions of the 10-20 system) and aim reflect plausible functional anatomical parcellations where possible. While the BTH concept is flexible and the number of scales or the exact channel groupings can be adapted based on specific EEG hardware, channel density, or research questions, the 5-scale hierarchy presented here provides a robust and physiologically-innnee framework for our experiments. This structured approach to spatial ordering is a key component enabling the prediction" strategy of our THD-BAR BAR model.
3000
S1
S2
S3
S4
S5
Figure 9: Brain Topology Hierarchy (BTH).
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E Experimental Configurations
Table Hyperparameters for neural tokenizer.
Hyperparameters
Values
Temporal Encoder
Input channels
{1, 16, 16)
Output channels
(16, 16, 16)
Kernel size
3,3)
Stride
{8, 1, 1)
Padding
(7, 1, 13
Transformer encoder layers
12
Transformer decoder layers Hidden size
3
768
MLP size
3072
Attention head number
12
Codebook size
8192 x 128
Training
Batch size
512
Peak learning rate
5e-5
Minimal learning rate
le-5
Learning rate scheduler
Cosine
Optimizer
Adam
Adam 8
(0.9, 0.999)
Weight decay
le-4
Total epochs
50
Warmup epochs
5
Data overlap
None
Gradient clipping
None
Table 5: Hyperparameters for autoregressive pre-training.
Hyperparameters
THD-BAR-Baas
THD-BAR-Large
THD-BAR-Huge
Model size
124M
354M
1555M
Transformer encoder layers
12
24
48
Hidden size
768
1024
1600
MLP size
3072
4096
6400
Attention head number
12
16
25
EEG batch size
480
512
512
Text batch size
32
64
64
Peak learning rate
6c-4
6c-4
6e-4
Minimal learning rate
6e-5
6e-5
6e-5
Learning rate scheduler
Cosine
Cosine
Cosine
Optimizer
AdamW
AdamW
Adam W
Adam 8
(0.9, 0.95)
(0.9, 0.95)
(0.9, 0.95)
Weight decay
0.1
0.1
0.1
Total epochs
20
20
20
Warmup epochs
2
2
2
Data overlap
None
None
None
Gradient clipping
1
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Table Hyperparameterss for instruction tuning.
Hyperparameters
Values
Instruction batch size
512
Text batch size
128
Peak learning rate
5e-4 (B), Se-5 (L), 2e-5 H)
Minimal learning rate
5e-5 (B), 5e-6 (L), 2c-6 (H)
Learning rate scheduler
Cosine
Optimizer
AdamW
Adam /
(0.9, 0.95)
Weight decay
0.1
Total epochs
5 (B, L), 3 (H)
Warmup ratio
0.1
Gradient clipping
F
Instruction Description
Table 7: Instruction description for downstream datasets.
Dataset
Instruction Description
[SEP] Question: What are valence and arousal levels of EEG segment? Options: (A) Low valence and low avoidance. (B) High valence and low avoidance. (C) Low valence and
DEAP
high avoidance. (D) High valence and high avoidance. Answer: ((A), (B), (C), (D)) [END]
[SEP] Question: Which emotion type does this EEG segment belong to? Options: (A)
SEED
Positive, (B) Neutral, (C) Negative. Answer: ((A), (B), (C) [END]
[SEP] Question: Is this EEG segments for Left hand or right hand motor imagery? Options:
MIBCI
(A) Left, (B) Right. Answer: ((A), (B)) [END]
[SEP] Question: Is this EEG segments for I Left hand or right hand motor imagery? Options:
BCIC4-1
(A) Left, (B) Right. Answer: ((A), (B)) [END]
[SEP] Question: Is this EEG segment for rest or for task? Options: (A) Rest, (B) Task.
EEGMat
Answer: ((A), (B)) [END]
[SEP] Question: What is the mental workload level of this EEG segment? Options (A) Low.
STEW
(B) Medium, (C) High. Answer: ((A), (B), (C)) [END]
[SEP] Question: What the sleep stage of this EBG segment? Options: (A) wake, (B) NI.
EDF
N2. (D) N3. (E) Movement. Answer: ((A), (B), (C), (D), (B)) (END)
[SEP] Question: Which sleep type does this EBG segment belong to? Options: (A) Wake.
HMC
(B) NREM-1. (C) NREM-2. (D) NREM-3, (E) REM. Answer: [(A), (B), (C), (D), (E))
[END]
TUAB
[SEP] Question: Is this EEG segment abnormal? Answer: (Yes, No) [END]
[SEP] Question: Which event type does this EEG segment belong to? Options: (A) spike and slow wave. (B) generalized periodic epileptiform discharge. (C) periodic lateralized epileptiform discharge. (D) eye movement. (E) artifact. (F) background. Answer: ((A), (B),
TUEV
(C), (D), (E), (F)) [END]
G
Visualization Results
Figure 10 extends the analysis of spatio-tempora dynamics. presents additional heatmap reconstruction examples from the MIBCI dataset, showcasing THVQ VAE's proficicncy in capturing the evolution of spatial brain activity patterns over consecutive time steps. These samples further illustrate its strength in representing dynamic EBG features compared to baseline methods, reinforcing the findings discussed regarding Figure iin the main text. Figure TT presents multi-domain comparison between the original EEG signal and the reconstructed signal, examining their time-domane waveforms, frequency-domen spectrograms, and spatial heatmaps. The domeidomann waveforms show close alignment, indicating effective reconstruction. The frequency- domain spectrograms and spatial heatmaps further reveal that both frequency characteristies and spatial distributions are well- sell-preerreed demonstrating the successful retention of key signal features across all domains.
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Original (Ground (n
Original
VONAE Reconstuction
VOVAE
THVO-URE Reconeaiaatton
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(b)
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1art
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Figure 10: Additional examples from the MIBCI dataset further illustrating THVQ-VAF' enhanced capability in capturing spatio-temporal dynamics of EEG signals.
Orig not Treguenty
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Figure 11: Each row showeases a different EBG channel/segment, comparing: (Left) Original vs. Reconstructed time-domain waveforms, (Center) Original vs. Reconstructed frequency-doman spectrograms (multi-channel); and (Right) Original Reconstructed spatial heatmaps specific time point.
