Step-Aware Residual-Guided Diffusion for EEG Spatial Super-Resolution
Authors: Hongjun Liu
Leyu Zhou
Zijianghao Yang
Chao Yao
Venue: ICLR 2026 Poster (2026)
Keywords: EEG spatial super-resolution; Conditional Diffusion Model; Multi-channel Time Series Generation
Primary area: learning on time series and dynamical systems
TL;DR: We introduce SRGDiff, a step-aware residual-guided diffusion model that formulates EEG spatial super-resolution as dynamic conditional generation.

Abstract: For real-world BCI applications, lightweight Electroencephalography (EEG) systems offer the best cost–deployment balance. However, such spatial sparsity of EEG limits spatial fidelity, hurting learning and introducing bias. EEG spatial super-resolution methods aim to recover high-density EEG signals from sparse measurements, yet is often hindered by distribution shift and signal distortion and thus reducing fidelity and usability for EEG analysis and visualization. 
To overcome these challenges, we introduce SRGDiff, a step-aware residual-guided diffusion model that formulates EEG spatial super-resolution as dynamic conditional generation.
Our key idea is to learn a dynamic residual condition from the low-density input that predicts the step-wise temporal and spatial details to add and uses the evolving cue to steer the denoising process toward high density reconstructions.
At each denoising step, the proposed residual condition is additively fused with the previous denoiser feature maps, then a step-dependent affine modulation scales and shifts the activation to produce the current features. 
This iterative procedure dynamically extracts step-wise temporal rhythms and spatial-topographic cues to steer high-density recovery and maintain a fidelity–consistency balance.
We adopt a comprehensive evaluation protocol spanning signal-, feature-, and downstream-level metrics across SEED, SEED-IV, and Localize-MI and multiple upsampling scales. 
SRGDiff achieves consistent gains of up to 40\% over strong baselines, proving its superiority in the task of EEG spatial super-resolution. Moreover, topographic visualizations comparison and substantial EEG-FID gains jointly indicate that our SR EEG mitigates the spatial–spectral shift between low- and high-density recordings. Our code is available at https://github.com/DhrLhj/ICLR2026SRGDiff.

Published as a conference paper at ICLR 2026
STER-AWARE RESIDUAL-GUIDED DIFFUSION FOR EEG SPATIAL SUPER-RESOLLU RESOURRNNO
Hongjun Liu
Leyu Zhou
Zijianghao Yang' Chao Y
'Schoo of Intelligence Science and Technology, University of Science and Technology Beijing
Sccooo of Computer and Communication Engineering, University of Science and Technology Beijing
ABSTRACT
For real-world brain-computer interface (BCT) applications, lightweight Elec- tnoencephalogrphyy (EEG) systems offer the best cost-deployment balance. How- ever, such spatial sparsity of EEG limits spatial fidelity, hurting learning and in- troducing bias. EEG spatial super-resolution methods aim recover high-density EEG signals from sparse measurements, yet is often hindered by distribution shift and signal distortion and thus reducing fidelity and usability for EEG analysis and visualization. To overcome these challenges, we introduce SRGDiff, step-aware sosidual-guided diffusion model that formulates EEG spatial super-resolution as dynamic conditional generation. Our key idea is to learn dynamic residual con- dition from the low-density input that predicts the step-wise temporal and spatial details to add and uses the evolving cue to steer the denoising process toward high density reconstructions. At each denoising step, the proposed residual con- dition is additively fused with the previous denoiser feature maps, then step- dependent affine modulation scales and shifts the activation to produce the cur- rent features. This iterative procedure dynamically extracts step-wise temporal rhythms and spptral-tooographiic cues to steer high-density recovery and maintain a fidelit--consistency balance. We adopt a comprehensive evaluation protocol spanning signal-, feature-, and downstream-level metrics across SEED, SEED-IV, and Localize-MI and multiple upsampling scales. SRGDiff consistently achieves higher SNR than the baseline ESTffrmer and STAD among Localize-MI, SEED and SEED-IV datasets, with up to roughly 75% relative SNR improvement the most challenging 8x setting. Moreover, topographic visualizations compar- ison and substantial EEG-FID gains jointly indicate that our SR EEG mitigates the spatial-spectrall shift between low- and high-density recordings. Our code is available at https//ggittbbboooo/DD/////////t
INTRODUCTION
Electroenceephalggrapyy (EEG) is noninvasive technique for monitoring the brain's electri- cal activity, with widespread applications in neuroscience and clinical practice- rerneinganngggggn from crmputco interfaces and epilepsy diagnosis to emotion recognition (Jiang et al., 2025). How- ever, HEG's spatial resolution is inherently constrained by the number of scalp electrodes and the volume-conducctoo effect (Li al. 2025a). High-density (HD) systems with hundreds of channels can mitigate these issues but are costly, cumbersome to deploy, and uncomfortable for extended wear, whereas low-density (LD) setups (e.g., 16 electrodes) are far more practical yet suffer from severe under-samplin bias (Wang et al. 2025). Indeed, as illustrated in Figure the inter- channel activation patterns of 256-channel HD EEG diverge dramatically from those of 16-channel LD EEG, highlighting the strong bias in sparse recordings. EEG spatial super-resolution (SR) has therefore garnered growing attention, with methods that reconstruct high-density EEG from sparse recordings increasingly explored and applied.
Traditionally, EEG spatial super-resolution has relied on direct maauurg techniques that leam an end-to-end mapping from low-density to high-density representations. These methods fall
"Equal contribution.
Corresponning author.

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Published as a conference paper at ICLR 2026
into two main categories: employs convolutional neural networks Transformers upsam- ple LD feature maps into HD ones (Tang et al. 2022), and the other leverages generative adversarial networks-based architectures that synthesize SR EEG signals conditioned on LD imputs (Wang et al. 2024). However, by treating the mapping as a static projection, these approaches often oversimplify the complex, nonlinear inter-electrode dependencies or demand vast amounts of training data and compute, resulting in overly smooth, detail-poor reconstructions that fail to capture true spatial con- sistency. Figure I c) indicates that such feature-mapping methods merely extend LD information, rather than recovering authentic HD channel relationships.
Recently, diffusion models have been widely applied to time-series gen- eration and missing-data imputation (Huang et al.) 2025; Yuan & Qiao, 2024] Li et al. 2025b).
LD BRGG
Galdinnce Informationn
this context, EEG spatial super-resolution can be cast as conditional genera- tion, where LD observations guide the recovery of HD signals. Within this line of work, researchers mainly focused on conditioning strategies through concatenating low-density features with the noise input (Vet-) ter et al. 2024) or using cross- attention between modalities (Wang) al. 2025) as shown in Fig- ure I Ta). While effective in prac- tice, these approaches remain suscep- tible to consistency-fidee fideelity trade- off. Interpolation-oriented SR tends cause distribution shift, making reconstructions adhere too closely the LD observation and deviate from the HD ground truth. Conversely, generation-orientel SR often introduces distortion, producing HD-like content that fails to remain consistent with the LD input.
Noke
U-Net
SR KEG
Dihrcam Made
GT-LD
ESTformer
(4) Existing Static Guidance Strategy
Path Aliganecal
Ratdial
L-Nett
CT-HD
SRGDiI
Sifecim
(b) Our Residual Guidance Strategy
A Spatial Superresotution Scenario
Figure 1: (a) Existing static guidance strategy vs. (b) our residual guidance strategy for EEG super-resolution, and (c) corresponding topographical maps of LD input, ESTformer output, GT HD EEG, and SRGDiff reconstruction.
To tackle these challenges, we introduce Step-aware Residual- -uidded Diffusion (SRGDiff) for EEG Spatial Super-Resolution, which reframes super-resolution a dynamic conditional generation task. The core idea is to estimate the forward-noising residual from low-density channels, and use it as a per-step corrective direction the reverse process. Technically, SRGDiff first encodes the low- density EEG with pre-trained VAE encoder to obtain a compact latent and multi-scale features, and applies forward diffusion to the high-density latent. At each reverse step, a lightweight residual head predicts path residual from the low-density features and uses it directional correction that additively fused with the previous denoising features to form an incremental feature. The feature is then weighted with a step-dependent affine modulation estimated from the low-density features and the timestep embedding, yielding the current denoised features. This loop repeats over timesteps, coupling the low-density nerward-noisine and high high-density reverse-denoising trajectories and progressively steering samples toward the high- high-yensity manifold. Our main contributions in this work can be summarized as follows:
We recast EEG spatial super-resolution as dynamic conditional generation, coupling the LD forward-noising trajectory with the HD reverse-denoising trajectory to balance consis- tency with the LD observation and fidelity the HD target.
We propose dynamic residual guidance paradigm: the path residual estimated from LD inputs serves as per-step directional correction and is fused additively for incremental up- dates, yielding stable, step-aware sampling scheme that remains effective across datasets and a wide range of SR factors.
We establish a three-level evaluation protocol across three datasets, covering signal- level (temporal consistency, spectral fidelity, spatial topology), feature-level (representation quality), and downstream-level (classification accuracy), which provides a comprehensive assessment beyond pointwise error.
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EBG
X
SMIM
diffusion steps
Z
10:0
Z-
noise
Z,
ROX
peior
pribe
diffuusin steps
RESIDUAL DIRECTION
RDM
X
Condirio
guidance
guidance
diffusion steps
Coodhine
Encoder
Decoder
2,
2,
2
D
E
noise
(a) Overall Architecture
(b) Illustration of ResiiduallGuide Diffusion
Figure 2: SRGDiff overview. (a) Overall architecture: Low-density EEG X conditions the latent reverse process. RDM predicts a residual direction from X and current decoder features. SMM provides step-aware affine parameters to fuse the residual and modulate activations. (b) Residual learning: At each step, the predicted residual guides denoising, and the residual derived from the forward noising process provides supervision residual loss.
