Autoregressive Visual Decoding from EEG Signals
Authors: Sicheng Dai
Hongwang Xiao
Shan Yu
Qiwei Ye
Venue: ICLR 2026 Poster (2026)
Keywords: EEG decoding
Visual reconstruction
BCI
Visual neural decoding
Primary area: applications to neuroscience & cognitive science

Abstract: Electroencephalogram (EEG) signals have become a popular medium for decoding visual information due to their cost-effectiveness and high temporal resolution. However, current approaches face significant challenges in bridging the modality gap between EEG and image data. These methods typically rely on complex adaptation processes involving multiple stages, making it hard to maintain consistency and manage compounding errors. Furthermore, the computational overhead imposed by large-scale diffusion models limit their practicality in real-world brain-computer interface (BCI) applications. In this work, we present AVDE, a lightweight and efficient framework for visual decoding from EEG signals. First, we leverage LaBraM, a pre-trained EEG model, and fine-tune it via contrastive learning to align EEG and image representations. Second, we adopt an autoregressive generative framework based on a "next-scale prediction" strategy: images are encoded into multi-scale token maps using a pre-trained VQ-VAE, and a transformer is trained to autoregressively predict finer-scale tokens starting from EEG embeddings as the coarsest representation. This design enables coherent generation while preserving a direct connection between the input EEG signals and the reconstructed images. Experiments on two datasets show that AVDE outperforms previous state-of-the-art methods in both image retrieval and reconstruction tasks, while using only 10% of the parameters. In addition, visualization of intermediate outputs shows that the generative process of AVDE reflects the hierarchical nature of human visual perception. These results highlight the potential of autoregressive models as efficient and interpretable tools for practical BCI applications.

Published as a conference paper at ICLR 2026
AUTOREGRESSIVE VISUAL DECODING FROM EEG SIGNALS
Sicheng Dail:2.3, Hongwang Xiao Shan Yu! Qiwei Ye
IIstitute of Automation, Chinese Academy of Sciences
2 School of Artifcial Intelligence, University of Chinese Academy of Sciences
State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology "Beijing Academy of Artificial Intelligence
Naatooal Key Laboratory for Multimedia Information Processing, Peking University en, qiwei ye@baaibbaa... cn
dais wisichhng2223e11
ABSTRACT
Electroencephallgran (EEG) signals have become a popular medium for decod- ing visual information due their cost-effictiveness and high temporal resolution. However, current approaches face significant challenges in bridging the modal- ity gap between EEG and image data. These methods typically rely complex adaptation processes involving multiple stages, making it hard to maintain con- sistency and manage compounding errors. Furthermore, the computational over- head imposed by large-scale diffusion models limit their practicality in real-world brain-compurt computrr interface (BCI) applications. In this work, we present AVDE, a lightweight and efficient framework for visual decoding from EEG signals. First, we leverage LaBraM, a pre-trained EEG model, and fine-tune it via contrastive learning align EEG and image representations. Second, we adopt an autoregres- sive generative framework based on "nell-scale prediction" strategy: images are encoded into multi-scale token maps using pre-trained VQ-VAE, and trans- former is trained to autoregressively predict finer-scale tokens starting from EEG embeddings the coarsest representation. This design enables coherent genera- tion while preserving a direct connection between the input EEG signals and the reconstructed images. Experiments
datasets show that AVDE outperforms previous state-of-the -ft---rrtt methods in both image retrieval and reconstruction tasks, while using only 10% of the parameters. In addition, visualization of intermediate outputs shows that the generative process of AVDE reflects the hierarchical nature of human visual perception. These results highlight the potential of autoregressive models as efficient and interpretable tools for practical BCI applications. Code is available wtthtps://// / com/ ddi cee/ avde.
INTRODUCTION
How can access and interpret the rich visual information encoded human brain activity? This question has captivated neuroscientists for decades, driving fundamental research at the intersection of cognitive science and artificial intelligence. Decoding human vision from non-invasive neural signals not only advances our understanding of neural representation mechanisms but also promises transformative applications in comn-ce interfaces. Early pioneering work (Kay et al., 2008 Miyawaki et al. 2008 Naselaris et al. 2009) established that simple visual patterns could be de- coded from functional magnetic resonance imaging (fMRI), while recent advances in generative AI have enabled reconstruction of remarkably detailed visual content from brain signals (Takagi & Nishimoto 2023a Scotti et al. 2023 Fang et al. 2023).