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H Limitations and Future Work
While THD-BAN BAR shows promise, its current multi-modal integration primarily serves instruction based fine- tuning, with deeper fusion with other physiological signals richer contextual data remaining expansion. Future work will focus on enhancing THD-BAR's efficiency for real-time BCI applications through model compression and exploring von-autoreeressivve BAR variants. Expanding the pre-training corpus with more diverse EEG data (varied populations, tasks, noise conditions) and integrating advanced artifact handling will be crucial for improving robustness. Furthermore, building upon our current ahyyiologicalll--eriivee BTH and its validated effectiveness, future work will rigorously investigate the impact of alternative topological partitioning methods, such as anatomy-based templates and data-driven clustering approaches. We also aim to extend THD-BAY BHD-BAR for deeper multi modal co- co-learning with signals like iEEG. INIRS, fMRI, and behavioral data. Crucially, beyond current classification tasks, future efforts will adapt THD- BAR for broader range of applications, including EEG denoising. anomaly detection, spatio temporal localization of neural events disease biomarkers, and signal reconstruction, further validate the universality of its learned representations.
for
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penalize honesty concerning NeurIPS Paper Checklist
1. Claims
Question: the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state the main contributions, including the proposal of the THD-BAR framework, the Brain Topology Hierarchy (BTH), the THVQ-VAE tokenizer, and the "next-scale-time prediction" strategy.
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It is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
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Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The paper includes dedicated "Limitations and Future Work" section in the appendix (Appendix H). which discusses current limitations such as the scope multi-moda integration and outlines areas for future improvement.
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The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
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The answer NA means that the paper does not include theoretical results.
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Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does impact the core methodology, scientific rigorousness, or originality of the rescarch, declaration not required.
Answer: [NA]
The BAR modulc deseribed adopting GPT-2 series architecture, which known AR model, but its specific adaptation and overall THD-BAR framework are the novel contributions, not the LLM architecture itself as new component.
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for what should
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Reviews:
[{"review_id": "YnMqSEFxrz", "reviewer": "Reviewer_nsCH", "summary": "The paper introduces THD-BAR, a novel autoregressive framework for EEG signals. Here are the key points covered:\n1  The paper proposes a new method called Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations, which is designed to capture the rich physiological characteristics of EEG signals and effectively represent the dynamic spatial topology of brain activity.\n2  It introduces the Brain Topology Hierarchy (BTH), a multi-scale spatial order for EEG channels, and redesigns autoregressive learning as a \"next-scale-time prediction\" problem.\n3 The authors design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies.\n4 Through extensive large-scale pre-training on 17 datasets and rigorous validation on 10 downstream datasets across 5 distinct tasks, the proposed method demonstrates superior generalization and modeling capabilities.", "questions": "1. Model efficiency does not scale proportionally with performance improvements: The Huge version of the THD-BAR model mentioned in the paper has 1.5 billion parameters, requiring 20 epochs of training on 8 L40 GPUs (48GB VRAM), which imposes extremely high demands on hardware computing power. Considering the training costs and actual performance improvements, it was found that the performance gains are limited, making the cost-effectiveness low, which restricts the model's application potential in resource-constrained environments.\n\n2. Incomplete details on data preprocessing: In the data preprocessing section, while IQR scaling is mentioned for robust scaling to reduce the impact of outliers, the specific quantiles used are not explicitly stated. It is recommended to clearly specify the quantiles used in the paper to facilitate readers' understanding and reproduction of the relevant experiments.\n\n3. Formatting and expression issues: There are some formatting flaws in the paper, such as repetitive expressions in some sections (e.g., Section 4.1) and misaligned formula numbers, which affect the readability and professionalism of the paper. It is recommended that the authors carefully proofread the entire paper and correct such issues to improve the quality of the paper.\n\n4. Insufficient method details and validation: The paper only vaguely describes the topological division principle of “system + spatial proximity + brain region function” in the main text, while the specific details of the 5-scale division are placed in Appendix D.", "limitations": "1. Conduct additional experiments to investigate the impact of different topological partitioning methods (e.g., anatomy-based templates, data-driven clustering, etc.) on experimental results, thereby demonstrating the robustness and superiority of the proposed method.\n2. Report the average physical distance or functional connectivity metrics between channels to more effectively validate the rationality of the adopted hierarchical strategy.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The paper presents a comprehensive study to support the effectiveness of the proposed method. The experimental results show that THD-BAR outperforms existing methods on various EEG tasks, including emotion recognition, motor imagery classification, mental workload assessment, sleep stage classification, and epilepsy detection. The authors also conduct ablation studies to validate the necessity of the multi-scale design and the proposed masking strategies. The visualization results further demonstrate the ability of THVQ-VAE to capture the dynamic evolution of brain activity patterns.\n\nThe paper is well-structured and clearly written, with a logical flow of ideas. The proposed method is innovative and has the potential to advance the field of EEG analysis. The experimental results are convincing and demonstrate the effectiveness of the proposed approach. The ablation studies provide valuable insights into the design choices of the model. The visualization results offer a qualitative assessment of the model's performance and help to better understand the capabilities of the proposed method.