2
RELATED WORK
Diffusion Models for Missing Data Imputation. Diffusion-based models have emerged as powerful framework for time-series imputation, leveraging denoising diffusion processes to recon- struct missing values. Diffusion-TS (Yuan & Qiao 2024) demonstrates interpretable conditional generation across diverse time-series without domain-speciae et al. 2025) further enhances precision and efficiency by first generating coarse estimates of miss- ing values through deterministic interpolation, then conditioning diffusion model on both observed data and these estimates to iteratively refine residual errors. SaSDim (Zhang et al., (202)) introduces self-adaptive noise scaling preserve spatial dependencies within sensor networks, while SADI [Islam et al. 2025) integrate self-attention mechanisms to handle partial data missing.
priors. More recently, RDPI (Liu
EEG Spatial Huper-Resooltton. Early attempts at EEG spatial super-resolution adapted image- based frameworks to reconstruct dense electrode maps from sparse recordings. EEGSR-GAN (Corley & Huang 2018) first applied adversarial training hallucinate missing channels. EST- former (Li et al.) 2025a) then introduced spatiotemporal transformers to model long range depen- dencies across electrodes and successfully capture global patterns. More recent diffusion- based and attention-driven approaches have sought to address these limitations. DDPM-EEG (Vetter et al. 2024) leverages denoising diffusion probabilistic models to iteratively refine spatial patterns. STAD (Wang et al. 2025) tackles this by decomposing matial-temporal interactions into spatial-temporal attention streams. This diffusion-based generative paradigm improves diversity and spectral fidelity compared to GANs, yet their static condition can still lead to distribution drifts and distortion.
Residual Diffusion in Related Domains. Several recent works incorporate residual signals into diffusion models. Ou et al. (0244) synthesize PET from MRI by learning a modality residual un- der prior information, i.e., static cross-modal gap that conditions generation. Zhhu al. (2024) reconstruct event-driven video by predicting temporal residuals with inter-frame differences as the generation target to recover dynamics. Mao al. (2025) address medical segmentation by learn- ing residual-to-pric that corrects a coarse segmentation, improving calibration and efficiency. These designs either treat the residual as a fixed target/offset or inject it once as a global prior, with weak coupling to the step-by-step reverse dynamics. In contrast, method targets EEG spa- tial ruper-tesolution and introduces a dynamic, step-aware residual direction that is re-estimated at every reverse step from the LD observation, and the timestep embedding.
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3 PRELIMINARIES AND DYNAMIC CONDITIONAL FORMULATION
Data and Latent Space. Let X E gCenghenghh and X E x denote low- and high- density EEG with C# > Cr. A pre-trained VAE encoder E maps signals to latent space = E(X") and feature extractor F provides LD features as condition generation. In practice, we reuse the VAE encoder as the feature extractor to obtain condition c.
F(X²) to condition
Forward Difftusion on the HD Latent. We corrupt the HD latent with a standard Gaussian forward process
q(2 23-1) M(vai 2-11, (1 (1- a.).).
t= T
(1)
Dynamic Conditional Reverse Process. In the reverse generation stage, sampling begins from an isotropic Gaussian noise initialization êт (o0, /). and the model iteratively predicts - from & until recovering the final latent representation &n. Unlike conventional diffusion models that rely solely on base denoiser, SRGDiff explicitly conditions each reverse step on low-density EEG observations, thereby coupling the LD forward-noising trajectory with the HD reverse-denoising trajectory. The reverse denoiser is defined as:
(6 C) = -M( Ho (() + (o,c, t)) B.1).
base denoiser
dynarnic conditional update
Here, the base denoiser (0(2,, is implemented as U-Net that estimates the noise component. To further enhance temporal fidelity and spatial coherence, we augment the base denoiser with two lightweight modules that inject step-wise conditional guidance from LD features:
Residual Direction Module (RDM). each timestep, RDM predicts path residual ro(e, from the LD features and applies it as a directional correction:
BRMM &, + (c,
Step-Aware Modulation Module (SMM). SMM calibrates the residual update with timestep- aware affine modulation. Specifically, it predicts scale " and bias 33 from LD features and the timestep embedding, and applies them to the residuul-corrected state:
SMM Th C
RDM B
Together, the pair (ra. 7to 9t) realizes dynamic conditional generation: at every denoising step, LD features provide both directional residual and step-dependent modulation strength, yielding stable and temporally consistent correction of the reverse diffusion process.
PROPOSED METHOD
This section presents the step-aware residual-guided diffusion framework, and outlines its architec- ture and core components. SRGDiff reframes SR as dynamic conditional generation that couples the LD forward forward-noising noisinn trajectory with the HD reverse-denoising trajectory, using an LD-estimated residual as per-step corrective direction and step-aware calibration to modulate its strength. The framework comprises four parts: the latent diffusion model backbone, Residual Direction Module (RDM) for additive directional updates, Step-Aware Modulation Module (SMM) for step-dependent modulation, and the overall training strategy. An overview is provided in Figure 2 and the following subsections describe each component in detail.
4.1 LATENT DIFFUSION MODEL BACKBONE
Our backbone consists VAE that builds the latent space and a denoising U-Net that performs diffusion in that space. The VAE follows the EEG autoencoding setup of Aristimunha et al. 2023
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We train secoder-decodee D) on HD EEG X to obtain E(X") and 8" =()), and optimize a pegpossttucctio-rrgggllaiiiaiiii objective
LVAE 1| - =21 Aspee (STFT(X") )llT + +xK KLL KL [I,((0. (5)
where S-FT(-) denotes the short-time Fourier transform applied along the temporal dimension of each ERG channel to encourage spectral fidelity.
Empirically, set the spectral weight 0.1 and the KL weight to 10-4, After convergence, (E, D) are frozen. On top of this latent space, we adopt latent-diffusion model in the style of Rombach al. 2022).
4.2 RESIDUAL DIRECTION MODULE
In the context of EEG spatial SR, most diffusion approaches condition the U-Net via feature concate- nation or cross-attention. We instead turn EEG spatial SR into finer and step-aware conditioning by learning residual direction from low-density recordings. We the VAE encoder extract multi- scale condition e and an RDM head Rg takes (c, r()) to predict a residual Res; in the encoder feature space, which acts as a per-step directional correction the reverse process. Concretely, first sample a timestep f, encode the HD EEG to obtain the latent 20 = E(X"), )), and draw
21
Vān 20
VI- a.c,
e~ C0,,
In the forward process, we obtain at each step the noise-corrupted latent of the HD EEG and use this sequence step-dependent features as supervision targets for the residual. The residual labels span t= 0, T and are defined as òz, 20 Z,,--
In the reverse process, we estimate dz, from the low-density EEG to supply the step-wise temporal and spatial details required for denoising. We introduce lightweight convolutional predictor Rg that takes the timestep embedding r(f) and LD features c = F(XL) as input and outputs the residual feature Res,, and trained by
T
Res: = Rx(T((), c), Lres
Res: 5zel2²
(7)
Finally, the predicted residual feature is then added to & as an incremental update:
RDD
Layer + Rest-
(8)
4.3 STER-AWARE MODULATION MODULE
After obtaining 2 3RDM we further modulate the current step to control the extent to which the resid- ual condition infuuences denoising. To enforce temporal fidelity, SMM explicitly weighted the cur- rent diffusion timestep with step-dependent affine modulation estimated from the low-density fea- tures and the current timestep embedding.
Specifically, SMM first encodes the low-density EEG through a lightweight ID convolutional net- work ESMa to produce a feature map he- To enable the conditioning to recognize the current diffusion step, SMM maps each sampled timestep t into sinusoidal time embedding A learn- able weight oe that decays linearly with t balances these two streams, yielding fused feature:
Tre onh + az)ec aj F.MM (c) + ot)ee.
For spatial coherence, we adopt an affine calibration mechanism. The fused feature hy passed through two MLPs M LPP, and M LP3 to predict channel-wist scale 7p and bias 8f:
SSMM %
RDM + g MLP, (he,t) @ 2RDM + MLPP (hr, O 2
(10)
Finally, SRGDiff feeds the updated latent & into the U-Net decoder to obtain the next denoised state 2-
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4.4 TRAINING STRATEGY
To stabilize optimization and decouple latent representation learning from conditional diffusion modeling, we adopt two-stage training strategy.
Stage 1: VAE Pre-training, We first train the VAE encoder-decoder pair on high-density EEG data obtain stable and structured latent space. After convergence, the VAE parameters are frozen to provide fixed latent representations for subsequent diffusion modeling.
Stage 2: Residual-Guided Latent Diffusion. On the frozen latent space, the final training objective is a weighted combination of the three terms:
Csuge 2 Ee.. e || co(z,, t,0) + Ares
(11)
Empirically, we set the residual weight to and the SMM weight to 10-2. The term ASMM (Ih 112 + [13: serves as a regularization component to prevent excessively large values of % and Br, therehy stabilizing the training dynamics.
EXPERIMENTS
5.1 DOWNSTREAM DATASETS
In this study, we employ three publicly available EEG datasets. The SEED dataset (Zheng & Lu 2013) uses 15 film clips of approximately four minutes each as emotional stimuli to induce stable and continuous emotional responses three categories: positive, neutral, and negative. Data were acquired via 62 channels at 1000 Hz. downsampled to 200 Hz, band-pass filtered (0-75 Hz) and segments with faulty sensors removed. SEED-IV (Zheng et al., (018)) extends SEED by using the same 15 subjects and 62-channel setup (1000 Hz) but adds music and image stimuli to evoke hap- piness, sadness, fear and neutral states; preprocessing mirrors that of SEED. Localize-MI (Mikulan) epilepsy patients, where
ctt al. 2020) contains 61 presurgical sessions from seven drug-resistan 256-channel scalp EEG was recorded at 8000 Hz during 0.1-5 mA intracerebral single-puls, pulle stim- ulation; preprocessing includes 0.1 Hz high-pass filter, notch filter, bad-channel/trial removal and alignment trials to the -300 ms to +50 ms stimulus-artife window.