Despite these successes, IMRI-based approaches face fundamental limitations for practical applica- tions: they operate at temporal resolutions orders of magnitude slower than actual neural process- ing, require costly infrastructure, and confine subjects to restrictive scanner environments (Menon & Kim 1999; Logothetis] 2008). These constraints have motivated a shift toward electroencephalog.
raphy (EEG) for visual decoding (Cichy & Pantazis 2017. EEG offers millisecond-levee temporal

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EEG
Imuge Embedding
Embedding
BEG Eincoder
High-level
Diffusion Prior
Final Reconstruction
CLIP Vision
EEG
Coarse Reconstruction
Stable Diffusion
Embedding
BEG Encoder
VAE Decoder
Low-level
VAE Encoder
Figure 1: A typical unCLIP framework employed in previous EEG- visual decoding works Li al.] (024); /Zhang et al. (2025); Xiao et al. (2025): Scotti et al. (2023). Despite its flexibility, the framework comprises multiple stages (five in this case), each introducing potential sources of error that can accumulate and degrade overall performance. Furthermore, the computational and memory demands of its components present significant challenges for practical implementation in BCIs.
precision, while providing significantly greater portability and accessibility at a fraction of the cost. Recent work in EEG-based visual decoding (Li et al. 2024 Xiao et al. 2025 Zhang et al., 2025) has demonstrated promising capabilitics in both image retrieval and reconstruction, suggesting the potential for more deployable applications.
However, a fundamental challenge persists: how to effectively bridge the vast distributional gap between noisy EEG signals and structured visual content. This challenge manifests in three limita- tions of current approaches. First, these methods typically rely on diffusion models and complex, multi-stage adaptation processes based unCL.IP (Ramesh et al., 2022) to project EEG signals into compatible latent representations. The sequential nature of these pipelines inherently compounds emrors across stages (Li & van der Schaar (023), , degrading the fidelity of reconstructed images. Second, the EEG encoders are typically trained from scratch using limited number of image-EEG pairs, which raises concerns about their capability
capture the intricate features in highly noisy EEG signals. Third, the computational demands of large-scale diffusion models (often exceeding 3B parameters) render these systems impractical for BCI applications where efficiency and respon- siveness are crucial.
To address these limitations, we propose Autoregressive Visual Decoding from EEG signals (AVDE), lightweight and efficient two-stage pipeline for EBG-to-image translation. Our approach makes two key innovations: First, rather than training EEG encoders from scratch, we leverage LaBraM (Jiang et al., 2024)-a model pre-trained on thousands of hours of diverse EEG fine-tune using contrastive learning to align EEG and image representations. This transfer learning approach substantially improves the extraction of meaningful features from noisy EEG signals. Sec- ond, we replace complex multi-stage diffusion processes with a streamlined autoregressive frame- work based on "next-scale prediction." Our approach encodes images into multi-scale token maps using a pre-trained VQ- VAE (Tian et al.) 2024), then trains a transformer to progressively predict increasingly detailed visual representations, starting from EEG embeddings as the coarsest represen- tation. This approach ensures coherent generation while maintaining direct relationship between EEG signals and visual outputs. Experiments on two datasets demonstrate that AVDE achieves ofate-of-thh-ar performance in both retrieval and reconstruction tasks while using only 10% of the parameters required by previous methods. Furthermore, visualization of the intermediate outputs shows that the generative process of AVDE reflects the hierarchical nature of human visual per- ception, underscoring the potential of autoregressive models as tools for exploring the dynamics of human visual cognition.
In summary, the main contributions are as follows:
We introduce AVDE, novel framework for hierarchical "next-scale prediction" strategy within an autoregressive transformer. This ap- proach progressively constructs visual representations from coarse to fine details, mirroring
visual decoding that employs
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the hierarchical nature of both biological visual processing and computational vision sys- tems.
We demonstrate that transfer learning from pre-trained EEG model significantly improves visual decoding performance. By fine-tuning the LaBraM encoder (Jiang et al. (2024) with contrastive learning, we achieve more robust alignment between EEG and image represen- tation spaces compared to training EEG encoders from scratch.