\n\nOverall, this paper presents a significant advancement in the field of EEG analysis and provides a promising new approach for learning universal EEG representations. The proposed THD-BAR framework demonstrates superior performance on a variety of EEG tasks and has the potential to impact various applications in brain-computer interfaces and related fields.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer nsCH for their exceptionally thorough, insightful, and highly constructive suggestions. Particularly, we are greatly encouraged by the reviewer's positive assessment of our work, including its novelty, structure, and potential impact, as well as the \"Accept\" recommendation. The questions and suggestions raised are invaluable and have helped us identify several key areas where the manuscript can be significantly improved in terms of clarity, reproducibility, and completeness.\n\nWe have carefully considered each point and have prepared detailed responses below. We will incorporate the suggestions into our manuscript to strengthen the paper. For clarity, we have addressed each point from the \"Questions\" and \"Limitations\" sections in order.\n\n**Questions / Limitations**:\n\n**[Q1] Model efficiency does not scale…**\n\nWe sincerely appreciate the insightful questions raised by the reviewers regarding the limitations of performance improvement and the potential applicability of the THD-BAR model. In fact, according to the **scaling laws** [1], as the model size increases, the performance metrics exhibit **a power-law relationship** with diminishing marginal returns, indicating that the performance improvements are accompanied by a significant rise in the costs of model training and inference. We have a detailed analysis of the model size, performance metrics, and corresponding resource consumption in our manuscript, our experimental results align with the predictions of the scaling laws, which validates that our method adheres to these principles.\n\nOur proposed THD-BAR model currently outperforms existing state-of-the-art methods, indicating notable superiority recognition performance in numerous complex task scenarios. Particularly, to improve the our proposed THD-BAR model practical applicability, we intend to further condense the model parameters and refine the model architecture, thereby improving its operational efficiency and facilitating easier deployment in resource-limited settings.\n\n[1] Kaplan J, McCandlish S, Henighan T, et al. Scaling laws for neural language models[J]. arXiv preprint arXiv:2001.08361, 2020.\n\n**[Q2] Incomplete details on data…**\n\nWe sincerely appreciate the reviewer’s insightful questions regarding data preprocessing. In fact, for the Interquartile Range (IQR) scaling, we employed the standard quartiles: the 25th percentile (Q1) and the 75th percentile (Q3). The scaling using the standard quartiles is computing as follows: `scaled_x = (x - Q1) / (Q3 - Q1)`.\n\n**[Q3] Formatting and expression…**\n\nWe take the formatting and expression issues pointed out by the reviewer seriously, and we will thoroughly revise the manuscript to address all formatting and expression issues including repetitive phrasing and misaligned formula numbers. \n\n**[Q4] Insufficient method details and validation…**\n\nWe appreciate the reviewer's excellent suggestions on the detailed description of the Brain Topology Hierarchy (BTH) in our manuscript. We will directly incorporate the topological division principle of “system + spatial proximity + brain region function” and illustrative examples of the 5-scale division into Section 2.1 of our manuscript.\n\n**[L1] Conduct additional experiments…**\n\nWe appreciate the reviewer's valuable suggestion about the impact of different topological partitioning methods. In this paper, we have developed the Brain Topology Hierarchy (BTH) model based on physiological principles, which deeply integrates core physiological features such as brain region functional localization. Our BTH model has adopted the widely used 10-10 electrode system to ensure its applicability across the majority of common EEG datasets. Our multi-scale ablation experiments (Section 4.2, Figure 6) have further validated the effectiveness of our chosen hierarchical structure, demonstrating that the complete integration of 5 physiologically-derived scales consistently achieves the optimal performance. In future, we will follow the suggestions of the reviewers to explore more diverse topological partitioning methods, including anatomy-based templates and data-driven clustering approaches and further optimize the model's ability to analyze complex EEG patterns and enhance its generalization performance.\n\n**[L2] Report the average physical…**\n\nWe appreciate the reviewer's excellent suggestion. Following your recommendation, we have conducted an analysis based on the physical distances between EEG channels (using standardized 3D electrode coordinates from a 10-20 system template). We calculated the average intra-group distance (the mean distance between all pairs of channels within the same group) and the average inter-group distance (the mean distance between channels in different groups) for each level of our BTH. The results provide strong quantitative support for our hierarchical strategy, as shown in the table below:\n\n| Hierarchy | Intra-Group|Physical|Distance |\n| --- | --- | --- | --- |\n|  | **Mean(m)** | **Max(m)** | **VAR(m^2)** |\n| S1 (Whole Brain) | 0.130401 | 0.210430 | 0.002281 |\n| S2 | 0.099308 | 0.204015 | 0.002291 |\n| S3 | 0.049732 | 0.103462 | 0.000352 |\n| S4 | 0.037156 | 0.073900 | 0.000154 |\n| S5 (Individual) | - | - | - |\n\nAs the hierarchy becomes finer (from S2 to S5), the average intra-group distance systematically decreases, confirming that our groupings are spatially coherent. At every level, the **average intra-group distance is significantly smaller than the average inter-group distance**. This demonstrates that our BTH successfully partitions the channels into spatially compact and distinct clusters, providing a data-driven validation of its structural rationality.\nWe will incorporate this analysis and table into a new subsection in the revised manuscript to quantitatively justify our BTH design."}, {"review_id": "pDD1eAmWoP", "reviewer": "Reviewer_2X2X", "summary": "The paper introduces THD-BAR, a novel framework designed for EEG signal analysis, leveraging \"next-scale-time prediction\" as an autoregressive modeling strategy. Its core innovation is the Brain Topology Hierarchy (BTH), which structures EEG channels according to a multi-scale spatial order. THD-BAR employs a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) to generate discrete hierarchical EEG representations, and a Brain Autoregressive (BAR) module to capture complex spatio-temporal dependencies across both scales and time steps. The framework demonstrates competitive performance through extensive pre-training on 17 diverse EEG datasets, followed by fine-tuning on 10 downstream tasks, ranging from emotion recognition to epilepsy detection. The results highlight the model's ability to generalize across various EEG applications.", "questions": "1. The prompts used during instruction fine-tuning appear to be very brief. Have the authors explored the use of more elaborate prompts, such as detailed task descriptions or chain-of-thought (CoT) prompting, to potentially enhance model performance?