5.2 EXPERIMENT SETUP
Data Preprocessing. The experimental setup for the EEG super-resolution task follows the ES- Tformer and STAD frameworks. The preprocessed EEG signals were segmented into fixed-length windows:
continuous, non-overlapping 4-second windows for SEED and SEED-IV datasets, and 260 ms windows (from 250 ms before stimulation to 10 ms after) for Localize-MI. In SEED and SEED-IV, we designed different super-resolution scale factors (2x, 4x and 8x) to evaluate recon- struction performance. The selection of visible channels and the super-resolution scaling factors follow the configurations used in ESTformer. For Localize-MI, due to the high channel density, applied more extensive scale factors (2x, 4x, 8x, 16x).
Training& & Environment Settings. For each dataset, we split the data into train/test with an 80%/20% ratio and reserve 10% of the training portion as validation set. Stage is trained only on the HD signals from the training split. Stage II is trained on paired LD/HD samples constructed from the same training split by masking HD channels according to the target SR scale; the validation set is used for early stopping and hyperparameter selection. The held-out test split is used once for final reporting, with fine-tuning.
Baselines. We compare SRGDiff with strong EEG SR and time-series imputation baselines: ES- Tformer (Li et al. 2025a and STAD (Wang et al. 2025) (transformer//diffusss based EEG SR), DDPMEEG (Vetter al. 2024) (diffusion for ECoG SR), SaSDim (Zhang et al. 2024) and SADI [Islam et al. 2025) (advanced missing data imputation), and the two-stage residual method RDPI LLu et al. 2025). We use authors' official implementations when available and otherwise provide

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Published as a conference paper at ICLR 2026
Model
Ref
Metric
2
16
NMSE
0.2675:10.00%
0.3427:10.00
0.4174:10004
0.4613-:0.003
SaSDim
UCAI 2024
PCC
0.819440.00 002
0.7246+0.000
0.6926=20.00 003
0.6476=00.02
SNR
5.7443+t0.007
4.3796=t0.008
3.5549::00009
2.7678=0.003
NMSE
0.2637.10.00%
0.3442.10.001
0.4164.10.004
0,456610.000
SADI
AAAI 2025
PCC
0.8243+00.M .002
0.7391 #00007
0.6944+0.003
0.6554=0.000
SNR
5.7511#0.007
4.3724=0.000
3.5498=0.008
2.8942=00009
NMSE
0.2561:10.00
0.3562110.001
0,4076:10.004
0.4531 1.0003
RDPI
AAAI 2025
PCC
0.8246:t0.00 002
0.7396=+0.00
0.7062=0.000
0.6549=0.008
SNR 5.7311#0.007
4.3966#00000
3.5643
2.7731
NMSE 0.2046110003
0.310810.001
0.355410.004
0.4076:40.002
DDPMEEG
Patterns 2024
PCC
0.8516:#00002 0.8163+40.007
0.7306:10.u03
0.6739:10.003
SNR 6.2151+00000
5.5126:t0.003
3.9891:0.009
3.2715:t0.00s
NMSE 0.272110.00%
0.3578110.001
0.4466:10.004
0.4837:10.002
ESTformer
KBS 2025
PCC
0.806110.000
0.7205 100.007
0.6867100000
0.631910.000
SNR 5.5403:10.00
3.8671 1.0.03
3.3023
2.5671 10.000
NMSE
0.1902.1..00
0.3067 100000
0.3649
0,410610.000
STAD
TCE 2025
PCC
0.8635110.000 0.8194.10.007
0.7216.10.003
0.669410.002
SNR
7.2591 5.5234.10.008
3.8715.10.009
3.2642.10.005
NMSE
0.1449:10.003
0.2384-10.001
10057710.004
0.3457:10.022
SRGDiff
OURS
PCC 0.9213
0.8854110.007
0.832310.003
0.7322:10.002
SNR
8.3755:10.008
6.3617:10.003
5.2249:10.009
4.0197-00006
Table I: Performance of all methods Localize-MI across different channel settings.
carefully verified reimplementations, applying their recommended hyperparameters and unifying training epochs and sampling steps across methods.
5.3 EVALUATION PROTOCOL.
We assess SR quality at three complementary levels to balance faithfulness to the ground truth, preservation of heurophysiological structure, and practical utility.
Signal level (does the waveform match?): We follow ESTformer and report normalized mean squared error (NMSE), Pearson correlation coefficient (PCC), and reconstruction signal-to-noise ratio (SNR) with respect to the HD reference, plus topology maps for qualitative inspection (formal definitions in the Appendix).
Feature level (does the representation distribution match?): We adopt EEG-FID following Lai (et al. (2025), using a frozen EEGNet trained per dataset on its training split; the embedding di- mension is 256 for SEED/SEED-IV -VV and 512 for Localize-MI. In addition, report frequency- domain MAE: we first compute channel-wir -wine STFTs of the reconstructed and reference HD EEG, form their power spectra, and then compute the normalized mean squared error between these spec- tra averaged over channels and frequency bins, so as to capture spectral distortions that are not reflected by time-domain NMSE alone.
Downstream level (is it useful?): We evaluate SEED/SEED-IV subject-dependent emotion recog- nition without cross-validation and binary epileptic classification on Localize-Mi both reporting accuracy. All results are summarized as std over subjects; implementation details and metric formulas are provided in the Appendix.
5.4
MAIN RESULTS
We report signal-level reconstruction quality in Tables I and For the most demanding 16x up- sampling on Localize-Mo SRGDiff attains an NMSE of 0.3457, over 15% lower than DDPMEEG's 0.4076, indicating that dynamic conditioning effectively guides the diffusion model to generate
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SEED (62)
SEED-IV (62)
Model
Metric
4
2
NMSE
0.4399.1..04
0.6234:0002
0.7767
0.3633
0.5543-10.02
0.7122::00055
SaSDim
PCC
0.7041=00002 0.5649
0.4349:+0.00
0.7249
0.600
0.5009::0.00
SNR
4.115420.09
2.2940=0046
1.1349=:0:27
4.5940=0000
2.6004:00004
1.621 1.6211 10.111
NMSE
0.4439:0.004 0.604900000
0.8106200.07
0.3557
0.5349:00.02
0.6844100005
SADI
PCC
0.7234.10.02 0.581910000
0.4064110.00
0.7624
0.6293:10.009
0.5243-1:0.08
SNR
4.2419:0.097
2.5160
1.0137:0..27
4.7093
2.0044:10.004
1.6610+0.12
NMSE
0.4064:10.044
0.6134+0002
0.7916:40.07
0,3491+0004
0,5416::0.02
0.6915
RDPI
PCC
0.7.16:00.02
0.57
0.4216:0.000
0.7861 2:002
0.6316:00.09
0.5164
SNR
4.2019
2.0160
1.0316:20.17
4.7190
2.6194:10.04
1.6492
NMSE
0.4916.10.004
0.7319100002
0.8634110.007
0.5136::0004
0.6513:10.00
0.7916110.00
DDPMEEG
PCC
0.6941
0.5134=0001
0.3419
0.7346=0001
0.5316:20.09
0.4305
SNR
0.9999
1,5391 #:0042
20:125
4.4165
2. 2.106::0.003
1.6105:20.110
NMSE
0.008
0.048
0.4149+00.07
0.3448+0000
0.391 +00.005
0.5125
ESTiormer
PCC
0.8368
0,8012
0.7070=100004
0.8106
0.7822
0,7048
SNR
5.0560=#0.09
4,5838
1.8871
4.7535 +0.008
#.1.33:00.08
2.9821 +:0.1
NMSE
0.4319=00.04
0
0.867
#0.007
09.004
0..013
0.7193
STAD
PCC
0.71 36:20.002
0.49462000
0.344 20.004
0,7316
0.5219
0.4319
SNR
0.9999
1 13.49
0.9134:10125
4.4930=0.008
2.0492:10.003
1.6193:20.114
NMSE
0.1632:10.004
0.207710000
0.3494.00.007
10.004
0.2115 :100002
0.2603-10.005
SRGDiff
PCC
0.9102:00000
0.8445
0.8167 100004
+..012
0.80-46:10.009
0.8210
SNR
7.8413:20.097
5,2606200043
4.5912 1.5
7.8660:00008
6.6402:10.03
6.0346:::0.200
Table 2: Performance comparison of different models on SEED and SEED-IV datasets across dif- ferent channel settings.
SEED
SEED-IV
MI
180
180
180
150
150
150
120
120
FID
90
90
60
60
60
30
30
30
2
Scale Factor
Scale Factor
Scale Factor
OURS
ESTformer
STAD
Figure 3: EEG-FID evaluation across three datasets compared with ESTformer and STAD.
super-resolved signals that closely approximate the true high-density data. Its PCC improves from 0.6739 to 0.7322, and its SNR increases from 3.27 dB to 4.02 dB (over 22%). demonstrating that under challenging settings the dynamic conditioning still learns the HD trend while maintaining favorable signal-to-noise ratio. On SEED with high temporal variability and frequent outliers, SRGDiff reduces the 2x NMSE from ESTformer's 0.3288 to 0.1632 (a reduction of more than 50%) and raises PCC from 0.8368 0.9102. A similar pattern is observed on SEED-IV, where NMSE drops to 0.1663 versus 0.3448 and PCC increases to 0.9113 versus 0.8106, indicating that despite lower SNR, the dynamic conditioning exhibits strong generalization.
5.5 FEATURE-LEVEL RECONSTRUCTION EVALUATION
We report EEG-FID results in Figure 3 and our method consistently achieves the lowest FID scores across SEED, SEED-IV, and Localize-MI datasets under different scale factors. These results indi-
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OURS
ESTformer
STAD
GT
OURS
ESTformer
STAD
1R
Power (415)
LR-16
LR
10
2
(a) SEED
(b) SEED-IV
Figure 4: Visualization of EEG topographic maps between ground-truth and reconstructed EEG signals by ESTformer, STAD and SRGDiff
SEED
SEED-IV
Localize-Mo MI
Model
2x
4x
8x
2X
4x
8x
2x
4x
8x
16x
ESTformer
6.96
9.31
9.73
7.11
8.30
8.86
7.03
16.67
32.32
35.73
STAD
9.19
11.04
14.40
9.50
10.95
13.12
8.76
13.11
22.53
25.37
SRGDiff
3.89
5.12
4.95
3.99
4.08
4.84
3.86
7.30
11.50
13.76
Table 3: Frequency-domain MAE between reconstructed and real HD topomaps on SEED, SEED-
IV, and Localize-MI under different SR factors.
cate that our approach generates EEG signals that are statistically closer to the real distribution in temporal domain.