We demonstrate through comprehensive experiments that AVDE achieves state-of-the-ar performance in both image retrieval and reconstruction tasks on two datasets, while being more lightweight and computationally efficient than prior methods. Our approach reduces parameter count by approximately 90% compared to diffusion-bart based methods, making it more suitable for practical BCI applications.
METHOD
2.1 EEG ENCODING WITH LABRAM
critical challenge EEG-based visual decoding extracting meaningful features from the herently noisy signals. Rather than training encoders from scratch on limited EBG-image pairs, we build upon LaBraM (Jiang et al. 2024). model pre-trained on over 2000 hours of diverse EEG data spanning multiple datasets and recording conditions.
The architecture processes input EEG data X RCTT (where C: represents channels and repre- sents time points) through the following encoding scheme:
1) Temporal patching: The input EEG signal is segmented in the temporal dimension with a non- overlapping window of length w, resulting in patches:
(Teg. RR j 1,2..... C, 1,2,,,2,.... T
2) Local feature extraction: Each patch is processed by a temporal encoder comprising stacked convolutional blocks (ID convolution, group normalization, GELU activation) to capture fine- grained temporal patterns:
fec, E 1,
1,2,
(2)
where d is the embedding dimension.
3) Spatiotemporal contextualization: To incorporate both temporal and spatial context into the model, set up two sets of trainable positional embeddings: a temporal embedding set TE [te; h: = 1. I and spatial embedding set SE {se, | = 1,2,.... C). The final patch representation is obtained by summing the corresponding temporal and spatial embeddings with the encoder output:
(eo, + te sej j 1, 1,2,
C,
4) Global integration The enriched patch embeddings are processed by Transformer encoder Vaswani et al. 2017) that models dependencies across both time and channels, effectively integrat- ing information from the entire EEG epoch.
2.2 REPRESENTATION ALIGNMENT THROUGH CONTRASTIVE LEARNING
While pre-training provides a strong foundation for EEG feature extraction, the LaBraM model was primarily trained on clinical data (Oheid & Picone 2016) rather than EEG responses visual stimuli. To adapt the model for visual decoding, we fine-tune it through contrastive learning, which creates alignment between EEG and image representation spaces.
Given paired EEG-image data (X BBCCXT I E RBxHxW), we encode EEG signals ing the LaBraM model and images using a frozen CLIP (Radford et al. 2021) encoder, produc- ing embeddings e, I Axd We then optimize bidirectional contrastive objective that max- imizes agreement between corresponding ÉEG-image pairs while minimizing similarity between non-corresponding pairs:
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EEG Encoder
CLIP objective
CLIP Vision
0-0
Rg
R3
R
R4
FEG
Projection
Multi Tokenization
Visual Autoregressive Transformer
Figure 2: AVDE involves two training stages. Stage pre-trained EEG encoder is fine-tuned using contrastive learning more effectively capture visual information embedded in EEG signals. This adaptation aims to provide a more informative initialization for the subsequent visual recon- struction process. Stage 2: A visual autoregressive transformer is trained using the next-scale pre- diction objective (Equation 7 Specifically, the model takes the sequence ([s), R1, Rg..... Rx-1) input and predicts the corresponding sequence (R1 R2, R3. ......... Rg). Training is guided by standard cross-entropy loss.
CCLIP 1 log exp(s(e,,))/T) log xx)(s((=2)/T)) Σ exp(s(er,
(4)
Ej=rexp(s( exp(s(e;,
where s denotes cosine similarity and 5 is learned temperature parameter that controls the sharp- ness of the distribution. This objective effectively creates shared embedding space where EEG signals are mapped near their corresponding image representations and away from unrelated ones.
To further strengthen the alignment, we incorporate a direct regression objective following practices established in BBenchetrit al. (2023) and ei al. (2024):
Loombsned XLoLPP + (1 X)LMSE
(5)
where LMSE the mean squared error between EEG and image embeddings, and (set 0.8 in our experiments) balances the two objectives. This dual-objective approach provides more stable train- ing dynamics and improves the precision of the EEG-to-image mapping by combining structural alignment properties of contrastive learning with the point-wise precision of direct regression.
2.3 AUTOREGRESSIVE EEG-TO-IMAGE GENERATION
With aligned EEG representations in hand, we turn to the challenge of generating corresponding images. Rather than employing complex diffusion based pipelines, we adopt a hierarchical autore- gressive approach inspired by VAR (Tian et al. 2024). This framework enables direct, progressive image generation from EEG embeddings through h coarse-to refinement process.