\n2. I am curious about the ablation strategy for BTH. What would be the effect of a configuration like S1–S3–S5, which preserves the coarse-to-fine progression from whole brain to individual channels without disrupting the hierarchical scale structure? If the full configuration S1–S2–S3–S4–S5 yields the best results, does this suggest that increasing the granularity of scales could lead to further performance improvements?\n3. In the proposed THVQ-VAE, the terms \"downscale\" and \"upscale\" are used, but their precise technical definitions are unclear. Could the authors provide more concrete details on the mechanisms and implementations of these scaling operations within the model?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. Novel Hierarchical Framework for EEG Representation Learning: The paper introduces BTH that systematically models EEG signals across multiple spatial scales (whole brain → regions → single channels). This is a significant conceptual advancement over prior autoregressive EEG models that rely solely on temporal sequencing. The proposed next-scale-time prediction strategy effectively captures both spatial and temporal dynamics, addressing a key limitation in existing approaches.\n2. Advanced Tokenization with THVQ-VAE: THVQ-VAE is an advanced neural tokenizer that plays a crucial role in discretizing the EEG signal into hierarchical tokens. By incorporating multi-scale quantization, the THVQ-VAE enhances the model's ability to capture detailed spatial patterns at various levels, making it well-suited for modeling the dynamic characteristics of EEG signals across multiple scales.\n3. Strong Empirical Validation Across Diverse Tasks: The authors conduct large-scale pre-training (17 datasets) and rigorous evaluation (10 downstream tasks), demonstrating consistent improvements over state-of-the-art methods. The multi-scale ablation studies and masking strategy comparisons provide convincing evidence for the design choices.\n\nWhile the proposed THD-BAR makes a valuable contribution to the field of brain foundation models, several weaknesses should be addressed to strengthen the paper's contributions and clarity:\n\n1. BTH is constructed based on full EEG channel configurations, but the downstream evaluation includes datasets with significantly fewer channels (e.g., EDF with 2 channels and HMC with 4 channels). The authors should explicitly discuss how channel sparsity affects the hierarchical spatial modeling in BTH and the generalizability of BTH to low-channel EEG systems, which are common in clinical or wearable settings.\n2. The choice of GPT-2 for the BAR module is not sufficiently motivated, especially given the availability of more advanced LLMs (e.g., LLaMA). The authors should clarify why GPT-2 was selected over newer architectures. *Suggestion*: Introduce the LLM earlier (e.g., in Section 2.1 or 2.2) to avoid confusion about the domain classifier's role and the overall framework design.\n3. The current ablation compares configurations using single or multiple scales but does not isolate the contribution of individual scales. Perform \"leave-one-scale-out\" experiments (e.g., remove S3 while retaining S1-S2-S4-S5) to quantify the importance of each scale.\n4. The explanations of the THVQ-VAE and Next-Scale-Time Prediction methods in the paper lack sufficient technical detail, and the current content contains substantial overlap with the descriptions in NeuroLM [1].\n5.  The current experiments focus on fine-tuning performance. To better validate the model's robustness as a foundation model, the authors should include zero-shot or few-shot evaluations on unseen tasks or prompts.\n6.  Figure-related Issues: Inconsistent font styles across different figures and within individual figures; In Figure 5, the notation \"S1+S2+S3+...+Sn\" in the lower right corner should use subscripts; Figure captions are overly concise and should include more detailed descriptions.\n7.  Table-related Issues: In Table 1, the bold formatting of task names in the \"Task\" column lacks justification; Table 2 lacks parameter counts for EEGNet, TSception and LGGNet models, which requires explanation; In Table 3, NeuroLM is erroneously cited as reference [5] (LaBraM).\n8.  Formula and Expression Issues: The sample length is initially defined as $W$, but the patch length is $T=\\lfloor \\frac{L}{P} \\rfloor$. Please clarify which variable is correct; Improper LaTeX syntax in Equation (2): space between \"arg\" and \"min\" should be removed; Ambiguous notation $q's$ in Section 2.3 Line 158 needs clarification; Typo in Section 3.1 Line 188: \"inAppendix\" should be corrected to \"in Appendix\" (add space).\n\nCitation:\n\n[1]Wei-Bang Jiang, Yansen Wang, Bao-Liang Lu, and Dongsheng Li. Neurolm: A universal multi-task foundation model for bridging the gap between language and eeg signals. *arXiv preprint arXiv:2409.00101*, 2024.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer 2X2X for their positive evaluation and highly detailed, constructive suggestions. We are greatly encouraged by the recognition of our work's novelty and strong empirical validation. The weaknesses and questions identified are exceptionally insightful and provide a clear path for strengthening the manuscript. A point-by-point reply to the reviewer’s comments is given below.\n\n**Weaknesses / Questions**\n\n**[W1] BTH is constructed based on…**\n\nWe thank the reviewer for this excellent question. We have discussed how channel sparsity affects the hierarchical spatial modeling of BTH, as well as its generalization capability in low-channel EEG systems, which are prevalent in clinical or wearable settings. Specifically, the Brain Topology Hierarchy (BTH) is designed to be a flexible framework. Its structure is dynamically adapted based on the available channels in a given EEG montage. For datasets with sparse channels (e.g., 2-channel EDF), the hierarchy naturally collapses into a simpler form. For instance, the finest scale (S5) would represent the individual channels, and the coarser scales (S1-S4) would represent trivial groupings of these few channels. Although some scales may become degenerate, the core principle of multi-scale decomposition remains. The model still learns to represent information from a \"global\" (all available channels) to a \"local\" (individual channel) level.\n\n**[W2] Dependency on GPT-2**\n\nWe thank the reviewer for this question. Our choice of the GPT-2 architecture was a deliberate one, intended to isolate the impact of our primary contribution: the Brain Topology Hierarchy (BTH) and our \"next-scale-time prediction\" strategy. By using a well-established backbone, we can directly attribute performance gains to our novel pre-training framework rather than the architecture itself. This approach ensures a fair and clear comparison with existing models, such as NeuroLM. While more advanced backbones like LLaMA are powerful, using them would confound the evaluation of our core ideas. Our framework is backbone-agnostic, and proving its effectiveness on a standard, reproducible architecture provides a stronger scientific baseline. Future work can readily explore integrating newer LLMs to potentially further enhance performance, building upon the foundation we have established here. We also appreciate the suggestion to improve clarity. In our revision, we will follow your advice and introduce that the BAR module is implemented with a GPT-2 style causal transformer earlier in the methods section (Section 2). This will provide better context for the overall framework design before detailing its individual components, clarifying the relationship between the tokenizer, the domain classifier, and the autoregressive module.