We further analyze the spectral fidelity of generated signals by visualizing EEG topographic maps under different scale factors. An EEG topographic map projects the power spectral density (PSD) of each channel onto the scalp surface, providing an intuitive representation of the spatial distribution of oscillatory energy. As shown in Figure 4 and Figure [5] although our reconstructed signals still exhibit minor deviations from the original data, they preserve high degree of overlap in critical regions with strong PSD responses.
Beyond qualitative inspection, also report frequency- domain error metric that quantifies the mean absolute error between reconstructed and real HD topomaps. As shown in Table B SRGDiff consistently achieves the lowest frequency-domain MAE across datasets and SR scales, indicating better preservation of the spatial distribution of spectral power.
5.6 DOWNSTREAM TASKS
Table reports results on the three datasets under various super-resolution scales. As the seale grows, accuracy for both raw and super-resolved inputs declines, yet SRGDiff's reconstructions consistently maintain a clear advantage. In particular, SRGDiff greatly outperforms missing-val valuue imputation methods and leads all other spatial imputation approaches. At 2x scale factor, its clas- sification accuracy approaches that obtained from the original full-channel recordings. We further compared the runtime efficiency of different methods as shown in Table 13 Although our proposed SRGDiff is slightly slower than the transformer-bast resolution within 0.1s, which meets the real-time requirement in practical applications. The detailed runtime statistics of all models are provided in the Appendix.
ESTformer, it can still complete EEG super-
5.7 ABLATION STUDY
To evaluate the contribution of each module in SRGDiff to EEG super-resolution reconstruction, we conducted ablation studies comparing SRGDiff with three variant models. LDM+LD keeps only

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SEED
SEED-IV
Localize-MI
Method
2
8
GT
0.7152
0.7152
0.7152
0.7027
0.7027
0.7027
0.8368
0.8368
0.8368
0.8368
LR
0.4981
0.4702
0.4424
0.5685
0.5618
0.5011
0.7208
0.6534
0.5219
0.3862
SaSDim
0.5097
0.4793
0.4429
0.5794
0.5692
0.4912
0.7193
0.6519
0.5237
0.3845
SADI
0,5137
0.4834
0.4456
0,5718
0,5644
0.4987
0.7215
0.6634
0,5314
0.3957
RDPI
0,5044
0.4802
0,4531
0.5591
0.5741
0.5071
0.7230
0.6624
0.5210
0.3892
DDPMEEG
0.4738
0.4610
0.4238
0.5548
0.5487
0,4838
0.7015
0.6387
0,5187
0.3767
ESTformer
0.6887
0.6509
0.6057
0.6782
0.6500
0.5084
0.7445
0.6033
0.4739
0.4391
STAD 0.5437
0.5249
0,4610
0.6651
0.6410
0.4977
0.7589
0.6797
0.6344
0,5384
SRGDiff
0.7019
0.6812
0.6273
0.6821
0.6558
0.5127
0.7641
0.7163
0.6806
0.5887
Table 4: Classification accuracy comparison across different methods and datasets. GT represents the ground truth performance. Best results are shown in bold, second-best are underlined.
LDM-LD
LDM-SMM
LDM-RDM
SRGDIff
1.0
1.0
0.9
0.8
7
0.8
NMSE
6
0.6
PCC
0.7
SNR
5
0.4
0.6
0.5
0.2
2
0.4
0.0
0.3
2
4
8
2
8
4
Scale Factor
Scale Factor
Scale Factor
Figure 5: Ablation study performance comparison between SRGDiff and three variant models on the SEED dataset.
the VAE-DDIM backbone and takes the LD EEG its input condition; LDM+SMM preserves the Step-aware modulation module; LDM+RDM retains the Residual Direction Module. All models were tested under the same experimental settings.
5 reports NMSE, PCC and SNR across 2x, 4x and 8x upsampling in SEED dataset. At 8x, adding SMM the baseline NMSE from 0.86 to 0.45 with 47% reduction and boosts PCC from 0.34 to 0.69, demonstrating its effectiveness in temporally aligning the denoising trajectory. Incor- porating RDM yields a comparable NMSE reduction with 44% and raises PCC 0.67, highlighting its role in injecting prior information for spatial consistency. When combined in SRGDiff, these modules further decrease NMSE 0.34 with 60% overall reduction and elevate PCC to 0.81. More ablation results in SEED-IV and Localize-Mi datasets shown in Figure in the Appendix.
CONCLUSION
We introduced SRGDiff, a step-aware residual-guided diffusion model that reframes EEG spatial super-resolution as guided HD generation with step-aware residual direction and adaptive mod- ulation. Across SEED, SEED-IV, and Localize-MI, SRGDiff consistently improves signal-level metrics, achieves the best EEG-FID across scales, and better preserves spectral and scalp topogra- phies. Downstream evaluations further show higher accuracy on emotion recognition and patient- wise classification, indicating that the reconstructed signals are not only visually and statistically closer HD EEG but also more useful for analysis. These results validate that explicit, step- wise conditioning on sparse inputs both necessary and effective for figh-iidelity, appoloyy-presenvee EEG super-resolution.
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7 ACKNOWLEDGEMENTS
This work was supported by the National Key Research and Development Program of China (Grant No. 2022ZD01 1000018000 18001), the National Natural Science Foundation of China (Grant Nos. U22A2022, 62332017), and was conducted in part at the MOE Key Laboratory of Advanced Materials and De- vices for Post-Moore Chips, the Beijing Key Laboratory Big Data Innovation and Application for Skeletal Health Medical Care, and the Beijing Advanced Innovation Center for Materials Genome Engineering.
ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. In this study, no human subjects or animal ex- perimentation was involved. All datasets used, including SEED, SEED-IV and Localize-MI, were sourced in compliance with relevant usage guidelines, ensuring violation of privacy. Details could be found in Appendix. We have taken care to avoid any biases or discriminatory outcomes in our research process. No personally identifiable information ducted that could raise privacy or security concerns, We are committed to maintaining transparency and integrity throughout the research process.
used, and no experiments
REPRODUCIBILITY STATEMENT
We have made every effort to ensure that the results presented in this paper are reproducible. All code and datasets have been made publicly available an anonymous repository to facilitate repli- cation and verification. The experimental setup, including training steps, model configurations, and hardware details, is described in detail in the paper. We have also provided a full description of SRGDiff, to assist others in reproducing our experiments.
We believe these measures will enable other researchers to reproduce our work and further advance the field.
REFERENCES
Bruno Aristimunha, Raphael Yokoingawa de Camargo, Sylvain Chevallier, Adam G Thomas, Oeslle Lucena, Jorge Cardoso, Walter Hugo Lopez Pinaya, and Jessica Dafflon. Synthetic sleep eeg sig- nal generation using latent diffusion models. In DGM4H 2023-Ist Workshop Deep Generative Models for Health at NeurlPS 2023, 2023.
Isaac Corley and Yufei Huang. Deep eeg super-resolution: Upsampling eeg spatial resolution with generative adversarial networks. In 2018 IEEE EMBS International Conference on Biomed- ical Health Informatics (BHT), pp. 100-103, 2018. doi: 10.1109/BH1.0018883333 8333337..
Yu-Hao Huang, Chang Xu, Yueying Wu, Wu-Jun Li, and Jiang Bian. Timedp: Learning to generate multi-domain time series with domain prompts. In Proceedings of the 39th AAAI Conference on Artificial Intelligence, 2025.
Mohammad Rafid U1 Islam, Prasad Tadepalli, and Alan Fern. Self-attention-bart diffusion model for time-series imputation in partial blackout scenarios. In AAAI, volume 39, pp. 17564-17572, 2025. URL wal.https::.. //ttts:://oi...o org/ 1609666988000 aaai. v39117.3393
Weibang 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. In The Thirteenth International Conference on Learning Representations, 2025.
Yongfan Lai, Jiabo Chen, Qinghao Zhao, Deyun Zhang, Yue Wang, Shijia Geng, Hongyan Li, and Shenda Hong. Diffusets: 12-lead eeg generation conditioned on clinical text reports and patient- specific information. Patterns, 2025.
Dongdong Li, Zhongliang Zeng, Zhe Wang, and Hai Yang. Estformer: Transformer utilising spa- tiotemporal dependencies for electroencephalogram super-resolution. Knowledge-Bac Based Systems, 317:1 13345, 2025a.
11

---

Published as a conference paper at ICLR 2026
Yang Li, Han Meng, Zhenyu Bi, Ingolv T Umnes, and Haipeng Chen. Population aware diffusion for time series generation. Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 18520-185222 18529, 2025b.
Zijin Liu, Xiang Zhao, and You Song. Rdpi: refine diffusion probability generation method for spatiotemporal data imputation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 12255- 12263, 2025.
Fuyou Mao, Beining Wu, Yanfeng Jiang, Han Xue, Yan Tang, and Hao Zhang. Prior-guided residual diffusion: Calibrated and efficient medical image segmentation. arXiv preprint arXiv:2509.01333., 2025.
Ezequiel Mikulan, Simone Russo, Sara Parmigiani, Simone Sarasso, Flavia Maria Zauli, Annalisa Rubino, Pietro Avanzini, Anna Cattani, Alberto Sorrentino, Steve Gibbs, al. Simultaneous hu- man intracerebral stimulation and hd-eeg, ground-truth for source localization methods. Scientific data, 7(1):127, 2020.