The architecture consists of two key components:
1) Multi-scale image tokenization: pre- pre-trained VAE tokenizes images into a hierarchy of discrete representations at multiple resolutions. Given image I, the tokenizer produces feature map F E hxx that is quantized into multi-scale residual maps (R, R2...., Rx), where each R2 has resolution by we that progressively increases with k.
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These residual maps be combined to progressively reconstruct the full-resolution feature map:
FA
up R4, (h, w)):
where up(-) denotes bilinear upsampling, and Fj represents the accumulated feature map after corporating the first residuals. This formulation allows the image to be constructed incrementally, from coarse structures to fine details.
2) Next-scale prediction transformer: A decoder-only transformer is trained to autoregressively predict these residual maps from EEG embeddings e. The model learns to generate increasingly detailed visual representations according to:
P(R1.
Rk)
I Ri..... RR-1,.))
where the sequence (RI,.... Rx e) provides the context for predicting the next-scale residual Rg-
This formulation is particularly appropriate for neural decoding because mirrors theories of hier- archical visual processing in the brain, where perception progresses from coarse features to increas- ingly fine details. The EEG embedding e serves the initial neural representation of the perceived image, and the transformer progressively elaborates this representation across multiple scales.
Rd is first projected to the transformer's hidden dimension h to create special token [s]. which initiates the generation process. For each subsequent scale 1, the model processes the appropriately downsampled version of the previous cumulative feature map:
In practice, shown in Fig. 2, the EEG embedding
F (down(F-11 (hg, wx)),
where down(.) represents bilinear downsampling match the target resolution (hy, ws) of the current scale.
During training, employ block-wise causal attention mask ensure the model only attends to the appropriate context when predicting each scale. During inference, the process begins with the EEG embedding and autoregressively generates each scale until reaching the final resolution, which point the multi-scale VQ-VAE decoder transforms the predicted feature map FK into complete image.
3 EXPERIMENTS
3.1 EXPERIMENTAL SETUP
We primarily evaluate our method on the THINGS-EEG dataset (Grootswagers et al., 2022), which serves as a widely adopted benchmark for EEG-based visual decoding. To further verify the ver- satility of AVDE, we additionally conduct experiments on the EEG-ImageNet dataset (Zhu et al.) with results reported in Appendix C
2024..
Dataset Overview. The THINGS-EBE dataset (Grootsswagess et al., 2022 contains EEG record- ings from 10 participants collected under rapid serial visual presentation (RSVP) paradigm. The training set consists of 1,654 object concepts, each associated with 10 images presented four times, yielding total of 66,160 EEG trials. The test set includes 200 distinct concepts, each represented by single image repeated 80 times, resulting in 16,000 EEG trials. To mitigate habituation effects, pseudorandom order. Each image is displayed for
both training and test images are presented 100 milliseconds, followed by 100-millisecond blank screen to reduce blink-related and other arti- facts. EEG signals were recorded from 63 channels, band-pass filtered between 0.1 Hz and 100 Hz, and sampled at 1,000 Hz.
Data Preprocessing. Following the practice in Song et al. (2023) and Li et al. (2024), we segment the EEG data into epochs spanning O to 1,000 ms relative to stimulus onset and apply baseline

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Table 1: Overall accuracy of 200-way zero-shot retrieval under both within-subject and cross-subject settings. Each cell presents the Top-1 accuracy on the first line and the Top-5 accuracy on the second line. Results are averaged over five different random seeds; corresponding standard deviation values For each subject, the highest accuracy values are indicated in bold.
are presented in Table 12
Method
Sub-01 Sub-02 Sub-03 Sub-04 Sub-05 Sub-06 Sub-07 Sub-08 Sub-09 Sub-10 Ave
Within-suhject: train and test on one subject
EEGNerV4 Lawhern et al.
2018
0.144
0.159
0.202
0.224
0.132
0.129
0.198
0.246
0.184
0.237
0.186
0.391
0.398
0.432
0.517
0.289
0.402
0.467
0.549
0,419
0,543
0.441
EEGConformer [Song et al.