\n\n**[W3 & Q2] Ablation study**\n\nWe appreciate the reviewer's insightful questions regarding the granularity and specific configurations of our Brain Topology Hierarchy (BTH) ablation study. We have conducted additional experiments to investigate the marginal contribution of each hierarchical level and the importance of scale continuity. These new experiments include \"leave-one-scale-out\" configurations as well as sparse hierarchy setups. The experimental results are presented in the table below.\n\n| Scales  | **THVQ-VAE** |  | **BAR** |  |\n| --- | --- | --- | --- | --- |\n| Included | PCC | Loss                          | ACC | Loss |\n| S1-S3-S5 | 0.712 | 0.186 | 0.730 | 1.342 |\n| S2-S3-S4-S5 | 0.728 | 0.184 | 0.734 | 1.121 |\n| S1-S3-S4-S5 | 0.725 | 0.181 | 0.729 | 1.344 |\n| S1-S2-S4-S5 | 0.711 | 0.191 | 0.654 | 0.148 |\n| S1-S2-S3-S5 | 0.673 | 0.216 | 0.565 | 1.492 |\n| S1-S2-S3-S4 | 0.526 | 0.257 | 0.456 | 2.827 |\n\nWe are confident that these additional experiments will provide a more granular understanding of each scale's contribution and further validate the robustness and optimality of our BTH design. We will incorporate these full results into the final version of the paper upon acceptance.\n\n**[W4] The explanations of the THVQ-VAE…**\n\nWe thank the reviewer for pushing us to clarify our technical novelty. While we build on the AR paradigm, our proposed THVQ-VAE framework differs fundamentally from NeuroLM in its core components, which is given below in detail.\n\n1. **Tokenization:** NeuroLM uses a standard VQ-VAE. Our **THVQ-VAE** is a new architecture that performs **hierarchical tokenization** guided by the BTH. It uses unique `downscale` and `upscale` operations (Algorithms 1 & 2) to create a set of complementary tokens for each spatial scale, a concept absent in NeuroLM.\n2. **Prediction Task:** NeuroLM employs \"next-time prediction.\" We introduce **\"next-scale-time prediction,\"** a fundamentally different objective that models spatial dependencies *within* each time step before advancing temporally.\n3. **Masking:** This new objective necessitates our novel **Scale-Time-wise Mask**, which implements a nested spatio-temporal causality, unlike the simple temporal mask in previous works. These co-designed innovations represent a significant conceptual and technical departure from existing AR frameworks.\n\n**[W5] The current experiments focus on…**\n\nWe thank the reviewer for this valuable suggestion. We fully agree that zero-shot and few-shot evaluations are the ultimate test for any foundation model. Our current work's primary goal was to first establish the efficacy of our novel pre-training paradigm through comprehensive fine-tuning, demonstrating that the learned representations are indeed powerful and generalizable across a wide variety of tasks. The promising results from this stage provide a strong basis for future explorations. We consider explicit zero-shot and few-shot benchmarking to be a critical and high-priority next step, and we have added this as a key direction in our **Future Work** section.\n\n**[W6 & W7 & W8] Figure, Table, and Formula/Expression Issues**\n\nWe sincerely thank the reviewer for their meticulous and detailed review of our manuscript's presentation. We apologize for the formatting errors, inconsistencies, and typos across figures, tables, and formulas. In response to your comprehensive feedback, we have conducted a thorough proofreading and revision pass to address every point mentioned:\n\n- All figures and tables have been corrected for consistency, formatting (e.g., subscripts in Figure 5, bolding in Table 1), and clarity (e.g., more descriptive captions, explaining missing parameters).\n- All formulaic and notational errors have been rectified, including the calculation of *T*, LaTeX syntax, and ambiguous terms like \"q's\".\n- All typographical errors have been corrected. We are confident these revisions significantly improve the professionalism, clarity, and reproducibility of our paper.\n\n**[Q1] The prompts used during instruction…**\n\nThank you for this insightful question. Our use of brief prompts during instruction fine-tuning was a deliberate choice. The primary goal was to establish a simple, consistent, and reproducible baseline. This approach allows us to confidently attribute the model's performance gains to our novel pre-training framework—the Brain Topology Hierarchy (BTH) and \"next-scale-time prediction\"—rather than to the effects of complex prompt engineering. Regarding **Chain-of-Thought (CoT) prompting**, we believe it may be less suitable for the specific nature of EEG classification. CoT excels at tasks that involve explicit, multi-step logical reasoning. In contrast, EEG classification is fundamentally a pattern recognition or perceptual task. The model learns a direct, implicit mapping from high-dimensional signal patterns to a classification label. Its internal decision-making process is not easily decomposable into a discrete, human-interpretable chain of logical steps. Forcing the model to generate such an output could conflict with its intrinsic decision mechanism and potentially hinder performance.\n\n**[Q3] \"downscale\" and \"upscale\"**\n\nIn the proposed THVQ-VAE, \"downscale\" and \"upscale\" are fundamental operations for processing EEG features across our Brain Topology Hierarchy (BTH), which defines multi-scale spatial orders. The BTH establishes a hierarchical organization of EEG channels, where `ch_s` denotes the channel grouping at a specific scale `s` (e.g., S1 for whole brain, S5 for individual channels). `ch_S` specifically refers to the finest scale (our S5), representing the original individual channel resolution. \n\n**Downscale:** This operation aggregates EEG features from a finer spatial scale (e.g., individual channels at `ch_S`) to a coarser scale (e.g., broader brain regions at a coarser `ch_s`). Its technical mechanism involves mean aggregation: for each predefined coarser channel group, the feature values of all constituent finer channels or channel groups are averaged. This compresses spatial information, reducing the number of spatial units, typically occurring in the encoder path to generate multi-scale tokens. \n\n**Upscale:** Conversely, this operation interpolates EEG features from a coarser scale (e.g., a quantized feature vector `z_s` from a coarser `ch_s`) back to a finer scale (typically `ch_S`). Its technical mechanism is replication or copying, acting as a form of nearest-neighbor interpolation. The feature value of a coarser channel group is duplicated across all the individual channels or finer groups it encompasses. This distributes aggregated information to a higher spatial resolution, crucial for residual learning within the encoder and for accumulating features during the decoder's reconstruction."