Zaixin Ou, Caiwen Jiang, Yongsheng Pan, Yuanwang Zhang, Zhiming Cui, and Dinggang Shen. A prioriifformation-uuieee residual diffusion model for multi-modal pet synthesis from mri. In Pro- ceedings of the Thirty-Thin International Joint Conference on Artificial Intelligence, pp. 4769- 4777, 2024.
Steven M Peterson, Satpreet H Singh, Benjamin Dichter, Michael Scheid, Rajesh PN Rao, and Bingni W Brunton. Ajile12: Long-term naturalistic human intracranial neural recordings and pose. Scientific data, 9(1::184, 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Yunbo Tang, Dan Chen, Honghai Liu, Chang Cai, and Xiaoli Li. Deep eeg superresolution via correlating brain structural and functional connectivities. IEEE Transactions Cybernetics, 53 (T::441044422 2022.
Julius Vetter, Jakob H Macke, and Richard Gao. Generating realistic neurophyssioogical time series with denoising diffusion probabilistic models. Patterns, 5(9), 2024.
Li Wang, Jungwook Go, and Xiang Chen. An eeg-based emotion recognition model using an inter- action design framework and deep learning. Journal of Mechanics in Medicine and Biology, 24 (02):2440022, 2024.
Shuqiang Wang, Tong Zhou, Yanyan Shen, Ye Li, Guoheng Huang. and Yong Hu. Generative ai enables eeg super-resolution via spatio-temporal adaptive diffusion learning. IEEE Transactions on Consumer Electronics, 71(1):033-1045, 2025. doi: 10. 1..109997C.2200.552843.
Xinyu Yuan and Yan Qiao. Diffusion- TS: Interpretable diffusion for general time series generation.
In The Twelfth International Conference on Learning Representations, 2024. URL https:
/ooee
eww. ew, wwt./ffrot/// hwtp://mwmm4hhhhppppp.m 1099
Shunyang Zhang, Senzhang Wang, Xianzhen Tan, Renzhi Wang, Ruochen Liu, Jian Zhang, and Jianxin Wang. Susdim:self-adaptive noise scaling diffusion model for spatial time series im- putation. In Kate Larson (ed.), Proceedings of the Thirty-Third International Joint Confer- ence on Artificial Intelligence, IJCAI-24, pp. 2561-2569. International Joint Conferences on Artificial Intelligence Organization, 2024. doi: 0..44963/jcaii22224/22 URL https:
//doi.org// 10. 0etpg//...49931111 ca...aiai.............
Main Track.
W. Zheng, W. Liu, Y. Lu, B. Lu, and A. Cichocki. Emotionmeter: A multimodal framework for recognizing human emotions. IEEE Transactions on Cybernetics, pp. 1-13, 2018. ISSN 2168- 2267. doi: 10. 10.110//C 2.218 2018.2797777
Wei-Long Zheng and Bao-Liang Lu. Investigating critical frequency bands and channels for EEG- based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, ((3):162-775, 2015. doi: 10.
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Published as a conference paper at ICLR 2026
Lin Zhu, Yunlong Zheng, Yijun Zhang, Xiao Wang, Lizhi Wang, and Hua Huang. Temporal residual guided diffusion framework for event-driven video reconstruction. European Conference on Computer Vision, pp. 41 411-427. Springer, 2024.
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A LLM USAGE
Large Language Models (LLMs) were used to aid the writing and polishing of the manuscript. Specifically, we used LLM to assist in refining the language, improving readability, and ensuring clarity in various sections of the paper. The model helped with tasks such as sentence rephrasing, grammar checking, and enhancing the overall flow of the text.
is important to note that the LLM was not involved in the ideation, research methodology. or experimental design. All research concepts, ideas, and analyses were developed and conducted by the authors. The contributions of the LLM were solely focused on improving the linguistic quality of the paper, with no involvement in the scientific content or data analysis.
The authors take full responsibility for the content of the manuscript, including any text generated or polished by the LLM. We have ensured that the LLM HLM-generated text adheres to ethical guidelines and does not contribute to plagiarism scientific misconduct.
Figure Example of raw SEED EEG segment with sensor faults highlighted.
B DATASET PREPROCESS DETAILS
B.I DATASET DETAILS
We three publicly available EEG datasets: SEED, SEED-IV, and Localize-MI.
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Figure 7: Example of raw SEED EEG segment with sensor faults highlighted.
consists of recordings from 15 subjects watching emotion-eliciting film clips (= 4 min each) designed to induce positive, neutral, and negative states. Data were acquired with 62-channel 10 20 montage at 1000 Hz, downsampled 200 Hz, and band-pass filtered 0.5 75 Hz. We manually inspected and removed sessions with sensor faults as depicted in Figure 6 and Figure 7
SEED (available at jttp://cmmm...uu..uuu........
sjtu. wwwpe/nnnneeeeeeeeeeeeeeeeeeeeee stel/seed-22 ittiiiil..mml html) is pub- licly available EEG dataset designed for emotion recognition research. It includes four emotional categories: happiness, sadness, neutrality, and fear. Emotional states are elicited using two types of stimuli: music and images. EEG recordings were collected from 15 subjects using 62-channel system with sampling rate of 1000 Hz.During preprocessing, the EEG signals were downsampled to 200 Hz and filtered with bandpass filter ranging from 0.5 to 75 Hz. To ensure data quality, visually corrupted or invalid trials were manually excluded.
SEED-IV (available at Ihttp://bcnt
is a high- density intracranial EEG dataset from seven drug-resistant epilepsy patients during 61 presurgi- cal sessions. Stereo-EEG electrodes delivered single-pulse biphasic currents (0.1 5 mA), and 256 channels were recorded at 8000 Hz. Preprocessing included 0.1 Hz high-pass filtering, notch fil- ters at 50/100/150/200 Hz, chdnnennnel/trial removal, and trial alignment using stimulation artifact peaks (-300 to +50 ms window). the Localize-Mo MI dataset, we designed binary classification task (epileptic nonepileptic) to evaluate the effectiveness of synthetic super-resolution EEG (SR
Localize-MI (available at htttps://do..orge http:://doo...rro..111111///111. 12t.00111711///...... www.ooddeeccc.......
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EEG) in detecting epileptic abnormalities. Specifically, EEG signals recorded before electrical stim- ulation are labeled as nonepileptic, while those recorded during stimulation are labeled as epileptic. The detailed experimental setup follows the description provided in the STAD (Wang et al. 2025) model section.
B.2 MORE EXPERIMENTAL DETAILS
We follow ESTformer and STAD slicing strategies. Preprocessed signals are windowed into fixed lengths: SEED and SEED-IV use non-overlapping 4 segments, while Localize-Mi retains -250 ms +10 ms around each stimulus (260 ms total). We randomly split 80% for training and 20% for testing, yielding 24265 62 x 800 train / 6067 x 62 x 800 test samples for SEED; 29199 x 62 x 800 800 test for SEED-IV; and 1914 256 x 2081 train 479 x 256 x 2081 test for Localize-MI. For SEED and SEED-IV we evaluate 2x, 4x, and 8x super-resolution; for Localize-MI we additionally include 16x. As shown Figure g Localize-MI employs 256- channel intracranial grid, while Figure 9 shows the 62-channe scalp montage used in SEED-IV and SEED.
train / 7300
3D Distribution of EEG Electrodes (n=256)
250
0.125
200
0.100
0.075
0.050
150
Electrode ID Sequence
0.000
-0.025
-0,000
1100
-0.075
0.10
0.05
50
-0.100 -0.100-0.075_000 100.0000.0-00000__pp 0.000
Y
0.025
0.050
0.075
0.100
X
Figure 8: Electrode topology of the Localize-Mi dataset (256 intracranial channels).
C MORE SRGDIFF MODEL DETAILS
C.1 VARIATIONAL AUTOENCODER
In this paper, Variational Autoencoder (VAE) follows the AutoencoderKL design (Aristimunha et al., 2023), comprising convolutional encoder, latent distribution (mean and variance) with KL reg- ularization toward (o I). and a decoder with deconvolutions and upsampling. We augment both encoder and decoder with attention layers (multi-head and non-local attention), residual connec-
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3D Distribution of Standard EEG Channels (62 Locations)
50
0.00
0.04
0.00
mg 88 08 08
0.08 -0.06 -0.04
-0.00
0.22
0.06
X
Figure Electrode topology of the SEED-IV and SEED datasets (62 scalp channels).
tions, and GroupNorm to capture global EEG features while ensuring stable training and efficient latent representations.
C.2 DDIM SCHEDULER
The DDIMScheduler manages noise scheduling and sampling in the forward and reverse diffusion processes. It supports multiple noise prediction types and variance strategies. At each step. it com- putes the noise coefficient, predicts the denoised sample, clips values for numerical stability, and injects random perturbations control output diversity.
D PARAMETER STUDY
D.I VAE LATENT SHAPP SELECTION
We found that the latent shape balances reconstruction precision and generalization. Higher di- mensions capture more detail but risk overfitting, while lower dimensions blur outputs. We experi- mented across the three datasets and selected latent of 32 x 400 for SEED/SEED-IV and 64 x 500 for Localize-MI. which yielded optimal NMSE, PCC, and SNR. Table and Table 6 shows the performance of SRGDiff in different latent shapes.
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Shape
NMSE
PCC
SNR (dB)
Shape
NMSE
PCC
SNR (dB)
64 x 400
0.15
0.93
7.75
x 400
0.16
0.92
7.58
32x400
0.12
0.95
8.72
320
0.13
0.94
8.58
16 x 200
0.20
0.89
6.81
16 x 200
0.21
0.87
6.86
8 400
0.16
0.92
7.26
8 400
0.19
0.91
7.23
SEED
SEED-IV
Table 5: VAE latent shape selection results on SEED and SEED-IV.