2022
0.095
0.108
0.142
0.155
0.088
0.081
0.122
0.164
0.109
0.151
0.122
0.261
0.274
0.306
0.371
0.198
0.280
0,318
0:405
0.298
0.392
0.310
NICE (ong ct al. 2023
0.201
0.192
0,212
0,224
0.144
0,261
0.269
0.382
0,234
0.298
0.242
0.479
0,369
0,538
0.504
0.316
0,563
0,557
0.674
0,532
0,586
0.512
ATM (Li ct al. 2024
0.232
0.188
0.273
0,280
0.168
0,280
0.268
0,393
0.245
0.372
0.269
0.512
0,432
0,570
0,541
0.395
0.592
0,537
0.715
0,512
0.677
0.548
AVDE (Ours)
0.250
0.241
0.275
0.298
0.254
0.335
0.274
0.417
0.261
0.395
0.300
0.552
0,510
0.586
0.547
0,503
0.603
0.552
0.713
0,521
0.730
0.582
Cross-subject: leave one subject out for test
0.086
0.082
0.073
0.113
0.092
0.101
0.056
0.084
0.074
0.124
0.089
EEGNerV4
0.232
0.226
0.171
0.257
0.217
0.224
0,182
0.231
0.196
0.305
0.224
EBGConformer
0.069
0.066
0.058
0.090
0.088
0.045
0.067
0.059
0.099
0.071
0.197
0.193
0.146
0.217
0.185
0.191
0,156
0.198
0.167
0.260
0.191
NICE
0.103
0.286
0.257
0.100
0.086
0.206
0.127
0.323
0.091
0,183
0.146
0.341
0.102
0.268
0.112
0.239
0.098
0.242
0.169
0,386
0.273
0.113
0.121
0.128
0.082
0.127
0.094
0.107
0.083
0.122
0.096
0.171
0.115
ATM
0.296
0,302
0.224
0.293
0.249
0.259
0,257
0.296
0.247
0.381
0.280
AVDE (Ours)
0.141
0.170
0.091
0.152
0.125
0.173
1.074
0.185
0.132
0.180
0.143
0.322
0.384
0.218
0.325
0.324
0.386
0.204
0.401
0.336
0.393
0.329
correction using the mean signal from the 200 ms pre-stimulus interval. All electrodes are preserved, and the data are downsampled to 200 Hz. Given that EEG amplitudes typically range from -0.1 mV to 0.1 m, normalize the signals by scaling them with respect to 0.1 mV, resulting in values primarily distributed between -1 and I. For the test set, EEG responses corresponding to each image are averaged across repetitions to improve the signal-to-noise ratio.
Implementation Details. We initialize the EEG encoder and the visual autoregressive (VAR) transformer with the pre-trained weights provided in the official GitHub repositories of LaBraM (Jing al. 2024) and VAR (Tian et al. 2024), respectively. The EEG encoder is trained using the AdamW W optimizer with an initial learning rate of 2e-3, weight decay of 0.05, and minimum learning rate of le-5. The batch size set to 128. For the VAR transformer, we configure the model with depth of 16 and train it using the AdamW optimizer with 31 = 0.9, B2 = 0.95, a base learning rate of 2e-5, a weight decay of 0.05, a global batch size of 512, and 50 training epochs. Additional hyperparameter details are provided in Appendix B During generation, we employ classifier-free guidance (CFG) with a ratio of 4.0 and apply top-k sampling with = 900. All the experiments are conducted on Linux servers equipped with four NVIDIA A100 (40G) GPUs and Python 3.10.16 PyTorch 2.5.1 + CUDA 12.4 environment.
Evaluation. We assess the effectiveness of AVDE on both image retrieval and reconstruction tasks. For the retrieval task. we compute the cosine similarity between the EEG embeddings generated by the EEG encoder and the CLIP image embeddings of 200 test concepts. Retrieval performance is evaluated based on the probability that the ground truth concept appears among the top-K candidates
(K= 1 or 5). For the reconstruction task, we adopt standard evaluation metrics following prior work (Scotti et al. 2023 Li et al. 2024) to quantify the similarity between reconstructed and ground truth visual stimuli: (1) PixCorr pixel-wise correlation; (2) SSIM Structural Similarity Index Measure;
(3) SwAV - average correlation distance computed from SwAV AV-ResNet50 (Caron et al. 2020) fea- tures; and (4) Two-way identification using pretrained neural networks (AlexNet (Krizhevsky al. 2012) layers 2 and 5, Inception (Szegedy et al. 2013), and CLIP). Two-way identification is treated
bidirectional retrieval task, described in Ozcelik & VanRullen 2023).