}, {"review_id": "eX7mCWoyZB", "reviewer": "Reviewer_cY9i", "summary": "This paper proposes THD-BAR, a novel autoregressive framework for EEG representation learning. It introduces a Brain Topology Hierarchy (BTH) to model spatial structure and temporal dynamics in EEG data, employing a Topology-Hierarchical VQ-VAE (THVQ-VAE) for hierarchical tokenization and a Brain Autoregressive (BAR) module for \"next-scale-time\" prediction. The framework is pre-trained on 17 datasets and evaluated on 10 downstream EEG tasks across five categories, demonstrating state-of-the-art performance and generalizability.", "questions": "1. Did you experiment with different numbers of scales (e.g., 3 vs. 5 vs. 7)? How does scale granularity affect performance?\n2. Could you clarify what chs and zs refer to in the THVQ-VAE, and how the upscaling/downscaling steps are performed?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n1. The paper presents evaluations on an impressive number of datasets (17 for pre-training, 10 for fine-tuning), supporting its claim of generalization.\n2. Ablation studies are well-designed and comprehensive, highlighting the necessity of the multi-scale design and the effectiveness of the masking strategies.\n3. Code availability encourages reproducibility and future research adoption.\n\nWeakness:\n1. Some critical implementation details are omitted. For instance, the data splitting strategy for train/val/test sets on downstream tasks is not discussed in the main paper.\n2. The THVQ-VAE design could be better explained. Terms like chs, zs, downscale, and upscale are mentioned in algorithms but not intuitively described. Ablation is also needed to verify the necessity of these components.\n3. While the BTH is novel, the overall framework is closely tied to existing work like NeuroLM. Many components (e.g., VQ-VAE, masking, instruction tuning) are adaptations or refinements rather than fundamentally new.", "was_revised": true, "final_justification": "The rebuttal is clear and convincing. I maintain my original score.", "rebuttal": "We extend our sincere gratitude to Reviewer cY9i for their meticulous review and constructive suggestion. We are encouraged that the reviewer recognized the strengths of our work, including the extensive evaluations and well-designed ablations. The weaknesses and questions identified are crucial, and we have carefully considered each point to improve the clarity, rigor, and positioning of our manuscript. Our detailed responses are outlined below.\n\n**Weaknesses / Questions**\n\n**[W1] On the omitted data splitting strategy**\n\nWe thank the reviewer for pointing out this critical omission. Our data splitting strategies were carefully chosen for each downstream dataset to ensure a fair and robust evaluation, generally following a subject-independent protocol wherever possible to test for generalization to unseen individuals.\n\nFollowing common practices in the field, our specific strategies varied by dataset to respect their unique structures:\n\n1. For datasets with official splits (e.g., TUAB, TUEV), we used the predefined train/test partitions, creating our validation set by splitting their training subjects 80%/20%.\n\n2. For those with specific protocols (e.g., SEED, HMC), we followed their established methods, such as chronological trial-based splits or fixed subject-ID divisions.\n\n3. For all other datasets, we employed a unified and rigorous cross-subject approach. We randomly partitioned subjects into an 8:1:1 ratio for training, validation, and testing, ensuring no subject overlap.\n\nWe will add a new, detailed subsection to the Appendix of our revised manuscript that explicitly outlines the precise splitting strategy used for each of the 10 downstream datasets, ensuring full transparency and reproducibility. We believe this addition will fully address the reviewer's concern.\n\n**[W3] While the BTH is novel…**\n\nThank you for your insightful feedback. We agree that our framework leverages successful paradigms, such as VQ-VAE and Transformer architectures, but our core innovation lies in fundamentally redefining autoregressive learning for EEG. Our primary contribution is the shift from a conventional \"next-time prediction\" to a physiologically-informed \"next-scale-time prediction\" paradigm. This is achieved through several distinct innovations:  \n\n1. **Brain Topology Hierarchy (BTH):** BTH introduces a novel multi-scale spatial order for EEG channels (whole brain to individual), fundamentally differing from linear temporal sequencing.\n2. **Topology-Hierarchical VQ-VAE (THVQ-VAE):** Uniquely designed to tokenize EEG into BTH-aligned multi-scale hierarchical discrete tokens, THVQ-VAE captures deeper features than single-granularity methods.\n3. **\"Next-Scale-Time Prediction\" Strategy:** Our core strategy (Fig. 1c) compels the model to predict tokens hierarchically across scales *within* each time step before advancing temporally, simultaneously modeling intra-time spatial and inter-time dynamics. This is enabled by a specialized Scale-Time-wise Mask (Fig. 7c), enforcing causality across both time and spatial scales, crucial for superior performance.\n\nIn summary, THD-BAR's novelty lies in its BTH-driven, multi-scale spatial ordering and the re-conceptualized EEG autoregression. These innovations effectively model complex spatio-temporal dynamics, yielding significant performance improvements.\n\n**[Q1] Ablation study**\n\nWe appreciate the reviewer's insightful questions regarding the granularity and specific configurations of our Brain Topology Hierarchy (BTH) ablation study. We have conducted additional experiments to investigate the marginal contribution of each hierarchical level and the importance of scale continuity. These new experiments include \"leave-one-scale-out\" configurations as well as sparse hierarchy setups. The experimental results are presented in the table below.\n\n| Scales  | THVQ-VAE |  | BAR  | |\n| --- | --- | --- | --- | --- |\n| Included | PCC | Loss                          | ACC | Loss |\n| S1-S3-S5 | 0.712 | 0.186 | 0.730 | 1.342 |\n| S2-S3-S4-S5 | 0.728 | 0.184 | 0.734 | 1.121 |\n| S1-S3-S4-S5 | 0.725 | 0.181 | 0.729 | 1.344 |\n| S1-S2-S4-S5 | 0.711 | 0.191 | 0.654 | 0.148 |\n| S1-S2-S3-S5 | 0.673 | 0.216 | 0.565 | 1.492 |\n| S1-S2-S3-S4 | 0.526 | 0.257 | 0.456 | 2.827 |\n\nWe are confident that these additional experiments will provide a more granular understanding of each scale's contribution and further validate the robustness and optimality of our BTH design. We will incorporate these full results into the final version of the paper upon acceptance.\n\n**[W2 & Q2] Clarification of Terms**\n\nThe THVQ-VAE's primary function is to transform continuous EEG signals into a sequence of discrete, hierarchically structured tokens. Let's clarify the key terms and their roles within the THVQ-VAE:\n\n1. **`ch_s` (Multi-scale Channel Hierarchy):** Represents the spatial organization or grouping of EEG channels at a specific scale `s` within the BTH (e.g., S1 for whole brain, S5 for individual channels).\n2. **`ch_S`: Specifically denotes the channel configuration at the finest (most granular) scale** of the BTH (our S5), typically corresponding to the original number of individual EEG channels. It serves as the baseline spatial resolution for the encoder's output and as a target/source for scale transformations.