Shape
NMSE
PCC
SNR (dB)
128 x 1000
0.13
0.94
8.60
64x500
0.09
0.96
9.01
32 500
0.15
0,92
7.61
32 1000
0.13
0.94
8.62
Table 6: Localize-MI: VAE latent shape selection results
D.2 DIFFUSION HYPERPARAMETERS
D.2.1 DIFFUSION SCHEDULES
We compare linear and cosine noise schedules. Linear adds noise at a constant rate but may cause instability endpoints; cosine offers smoother transitions and better performance for long diffusion chains. We fixed 1000 timesteps with cosine scheduling and evaluated NMSE, PCC, and SNR on the latent reconstructions choose this setting as shown in Table 7
Dataset
Schedule
NMSE
PCC
SNR (dB)
Linear
0.42
0.71
4.18
SEED
Cosine
0.20
0.86
7.15
Linear Cosine
0.51
0.66
4.02
SEED-IV
0.19
0.88
7.24
Linear Cosine
0.14
0.93
8.39
Localize-MI
0.11
0.95
8.88
Table 7: Comparison of noise schedules on three datasets (NMSE, PCC, SNR).
D.2.2 TRAINING TIMESTEP LENGTHS
We also tested different training timestep lengths (200, 1000, 2000). Larger values introduce stronger noise but make denoising harder; smaller values lack coverage of high-noise regimes. Using cosine scheduling, the results in Table TableBexhiibi that 1000 timesteps be optimal across datasets.
D.2.3 COSINE SCHEDULE OFFSET FACTOR
In the cosine noise schedule for DDIM, the offset factor s adjusts the smoothness and starting point of the noise variance curve to prevent instability from overly small initial noise levels. Concretely, s introduces a phase shift in the cosine function, producing more gradual noise increase at early timesteps thherey avoiding abrupt noise jumps-whin still covering the full variance range at later steps. Smaller values of yield gentler initial noise ramp-up. whereas larger accelerate early noise growth. Table depicts the effect of cosine schedule offset factors reconstruction quality.
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Dataset
Steps
NMSE
PCC
SNR (dB)
200
0.32
0.76
5.95
SEED
1000
0.20
0.86
7.15
2000
0.29
0.78
6.19
200
0.36
0.75
5.93
SEED-IV
1000
0.19
0.88
7.24
2000
0.31
0.76
6.11
200
0.15
0.92
8.37
Localize-Mo MM
1000
0.11
0.95
8.88
2000
0.11
0.95
8.94
Table 8: Impact of training timesteps on reconstruction quality
Dataset
NMSE
PCC
SNR (dB)
0.005
0.20
0.86
7.15
SEED
0.010
0.24
0.84
7.02
0.025
0.26
0.83
6.93
0.005
0.20
0.86
7.17
SEED-IV
0.010
0.19
0.88
7.24
0.025
0.20
0.85
7.11
0.005
0.11
0.95
8.94
Localize-MI
0.010
0.15
0.93
8.41
0.025
0.18
0.91
8.23
Table 9: Effect of cosine schedule offset factor on reconstruction quality
D.3 EFFECT OF ARES AND ASMM
To assess the sensitivity of SRGDiff the weighting coefficients in the loss, we conduct a parameter study on the most challenging SR settings (highest SR factor) for each dataset. We vary the residual- guidance weight Ares and the step-aware modulation weight ÀSMM around the default values used in the main paper, while keeping all other hyperparameters fixed.
Concretely, we sweep Ares (0.1, 0.5, 1.0, 2.0, 5.0) (relative to the default), and ASMM (0.001, 0.005, 0.01, 0.1). Tables 10 report the NMSE SEED, SEED-IV, and Localize-Mi under these settings.
A
SEED
SEED-IV
Localize-MI
ASMM
SEED
SEED-IV
Localize-MI
0.1
0.3928
0.3286
0.3941
0,001
0.3975
0.3133
0,3904
0.5
0.3532
0.2822
0.3578
0.005
0.3539
0.2696
0,3519
1.0
0.3494
0.2603
0.3457
0.01
0.3494
0.2603
0.3457
2.0
0.3508
0.2810
0.3565
0.02
0.3513
0.2712
0,3489
5.0
0.4012
0.3369
0.3827
0.1
0.3990
0.3240
0.3915
(a) Effeet of Ase
(b) Effecc of ASMM
Table 10: Effect of Ares and ASMM NMSE (hardest SR setting per dataset).
Overall, the performance is reasonably stable within a broad range around the default values, indi- cating that SRGDiff is not overly sensitive to these hyperparameters. When A becomes too large, the residual guidance term dominates and suppresses learning in the diffusion backbone; when it is too small. the residual guidance has almost no effect. Similarly, if ASMM is too small, the guidance feature modulation is overly strong, whereas for very large AsMm the diffusion model effectively
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SEED
SEED-IV
Localize-MI
Model
2x
4x
8
2x
4x
8x
2x
4x
8x
16x
ESTformer
6.96
9.31
9.73
7.11
8.30
8.86
7.03
16.67
32.32
35.73
STAD
9.19
11.04
14.40
9.50
10.95
13.12
8.76
13.11
22.53
25.37
SRGDiff
3.89
5.12
4.95
3.99
4.08
4.84
3.86
7.30
11.50
13.76
Table II: Frequency-domain NMSE between reconstructed and real HD topomaps on SEED, SEED- IV, and Localize-MI under different SR factors.
ignores the guidance features. The default configuration achieves the best overall trade-off across datasets.
E DOWNSTREAM TASK
E.1 CLASSIFICATION FEATURE EXTRACTION
E.1.1 DIFFERENTIAL ENTROPY FEATURE
Raw EEG at 1000 Hz is downsampled to 200 Hz, band-pass filtered (1-50 Hz) with 6th-order windows (200 samples). Each win- dow is transformed by STFT (Hanning window, 200-point length, 256-point FFT). We compute band-specific power E for 8 (1-3 Hz). 0 (z)7Hz), (8-13 Hz), (14-30 Hz), and y (31-50 Hz). normalize by the number of bins N, and define differential entropy (DE) feature as lEg(E/N) with small constant added for numerical stability.
Butterworth filter, and segmented into non-overlapping
E.1.2 POWER SPECTRAL DENSITY FEATURE
Power spectral density (PSD) feature features use the same STFT pipeline but report the mean squared magnitude (average power) in each band.
E.2 RANDOM FOREST CLASSIFIER
For emotion classification on SEED and SEED-IV and epileptic detection on Localize-MI, we em- ploy random forest with 100 trees. This set of hyperparameters balances nonlinearity modeling with computational efficiency, yielding robust performance on high-dimensional EEG features.
F SUPPLEMENTAL ABLATION RESULTS
F.1 FREQUENCY-DOMATI TOPOMAP ERROR
To complement the qualitative topographic visualizations in Figure and rule out potential visual selection bias, report quantitative frequency-domain MAE between reconstructed and real HD topomaps. Concretely, we first transform both reconstructed and reference HD EEG into the fre- quency domain, aggregate power within standard EEG bands (e.g.. 8, a. 3), and interpolate the band power of each channel onto 2D scalp grid using electrode coordinates. We then compute the pixel-wise normalized mean squared error between the reconstructed and real topomaps, averaged over all frequency bands and test samples. A lower value indicates that the model better preserves both the spectral content and its spatial distribution over the scalp.
Table I reports rrequency-domale MAE on SEED, SEED-IV, and Localize-MI under different SR factors. SRGDiff consistently achieves the lowest error across all datasets and scales, with a larger margin over ESTformer and STAD than in time-domain NMSE/PCC/SNR. This confirms that caidual-guided generative formulation not only improves pointwise reconstruction quality, but also more faithfully recovers the HD spectral-spatial structure.
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F.2 ADDITIONAL ABLATION STUDIES ON SEED-IV AND LOCALIZE MI DATASETS
Figure [0] illuutraaee the LDM+LD baseline used in our ablations. In the first stage (top row), we pretrain VAE on full high-density (HD) EEG: preprocessed HD signals passed through the encoder E and decoder D to learn a latent space tailored to HD scalp topography. In the second stage (bottom row), low-density EEG is first mapped into this latent space using the pretrained encoder E, yielding a static guidance feature. This guidance feature is then added to the diffusion latent during the denoising process, and the final denoised latent is decoded by back to HD EEG, without employing RDM or SMM.
Figure presents additional ablation studies on SEED-IV and Localize-Mi datasets, reporting NMSE, PCC, and SNR for the baseline LDM+LD, LDM+SMM, LDM+RDM, and the full SRGDiff across various upsampling scales. These plots further illustrate the individual and combined contri- butions of our two modules to reconstruction quality.
E
LR EEG
Guidance Feature
E
E
+
D
D
Noise
Diffusion
HR EEG
Figure 10: Illustration of the LDM+LD baseline.
LDM-LD
LDM+SMM
LUM+RDM
SRGDITT
1.0
0.9
Localize-Mi
0.8
NMSE
0.7
SVR
0.6
0.5
0.4
0.0
0.3
2
16
Scale Factor
Scale Factor
Scale Factor
1.0
1.0
0.9
0.8
0.8
SEEDLTA
NMSE
0.6
0.7
SNR
0.4
0.6
0.5
0.2
0.4
0.0
0.3
8
8
2
Scale Factor
Scale Factor
Scale Factor
Figure 11: Ablation results SEED-IV and Localize-MI: comparison of NMSE, PCC, and SNR for LDM+LD, LDM+SMM, LDM+RDM, and SRGDiff at 2x, 4x, and 8x upsampling and 2x, 4x, 8x, and 16x upsampling, respectively.
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G EFFICIENCY ANALYSIS
G.1 COMPUTATIONAL COST UNDER COMPARABLE PARAMETER BUDGETS
To complement the main main-paper results, we further compare the computational cost of SRGDiff with simpler wansformer-basee SR models under comparable parameter budgets. Table 12 reports, for ESTformer, STAD, and SRGDiff, the total number of trainable parameters and the average compu- tation cost per s EEG window (measured as FLOPs under the same input resolution). Although SRGDiff requires more FLOPs than the single-pass transformer ESTformer due to the iterative de- noising process, its parameter count remains in the same order of magnitude as transformer-basee baselines, and the additional cost is the price we pay for exploiting a strong latent diffusion prior.