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3.2 RETRIEVAL PERFORMANCE
Table presents a quantitative evaluation EEG-based image retrieval performance, comparing proposed method, AVDE, with several baseline approaches. Remarkably, AVDE achieves top-1 accuracy of 0.300 and a top-5 accuracy of 0.582 in the zero-shot EEG-to-image retrieval task under the within-subject setting. Under the more challenging cross-subject setting, attains a top-1 accuracy of 0.143 and top-5 accuracy of 0.329. These results represent a substantial improvement over existing state-of oftte-of-the-rrr art methods, highlighting the effectiveness of our approach.
The strong performance of AVDE underscores the utility of the LaBraM- -aased EEG encoder, which benefits significantly from large-scale pre-training. This pre-training enables the encoder to gen- eralize more effectively across individuals and extract semantically meaningful features from raw EEG signals. These high-quality EEG embeddings serve as robust foundation for the subsequent visual decoding stage, thereby facilitating more accurate and coherent EEG-to-image generation. Overall, these findings demonstrate the potential leveraging pre-trained architectures to enhance the performance of EEG-based inference systems.
3.3 RECONSTRUCTION PERFORMANCE
Since Subject-08 08 exhibits the highest retrieval performance, follow the convention of prior works al. [2024] Zhang et al. 2025 Xiao et al. 2025) by quantitatively evaluating reconstruction per- formance on Subject-08 ensure fair comparison. As shown in Table AVDE outperforms isting approaches, achieving the highest scores across both high-level and low-level metrics. These results indicate that AVDE improves not only low-level visual fidelity but also high-level seman- tic consistency in the reconstructed images. Additional results for other subjects are provided in Appendix B
Table 2: Quantitative assessments of EEG-based visual reconstruction quality on Subject-08.
Low-level
High-level
Method
PixCor t SSIM
AlexNet(2) t AlexNet(5)
Inception
CLIP t SwAV
Li et al. 2024
0.160
0.345
0.776
0.866
0.734
0.786
0.582
GeoCap
0.148
0.380
0.813
0.877
0.712
0.791
0.582
CognitionCapturer
0.175
0.366
0.760
0.610
0.721
0.744
0.577
AVDE (Ours)
0.188
0.396
0.817
0.889
0.765
0.795
0.557
Qualitative results Figure 3 further substantiate the quantitative evaluations. Compared to pre- vious methods, AVDE reconstructs images that more closely resemble the ground-truth stimuli in terms of both structure and recognizable content. Whereas earlier models often yield semantically ambiguous reconstructions, our approach recovers finer details and clearer object shapes, benefiting from progressive multi-scale refinement and the informative EEG feature initialization.
3.4 EFFICIENCY ANALYSIS
We further analyze the inference efficiency of AVDE by comparing it against previous state-of-the. art methods. Since most existing approaches adopt similar unCLIP pipeline and utilize the same diffusion model (SDXL (Podell et al.) (2023) for image generation. we select Li et al. (2024) as a representative due to widespread adoption and open-source availability. To comprehensively assess both computational and spatial efficiency, we evaluate the following metrics: (1) FLOPPs the number of floating-point operations; (2) Inference time - the GPU time required for generation; and (3) Memory usage the peak GPU memory usage during inference. All metrics are measured using PyTorch's built-in profiler on single NVIDIA A100 GPU. The batch size is set to 1, cor- responding to the resource cost of generating single image. As summarized in Table 3 AVDE achieves faster image generation and lower memory consumption compared to prior state-of-the-a methods, demonstrating its superior suitability for practical applications.
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Seen
Li et al.
Ours
Figure 3: Qualitative Comparison of Visual Reconstruction Performance. Selected reconstruction results from subject-08 demonstrate that the visual stimuli reconstructed by our method preserve finer-grained features, suggesting improved fidelity and detail compared to alternative approaches.
Table 3: Comparison with state-of fotte-of-ie of-toe-attteee the-art method on inference efficiency. All metrics are evaluated using PyTorch's built-in profiler on a single NVIDIA A100 GPU. The batch size is fixed at and results are averaged over 200 runs to ensure stability and reliability.