\n3. **`z_s` (Quantized Feature Vector at Scale s):** This is the continuous feature vector retrieved from a shared codebook, representing the discrete token for a given scale `s`.\n4. **`downscale`:** A spatial aggregation operation that transforms EEG features from a finer scale to a coarser scale (e.g., from `ch_S` to `ch_s`). It achieves this by averaging the signals of multiple channels or channel groups that belong to a predefined coarser spatial unit, effectively compressing the information.\n5. **`upscale`:** A spatial interpolation operation that transforms EEG features from a coarser scale to a finer scale (e.g., from `ch_s` back to `ch_S`). It does so by copying the feature value of a coarser channel group to all the individual channels or finer groups it encompasses, acting as a form of nearest-neighbor interpolation. These operations are crucial for residual learning during encoding and information accumulation during decoding."}, {"review_id": "ZASs1DMSem", "reviewer": "Reviewer_tA9Y", "summary": "This paper introduces THD-BAR (Topology Hierarchical Derived Brain Autoregressive Modeling), a novel framework designed to learn generic EEG representations by incorporating both spatial and temporal dynamics. The key innovation lies in the construction of a Brain Topology Hierarchy (BTH), which leverages standard electrode placements and domain knowledge to establish a multi-scale spatial structure of EEG channels. The authors further propose a Topology-Hierarchical Vector Quantized-VAE (THVQ-VAE) to support multi-scale tokenization. The framework is validated through large-scale pre-training on 17 datasets, followed by downstream evaluation on 10 datasets across 5 distinct tasks, demonstrating generalization performance and consistent improvements over prior methods.", "questions": "1. The authors adopt GPT-2 as the language model backbone in the BAR module. Given the availability of more powerful LLMs and open-source LLMs (e.g., LLaMA), could the authors justify the choice of GPT-2? Have alternative backbones been considered or compared?\n\n2. The paper mentions that \"The patch size P and window size W are set to 200 and 1024\". Given the heterogeneity of downstream datasets (e.g., varying sampling rates, sequence lengths, patch lengths, and more), using a fixed configuration may not be optimal. Could the authors clarify whether different configurations were tested per task? If not, how does the fixed setting impact model performance and adaptability?\n\n3. I recommend the authors include more related works such as the Brant and BrainBERT in the Related Work section, as they are closely related to the paper’s target downstream tasks.\n\n4. If space permits, the Related Work section should be moved from the appendix to the main paper to better serve readers, especially those without strong domain expertise.\n\n5. The current anonymous code repository lacks actual code or documentation. I strongly encourage the authors to either upload the code or provide a justification for this incomplete release. Without this, reproducibility remains a concern.\n\n6. The authors propose a topology hierarchy based on non-invasive EEG. Could the same method be extended to intracranial EEG signals with suitable hierarchical grouping? A discussion on the applicability or limitations in the context of iEEG would strengthen the paper’s broader relevance.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### Strengths\n\n- The paper is technically sound and well-motivated. The proposed Brain Topology Hierarchy (BTH) is grounded in established neurophysiological priors, offering a meaningful inductive bias to guide representation learning.\n\n- The multi-scale spatial structure is constructed by referencing standard EEG electrode systems, resulting in a five-level hierarchical decomposition from the whole brain to individual channels, which is conceptually clear and practically relevant.\n\n- The proposed model is comprehensively evaluated through large-scale pre-training and downstream transfer, and the authors conduct ablation studies that validate the necessity of multi-scale design and reveal the optimal scale utilization strategy for different scenarios.\n\n### Weaknesses\n\n- While the Related Work section includes recent efforts such as LaBram and NeuroLM, it overlooks several notable brain signal or physiological signal modeling efforts that are directly relevant to the downstream tasks addressed in the paper. For example, the Brant series works [1,2,3] and BrainBERT [4] solve similar tasks such as epilepsy detection, sleep staging, emotion recognition, and motor imagery. Therefore, these works should be discussed for a more comprehensive contextualization.\n\n- Additionally, for a deep learning paper in a domain-specific area, placing the Related Work section in the appendix may hinder accessibility for readers who are less familiar with the domain background. Including it in the main paper (if page limits allow) would improve readability and clarity.\n\n- Although the authors provide a link to an anonymous code repository, it currently only contains an **empty README file**. This significantly limits the reproducibility of the proposed approach and requires clarification.\n\n> [1] Brant: Foundation model for intracranial neural signal[J]. Advances in Neural Information Processing Systems, 2023, 36: 26304-26321.\n>\n> [2] Brant-2: Foundation model for brain signals[J]. arXiv e-prints, 2024: arXiv: 2402.10251.\n>\n> [3] Brant-X: A Unified Physiological Signal Alignment Framework[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024: 4155-4166.\n>\n> [4] BrainBERT: Self-supervised representation learning for intracranial recordings[J]. arXiv preprint arXiv:2302.14367, 2023.", "was_revised": true, "final_justification": "Most issues were solved. I maintain my positive recommendation for this paper, and hope that the revisions made by the authors during the rebuttal stage will be fully incorporated into the final version of the paper.", "rebuttal": "We sincerely thank Reviewer tA9Y for their incredibly detailed and constructive suggestions. We are grateful for the insightful questions and suggestions, which have provided a clear roadmap for improving the manuscript's clarity, comprehensiveness, and impact. We have carefully considered each point and outline our planned revisions below.\n\n**Weaknesses / Questions**\n\n**[Q1] Dependency on GPT-2**\n\nWe thank the reviewer for this question. Our choice of the GPT-2 architecture was a deliberate one, intended to isolate the impact of our primary contribution: the Brain Topology Hierarchy (BTH) and our \"next-scale-time prediction\" strategy. By using a well-established backbone, we can directly attribute performance gains to our novel pre-training framework rather than the architecture itself. This approach ensures a fair and clear comparison with prior art, such as NeuroLM, which uses a similar GPT-2 style model. While more advanced backbones like LLaMA are powerful, using them would confound the evaluation of our core ideas. Our framework is backbone-agnostic, and proving its effectiveness on a standard, reproducible architecture provides a stronger scientific baseline. Future work can readily explore integrating newer LLMs to potentially further enhance performance, building upon the foundation we have established here.