Method
#Params (M) GFLOPs window
ESTformer 12.111
4.302
STAD
13.949
1.650
SRGDiff 2.342
1.38
Computational cost of different SR models under comparable parameter budgets. We report the total number trainable parameters and GFLOPs per 4 EEG window. SRGDiff uses diffusion-based denoiser, its FL.OPs higher than those of ESTformer, but the parameter budget remains comparable.
Table 12:
G.2 RUNTIME COMPARISON
In addition to static computational cost, we also measure wall-clock runtime for all methods on the three EEG datasets under unified implementation and hardware setup (same GPU, batch size, and input length). Table 13 reports the average per-sample latency for a 4 window. Despite using an iterative denoising process, SRGDiff achieves inference times between 63 ms and 92 ms, which are significantly faster than most diffusion- ESTformer attains the smallest latency thanks its single-pass transformer structure, but, as shown in the main paper, does not match SRGDiff in reconstruction quality. Overall, these results indicate that SRGDiff strikes a favorable balance between accuracy and efficiency, and is suitable for real- time EEG spatial super-resolution.
baselines and are well below 0.1 In contrast,
Method
SEED
SEED-IV
Localize-MI
SasDim
265.4
269.5
374.4
SADI
329.0
324.1
428.7
RDPI
318.1
315.9
421.2
DDPMEEG
549.3
558.2
841.7
ESTformer
3.51
3.55
5.03
STAD
232.6
227.8
385.9
SRGDiff
63.0
62.1
92.5
Table 13: Runtime (ms) of different methods on SEED. SEED-IV, and Localize-MI datasets. Values are average per-sample latency for a 4 EEG window measured on the same GPU. SRGDiff remains below 0.1 in all cases while achieving the best reconstruction quality.
H GENERALIZATION ANALYSIS
H. EXTENSION TO CROSS-SUBIECT AND CROSS-SESSION SETTINGS
EEG signals are known exhibit strong subject- and session-specifie variability. To explicitly examine whether SRGDiff can generalize under this variability, we perform additional experiments on the SEED dataset in stricter cross cross-subject and cross session regime.
For the cross-session setting, we use all subjects in SEED and conduct three experiments: in each experiment, two sessions used for training and the remaining session is held out for testing.
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Model
Metric
Cross-subject (SEED)
Cross-session (SEED)
2x
4x
8x
2x
4x
8
NMSE
0.4411+0.004
0.4729+00.066
0.5633=000008
0.3624++0.004
0.4029:10.008
0.5129=00.07
ESTformer
PCC
0.739540.009
0.7189:10.007
0.6515=0.009
0.79244:.009
0.7742::0.077
0.6954=0.009
SNR
3.9516:0.031
3.5414::0.044
2.7279=0.039
4.8753-:00034
4.409710.047
3.1375=00043
NMSE
0.5537:0.004
0.7325:00.002
0.9267=0.008
0.56171:0.003
0.7675::0.02
0.937620.007
STAD
PCC
0.6224+0.003
0.4587=00.002
0.2959=0.0044
0.6373++0.000
0,4397+00002
0.2791 =0,004
SNR
3.3872++0.097
1.2449++0.049
0.7276+#0. 139
3.1794++0.089
1.1297:00002
0.7168
NMSE
0.2675-+0.003
0.3029=40.055
0.4512:=0.006
0.2480-10.000
0.3529:10.004
0.4127=+0.005
SRGDiff
PCC
0.8023:10.004
0.7232:10.004
0.6902=0.004
0.8508
0.7932:10.003
0.7702+0.007
SNR
5.6913:10.067
4.5657:40.055
4.1189=0.084
6.1473::0.093
4.1857::0.52
3.8189::0.034
Table 14: Cross-subject and cross-session reconstruction performance on SEED under different SR factors (mean + std over folds) terms of NMSE, PCC, and SNR.
We then report averages over all subjects and session splits. For the cross-subject setting, we train SRGDiff on subjects 1-12 and evaluate on held-out subjects 13-15 without any subject-specific fine- tuning.
The reconstruction performance under both reoss-session and cross-subject protocols is summarized in Table We observe that SRGDiff degrades gracefully in the cross-session setting, maintaining strong performance at 2 and 4x SR with more noticeable degradation at 8x, while the cross- subject setting substantially more challenging and leads to larger performance drops across all SR factors than the random division settings. Nevertheless, SRGDiff maintains clear margin over strong baselines ESTformer, STAD in terms of NMSE, PCC, and SNR, indicating that the proposed -ustralooberrattion diffusion formulation is reasonably robust to session- and subject-level variabil- ity on SEED.
H.2 EXTENSION TO ECOG CHANNEL
To examine whether SRGDiff is specific to scalp EEG or can generalize to other multi-channel neu- rophysiological signals, we further evaluate it on an invasive cleetrocortioorrahyy (ECoG) dataset. We use the public ECoG benchmark AJILE12 (Peterson et al. 2022) and follow the setting of Vetter al. (2024). Applying SRGDiff here serves two purposes: (i) tests whether our partial- observation formulation and dynamic residual guidance are modality-agnon spatially organized neural recordings, and (ii) verifies that the proposed method works under different signal regime (invasive BCoG rather than scalp EEG) without any architecture or hyperpa- rameter changes. As shown in Table T5] SRGDiff consistently improves over transformer-based SR baselines the ECoG benchmark, supporting claim that the approach extends beyond EEG to other neurophysiological channel super-resse resertresolution tasks.
within the family of
Model
Metric
2x
4x
8x
NMSE
0.4573
0.7189
0.8517
ESTformer
PCC
0.7367
0.5299
0,3845
SNR
3.3991
1.4334
0.6974
NMSE
0.4932
0.6901
0.7987
STAD
PCC
0.6854
0.5118
0.4312
SNR
3.1686
1.4449
1.1684
NMSE 0.3575
0.6529
0.7312
SRGDifF (ours)
PCC
0.8023
0.5332
0.4502
SNR
4.8913
2.1657
1.9089
Table 15: Channel super-resolution performance (NMSE, PCC, and SNR) on the ECoG dataset from Vetter et al. (204)). following their windowing and data split. SRGDiff consistently improves over transformer- based baselines across all SR factors and metrics.
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Train LD chans
Test LD chans
NMSE
PCC
SNR
16
8
0.4542
0.7196
4.0329
10
0.4031
0.7650
4.4211
12
0,3588
0.8012
4,8705
16
14
0.3245
0.8268
5.1203
16
16 (basc)
0.2977
0.8445
5,2606
8
0.4753
0.6735
3.9518
16
0.3585
0.7820
4,4002
32
32 (base)
0.1632
0.9102
7.8413
Table 16: SEED dataset: robustness of SRGDiff to variable LD montages. The model is trained with either 16- or 32-channel LD configuration and evaluated on subsampled LD inputs at test time without retraining.
H.3 EXTENSION TO VARIABLE AND IRREGULAR LD ELECTRODE LAYOUTS
In many practical deployments, the available low-density electrode layout may differ across sub- jects, sessions, hardware configurations. In addition, real-world recordings often contain missing or corrupted channels, leading to irregular montages that deviate from the nominal LD design. A natural question is whether single model can generalize across such variable and potentially irreg- ular LD layouts.
In SRGDiff, the L.D input treated set of spatially localized observations that first mapped into a common latent representation via the pretrained VAE. Concretely, each LD electrode em- bedded into a continuous scalp (or cortical) coordinate space, and its signal is projected onto fixed latent grid on which the diffusion model operates. The guidance network then consumes these latent features rather than discrete channel indices, so the conditioning is not tied specific LD channel configuration. This design makes the model inherently more flexible to changes in the number and spatial arrangement of LD electrodes at inference time.
To verify this empirically, we conducted two sets of experiments SEED. First, we trained SRGDiff with a 16-electrode I.D configuration and evaluated it at test time on 4/1e/12/14eeeerrodee inputs obtained by subsampling the 16-channel montage. Second, we trained SRGDiff with 32- electrode LD configuration and evaluated it on 8- and 16-electrode inputs, again using only subsam- pling at test time and retraining. As summarized in Tables 16. SRGDiff degrades gracefully as the LD montage becomes sparser: NMSE increases moderately, while PCC and SNR remain com- petitive across all tested LD configurations. These results indicate that single SRGDiff model can handle sparser or irregular LD layouts at inference time without retraining, provided that the new electrodes be embedded into the spatial coordinate system and projected onto the latent grid used during training.
I RECONSTRUCTION VISUALIZATION
Figure 12 through Figure 14 illustrate qualitative reconstructions on the three datasets. For each, plot single representative channel over time, comparing the ground-truth high-density EEG (black) against STAD (blue), ESTformer (red), and SRGDiff (green). These overlays demonstrate SRGDiff's closer alignment with the true waveform across diverse temporal patterns.
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2000
1003
1200
1300
1400
1992
2000
1700
1000
Time Points
Ground Truth (GT)
STAD
ESTformer
OURS
Figure 12: Reconstruction of a representative channel on Localize-MI (motor imagery): ground truth (black), STAD (blue), ESTformer (red), and SRGDiff (green).
420
602
700
Time Points (0-800)
Ground Truth (GT)
STAD
ESTformer
OURS
Reconstruction of a representative channel on SEED-IV (emotion recognition): ground truth (black), STAD (blue), ESTformer (red), and SRGDiff (green).
Figure 13:
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100
200
302
420
500
60c
720
000
Time Points (0-800)
Ground Truth (GT)
STAD
ESTformer
OURS
Figure 14: Reconstruction of a representative channel SEED (emotion recognition): ground truth (black), STAD (blue), ESTformer (red), and SRGDiff (green).
OURS
ESTformer
STAD
GT
LR-128
160
140
LR-64
120
Power (dB)
100
80
LR-32
60
40
LR-16
20
Figure 15: Visualization of EEG topographic maps between ground-trun and reconstructed EEG signals by ESTformer, STAD and SRGDiff on Localize-MI.