Method
Params (M) Steps
FLOPs (G) Inference Time (ms) Memory Usage (MB)
Li al. 2024
3818.1
4
8738.6
310.4
4826.73
AVDE (Ours)
425.3
10
1350.5
91.2
1809.63
3.5
ABLATION STUDY
To evaluate the contribution of each core component AVDE, namely the pre-trained EEG encoder and the autoregressive generative framework, we perform the following experiments: (1) Encoder substitution (ATM/EEGNe/NICE
VAR): These experiments replace the LaBraM encoder with other widely used EEG encoders (2) Generative framework substitution (LaBraM + Li et al., unCLIP baseline): This setting replaces the VAR generative framework with a standard unCLIP pipeline (Li et al. 2024h. (3) Model substitution with diffusion models (LaBraM LDM-4 (Rombach et al.) 2022) DiT-XL (Peebles Xie 2023 These experiments replace the VAR model with slightly larger diffusion models trained under the same conditions. As shown in Table performance de- grades when the EEG encoder is replaced, underscoring the value of high-quality embeddings from the pre-trained encoder for accurate visual reconstruction. Similarly, replacing the autoregressive framework results in substantial drop in performance, suggesting that our overall training strategy more effectively aligns the distributional characteristics of EEG signals with those of natural images.
3.6 ANALYSIS OF INTERMEDIATE OUTPUTS
Given that the "next-scale prediction" strategy employed in AVDE constitutes a progressive gener- ative process, we examine how the model incrementally extracts and interprets visual information from EEG signals throughout this procedure. To this end, visualize all intermediate reconstruc- tions by accumulating the feature maps at each scale and decoding them into images using the decoder of a pre-trained multi-scale VQ-VAE. Formally, the set of cumulative feature maps is de- noted as {Fx & = 1,,...., K), where represents the total number of scales, and each F₁ is computed described in Equation
As shown in Figure 4 the generative process AVDE exhibits notable parallels to the hierarchical organization of human visual perception. In the early stages of generation, the model produces
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Table 4: Impact of using different EEG encoders generative framework on the reconstruction performance. The results are averaged over all subjects.
Low-level
High-level
Method
PixCorr t
SSIM 1
AlexNei(2) AlexNeu(S)
Inception 1
CLIP
SwAV
VAR
0.147
0.366
0.766
0.835
0.724
0.747
0,586
ATM+VAR
0.141
0.351
0.752
0.821
0.711
0.731
0,601
ERGNet+VRR
0.132
0.323
0.733
0.803
0.687
0.712
0.627
NICE+VAR
0.136
0.341
0.742
0.812
0.701
0.719
0.613
LaBraM+ al. 2024
0.138
0.346
0.746
0.817
0.707
0.726
0,606
LaBraM+LDM-4
0.139
0.343
0.750
0.825
0.713
0.731
0.609
LaBraM+DiT-XL/2
0.143
0.354
0.761
0.829
0.715
0.735
0.594
coarse Seatures-mirronee the role of the retina and primary visual cortex (V1), which primarily encode low-level visual attributes such edges and color gradients (Togg 2003 Tootell et al. 1998). As the process continues, mid-level features begin emerge, resembling the functional role of V2 and V4 in integrating contours and object-level structures (Hegdé & Van Essen 2007). In the final stages, the model reconstructs semantically rich and coherent imagery, analogous to the activity in higher-order visual regions such as the inferotemporal cortex, where holistic object representations are formed (Tanaka 1996).
Figure 4: Intermediate reconstructions generated by AVDE across 10 progressive scales. Each row corresponds to a distinet EEG-evoked reconstruction instance, and each column represents the cumulative output up given scale. This process reflects the hierarchical nature of human visual perception, drawing parallels to the function of successive cortical visual areas (e.g., V1, V2/V4, and IT).
To examine the contribution of each intermediate scale to the generative process, we quantify the correlations between intermediate image features and EEG features derived from different brain regions. As shown in Figure 5p. EEG electrodes are grouped into five regions: frontal, temporal, central, parietal, and occipital. For each region, mean channel embeddings are computed using the EEG encoder, while for each intermediate scale, image embeddings obtained from the CLIP image encoder. The cosine similarity between each region-scale pair is then calculated to assess their correspondence.