\n\n**[Q2] The paper mentions that \"The patch size…**\n\nWe thank the reviewer for this excellent and practical question regarding our hyperparameter settings. The reviewer is correct; we intentionally used a fixed configuration (`P=200`, `W=1024`) for all downstream tasks. This was not an oversight but a deliberate design choice central to our goal of developing a single, robust, and truly general-purpose foundation model. This unified approach is made possible by a critical step in our data preprocessing pipeline (detailed in Section 4.2): all EEG signals, regardless of their original sampling rate, are resampled to a uniform 200 Hz. This standardization is key. It ensures that a patch size of `P=200` consistently corresponds to a 1-second segment of brain activity. This temporal consistency allows the model to learn meaningful, comparable patterns across highly heterogeneous datasets. For signals of varying lengths, we employ standard windowing and padding techniques, with the attention mask correctly handling any padded segments so they do not affect the outcome. While we acknowledge that fine-tuning these hyperparameters for each specific task might yield marginal performance gains, doing so would create a collection of specialized models, undermining the very concept of a universal foundation model. Our objective was to demonstrate that a single, fixed-configuration model could adapt and excel across a wide variety of tasks and data types.\n\n**[Q3 & Q4] Related Work**\n\nWe sincerely thank the reviewer for their insightful and constructive feedback on the scope and placement of our Related Work section. We fully agree that a comprehensive discussion of prior art is essential for contextualizing our contributions and that its placement significantly impacts the paper's accessibility, especially for an interdisciplinary audience.\n\nWe will certainly revise our Related Work section to include a detailed discussion of the Brant series and BrainBERT. Your suggestion is well-taken; these models are highly relevant as they address similar downstream tasks such as epilepsy detection and sleep staging. Incorporating them will allow us to provide a richer comparison and better contrast our unique hierarchical spatio-temporal modeling approach against other successful paradigms in the field, strengthening the positioning of our work.\n\nRegarding the placement of the section, we appreciate the reviewer's perspective. As you may have noted, we included a brief overview of key recent models in the second paragraph of our Introduction to provide initial context. The decision to place the more extensive, detailed Related Work section in the appendix was made solely due to strict conference page limitations.\n\nTo address this, we will adopt a more effective hybrid approach in our revision. We will move a more substantial, yet still concise, Related Work section into the main body of the paper. This section will be updated to include the Brant series and BrainBERT, ensuring all readers have the necessary background upfront. Concurrently, we will refine the summary in the Introduction to smoothly lead into this new section. The fully comprehensive literature survey will remain in the appendix for those desiring more in-depth information.\n\nWe believe this two-tiered approach will best serve all readers by providing essential context within the main text while respecting space constraints. We are confident these changes will significantly improve the paper's clarity and scholarly rigor.\n\n**[Q5] Code Availability**\n\nWe sincerely thank the reviewer for highlighting this critical issue and apologize for the initial oversight regarding the empty repository.\n\nTo ensure full reproducibility, our complete source code was indeed included as a supplementary material attachment with our original submission. We agree that direct repository access is more convenient and transparent for the review process. Therefore, following your feedback, we have now uploaded our complete source code to the anonymous repository link provided in the paper. The repository now contains the full implementation of our THD-BAR framework, including all model configurations and scripts required to reproduce our experiments.\n\nWe trust this fully addresses the concerns regarding reproducibility and reiterate our commitment to open science, with a well-documented public release planned upon acceptance.\n\n**[Q6] The authors propose a topology hierarchy…**\n\nWe believe the core principles of THD-BAR are indeed applicable to iEEG, with some important considerations. The BTH concept is highly adaptable to iEEG. In fact, it could be even more powerful in this context. iEEG electrodes are often implanted in structured grids or strips with known anatomical locations relative to specific gyri, sulci, or brain structures (e.g., hippocampus, amygdala). This provides a much more precise and physiologically-grounded basis for defining a hierarchy than non-invasive EEG. One could define scales based on:\n\n- **Scale 1:** Whole implant region.\n- **Scale 2:** Specific anatomical structures (e.g., all electrodes within the temporal lobe).\n- **Scale 3:** Individual electrode grids or strips.\n- **Scale 4:** Small clusters of adjacent electrodes.\n- **Scale 5:** Individual electrode contacts. This would allow THD-BAR to model the propagation of neural activity (e.g., a seizure wavefront) across precise anatomical pathways in a hierarchical manner.\n\nThe main challenge would be the high degree of inter-subject variability in iEEG implant locations. Unlike the standardized 10-10 system for scalp EEG, iEEG montages are patient-specific and clinically driven. This would require a more flexible, perhaps automated, method for generating the BTH for each individual subject based on their specific electrode coordinates and anatomical imaging data."}]

Author rebuttal:

Meta-review: In their paper, the authors introduce a new model called the Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE). This model uses a Brain Topology Hierarchy (BTH) to organize EEG channels in a multi-scale spatial order, which allows for multi-scale tokenization. The authors also developed an enhanced Brain Autoregressive (BAR) module that uses specialized masking strategies for more accurate prediction.

All the reviewers are positive about the contributions of the papers, including: (1) the novelty of the proposed methods; (2) the experiments are sufficient to support the claims about the performance of the proposed methods; (3) the paper is written and presented clearly. After the rebuttal, most of the concerns of the reviewers were addressed, and all the reviewers are happy with the current stage of the paper.

In my opinion, the contributions and originality of the proposed methods are sufficient for acceptance at NeurIPS. Therefore, I recommend accepting it in its current form. I encourage the authors to address the reviewers’ suggestions and integrate their feedback into the camera-ready version of their paper.
Decision: Accept (poster)