26


Reviews:
[{"review_id": "BnnnUprLm5", "reviewer": "Reviewer_7K5e", "summary": "This paper presents SRGDiff, a novel diffusion-based method for spatial super-resolution of EEG that addresses the consistency–fidelity trade-off by dynamically conditioning the reverse process on low-density EEG data using residual guidance. Specifically, the authors propose using the forward-noising residual from low-density channels as a per-step corrective direction in the denoising. To achieve this, a Residual Direction Module (RDM) predicts a path residual for directional correction, and a Step-Aware Modulation Module (SMM) predicts scale and bias for calibration of the residual update. \n\nExtensive experiments on three datasets (SEED, SEED-IV, and Localize-MI) demonstrate that SRGDiff outperforms strong baselines (e.g., ESTformer, STAD, DDPMEEG) across signal-, feature-, and downstream-level evaluations over multiple upsampling scales.", "questions": "- How was the form of the RDM and SMM chosen? Were other forms explored?\n- Is SRGDiff applicable to variable or irregular LD electrode layouts? For example, could a model trained on 16 electrodes generalize to 8-electrode inputs, or would retraining be required?\n- Have the authors experimented with SR tasks beyond EEG to support the claim of generality?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Originality. The idea of dynamically guiding each denoising step with a residual derived from the LD forward process is novel and conceptually distinct from prior approaches that rely on static conditioning.\n\nQuality. The method is comprehensively evaluated across three public datasets and multiple SR scales, covering signal-, feature-, and downstream-level tasks. Ablations demonstrate the importance of both RDM and SMM.\n\t\nSignificance. SRGDiff consistently achieves superior performance measured by NMSE, PCC, and SNR compared to strong baselines. The improvements are meaningful and practically relevant for EEG-based BCI applications.\n\nClarity. The paper is well-structured, concise, and coherent. Figures effectively illustrate the model components and results.", "weaknesses": "- The authors claim that SRGDiff is transferable to general SR settings, while all experiments are restricted to EEG. This should at least be discussed in more detail but ideally be experimentally verified to support the claim.\n- The impact of $\\lambda_{res}$ and $\\lambda_{SMM}$ is not studied. Adding an experiment to the ablation section where $\\lambda_{res}$ and $\\lambda_{SMM}$ are varied would provide insight into the sensitivity and impact of hyperparameters.\n- In the abstract, the claim of “up to 40% gains” over baselines is vague. It would improve clarity to specify under which dataset, SR scale, and metric these gains occur.\n- When the authors or the publication are not included in the sentence, the citation should be in parenthesis using \\citep{}, as outlined in the formatting instructions. \n- Some abbreviations are not defined (e.g., BCI, PCC).\n  Figure 4 is not readable due to the text being too small.\n\nIf the authors address the weaknesses the reviewer would be willing to increase the rating.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LcdIV30bfM", "reviewer": "Reviewer_sP2w", "summary": "The work proposes SRGDiff, a diffusion-based approach to solve the task of EEG spatial super-resolution (EEG-SR). SRGDifff proposes two modules, a residual direction module (RDM), predicting per-step residuals from LD EEG features as guidance, and a step-aware modulation module (SMM), providing time-dependent affine scaling and shifting of the latent features.", "questions": "- What is the conceptual difference between SRGDiff’s conditioning mechanism and standard conditional diffusion or ControlNet residual conditioning?\n- How does the \"residual direction\" differ from simply adding predicted corrections in the latent space (i.e., learned noise offsets)?\n- The reported FID improvements (Fig. 3) are inconsistent. SRGDiff does not always achieve the lowest FID, and in some settings, baseline methods perform comparably or better. Could the authors detail how the \"EEG-FID\" is computed, and why it is an appropriate metric for EEG fidelity (which seems primarily reference-based)?\n- Did the authors evaluate computational cost against simpler transformer-based SR models (e.g., ESTformer) under equal parameter budgets?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The work shows thorough experimentation by testing their approach across multiple datasets\n- The paper is well-writen and cleanly organized\n- The quantitative improvements over previous EEG SR methods are consistent and strong", "weaknesses": "Besides their meaningful application to an important domain, the proposed method largely combines existing diffusion-conditioning tricks (residual conditioning, affine modulation, step embeddings). There is little theoretical or conceptual innovation beyond standard conditional diffusion formulations. Overall, the method resembles ControlNet/T2I-Adapter-style modulation, rebranded for EEG. For the ICLR main track, this feels closer to an applied engineering paper than a new machine learning contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DbUciHqRI1", "reviewer": "Reviewer_cWrT", "summary": "This paper addresses the emerging problem of EEG spatial super-resolution, a relatively new and challenging topic in the EEG community. While super-resolution has been extensively studied in computer vision, only a few works have recently explored this concept for EEG, primarily in IEEE journals rather than mainstream neuroscience venues. The authors adapt state-of-the-art deep learning tools, such as transformers and generative diffusion models, to reconstruct high-density EEG from low-density recordings. This effort bridges the methodological gap between biomedical signal processing and modern deep generative modeling, which is commendable. However, several conceptual and technical issues require deeper clarification and refinement to make the work truly convincing from both a signal processing and machine learning standpoint.", "questions": "1. Why did you decide to continue using NMSE, PCC, and SNR? Could you justify why these metrics are still meaningful for evaluating generative EEG models, especially when HD EEG may not inherently outperform LD EEG on such metrics?\n\n2. How do you handle session- and subject-level variability? Were normalization or adaptation techniques applied to ensure robustness?\n\n3. Can you provide a more intuitive explanation for the specific architectural innovations that differentiate this method from STAD or ESTformer?\n\n4. Could you include full quantitative comparisons corresponding to Figure 4 to rule out visual selection bias?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "The paper introduces EEG spatial super-resolution to the broader ML community, addressing a domain problem that remains underexplored.\n\nThe use of deep generative modeling (transformer/diffusion) provides a fresh analytical perspective and helps re-interpret a traditionally biomedical task in a form understandable to the ICLR community.\n\nThe overall framework and evaluation pipeline are consistent with prior EEG SR baselines (e.g., ESTformer, STAD), allowing fair comparison and reproducibility.", "weaknesses": "1. The authors continue using NMSE, PCC, and SNR as the main evaluation metrics. These are inherited from previous EEG works but lack a theoretical justification for super-resolution tasks in the ML setting. Given the ICLR context, this presents an opportunity to re-examine whether these metrics are conceptually valid critically. For instance, does high-density (HD) EEG necessarily yield higher SNR values than low-density EEG? If not, why do the reported gains occur?\n\n2. EEG signals vary significantly across sessions and subjects. Without explicitly addressing this factor, through normalization, adaptation, or domain alignment, the model’s reported performance might not generalize. The paper does not mention whether such variability was handled or controlled.\n\n3. When compared to recent baselines such as STAD and ESTformer, the proposed method shows similar results across several datasets and metrics. The manuscript lacks a clear and intuitive explanation of how the proposed architectural modifications lead to consistent improvements beyond fine-tuned engineering choices.\n\n4. Figure 4 appears to showcase selected examples. Quantitative evidence (e.g., statistical comparisons or full-dataset averages) should be provided to demonstrate consistent superiority.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ynSUNM5IVT", "reviewer": "Reviewer_pkZv", "summary": "This paper tackles the problem of EEG spatial super-resolution (SR), which aims to reconstruct high-density (HD) EEG signals from sparse, low-density (LD) measurements. The authors argue that existing methods, including recent diffusion models, use a \"static guidance\" strategy (like concatenation) that leads to a trade-off between fidelity to the HD signal distribution and consistency with the LD input .\nTo solve this, they propose SRGDiff, a latent diffusion model that uses a novel \"dynamic conditional generation\" framework. The core idea is to learn a dynamic residual from the LD input, which acts as a step-aware directional correction during the reverse diffusion (denoising) process. This is implemented via two new modules: a Residual Direction Module (RDM) that predicts this step-wise residual and a Step-Aware Modulation Module (SMM) that applies step-dependent affine modulation to control the guidance strength. The authors evaluate their method on three datasets (SEED, SEED-IV, Localize-MI) using a comprehensive three-level protocol (signal, feature, and downstream) , demonstrating state-of-the-art results that significantly outperform strong baselines.", "questions": "In your ablation study (Figure 5), is the \"LDM\" baseline an unconditional model? If so, why did you not compare against the primary target of your criticism: a standard statically-guided LDM (e.g., using feature concatenation), which would serve as a proper baseline to evaluate the utility of your RDM and SMM modules?\n\nThe performance gains in your ablation (e.g., NMSE 0.86 for LDM vs. 0.34 for SRGDiff at 8x on SEED)  are far larger than the gains over the SOTA conditional model STAD (Table 2). This strongly suggests your LDM baseline is unconditional, making the ablation misleading. Can you provide a direct comparison between SRGDiff and an \"LDM + Static Concatenation\" baseline?\n\nYour runtime in Table 10 is impressively fast. Is this because the RDM and SMM are very lightweight? And does the static-guidance-by-concatenation baseline (which I am asking for) run even faster?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "The core concept of \"dynamic residual guidance\" is a clever and novel departure from standard static conditioning in diffusion models.\nThe paper demonstrates massive, consistent performance gains over a suite of strong, recent baselines (e.g., ESTformer, STAD) across three datasets . A 50% NMSE reduction on SEED is a remarkable improvement.\nThe three-level evaluation protocol (signal-level NMSE/PCC, feature-level EEG-FID, and downstream classification accuracy) is a model of rigor and provides a holistic and convincing picture of the generated signals' quality and utility .\nDespite its architectural complexity, the authors provide evidence (Table 10) that their method is significantly faster than other diffusion-based approaches, making it more practical.", "weaknesses": "The authors motivate their work by criticizing \"static guidance\"  but then fail to include a static guidance model (e.g., LDM with feature concatenation) in their ablation study (Figure 5). The comparison against a seemingly unconditional LDM baseline is a feeble argument. This omission makes it impossible to validate the paper's central claim: that the proposed dynamic RDM and SMM modules are superior to the standard, simpler conditioning method they are designed to replace.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)