Since the generative process is cumulative, we compute the stepwise increase at each scale to capture the incremental information contributed by that scale. As shown in Figure 5}, the step increase for occipital regions peaks at early scales and gradually diminishes thereafter. In contrast, the temporal and parietal regions exhibit relatively sustained step increases across early and middle scales, fol-

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Published as a conference paper at ICLR 2026
lowed by decline in later scales. The frontal and central regions, however, show low step increases initially, which progressively rise and peak at late scales. These results suggest that the intermediate scales reflect the functional roles of different brain regions during visual processing.
13
EEG Encoder
(9
Cosine Similarity
CLIP Image
b
Certts
Avtioa
0.18
Nean Smm aFR3
Stop ncross
2.005
Central
Scale
Scale
Figure 5: Analysis of similarities between intermediate scales and brain regions. (a) The mean channel embeddings from five brain regions are compared with the intermediate image embeddings. Cosine similarity is used the measure. (b) Since the generative process is cumulative, the similar- ities generally increase as more scales are involved. (c) Stepwise increase captures the incremental information contributed by each scale. The step increase for occipital regions peaks at early scales and gradually diminishes thereafter. The temporal and parietal regions exhibit relatively sustained step increases across early and middle scales, followed by a decline in later scales. The frontal and central regions show low step increases initially, which progressively rise and peak at late scales.
CONCLUSION
In this work, presented AVDE, novel autoregressive framework for visual decoding from EEG signals that addresses key limitations existing approaches in terms of complexity, efficiency, and performance. By leveraging pre-trained EEG representations via LaBraM and replacing multi-stage diffusion pipelines with streamlined autoregressive process, AVDE enables accurate and coherent reconstruction of visual content from noisy EEG data. Experiments on two datasets demonstrate that AVDE outperforms state-of-th tht-hhr-rt approaches in both retrieval and reconstruction tasks while requiring only fraction of their computational resources, making it well-suited for practical BCT applications. Moreover, the hierarchical structure of AVDE 's generative process mirrors the struc- ture of human visual perception, highlighting its potential as computational tool for investigating the mechanisms of visual cognition.
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A RELATED WORK
A.1 VISUAL DECODING FROM NEURAL SIGNALS
The field of visual decoding from neural signals has evolved substantially, progressing from early pattern recognition techniques to increasingly sophisticated generative models. Foundational stud- ies (Kay et al., 2008 Naselaris et al. 2009) established key methodologies by employing Gabor wavelet-based encoding models and Bayesian inference to identify stimuli from fMRI activity pat- terns. These early efforts demonstrated the feasibility of decoding visual information from brain activity, laying the groundwork for more advanced approaches. Subsequent progress marked by the integration of deep learning, particularly through representation alignment techniques. Methods such as BrainCLIP (Liu et al. 2023) and a series of related works (Benchetrit al. [223 Xia al.) 2024 Takagi & Nishimoto 2023b Qian et al. (023) leveraged contrastive learning frameworks in-
spired by CLIP (Radford et 2021) to align neural signals with high-level visual representations.
These approaches significantly enhanced decoding accuracy, even under constraints of limited train-
ing data.
A paradigm shift occurred with the introduction of generative frameworks. Mind's Eye (Scotti et al., 2023) pioneered the application of the unCLIP approach from DALLE-2 (Ramesh et al.J 2022) to iMRI-based image reconstruction, incorporating prior diffusion model to refine neural features before generation. This multi-stage framework was subsequently adapted for BEG-based visual de- coding (Li et al. 2024 Zhang et al., 2025 Xiao al. 2023). yielding visually impressive results. However, these complex pipelines introduced significant challenges: error propagation across multi- ple processing stages, substantial computational requirements, and difficulty maintaining coherence between the original neural signal and the generated output. These limitations highlight the need for more direct, efficient approaches that preserve the relationship between neural activity and visual reconstruction.
A.2 VISUAL GENERATIVE MODELS
Contemporary visual generative models primarily fall into two paradigms: diffusion models and au- toregressive models, each with distinet computational characteristies particularly relevant for neural decoding applications.
Diffusion models (Ho et al. 2020 Song et al. 2020: Podell al., 2023 Peebles & Xie 2023
Esser et al.) 2024) generate images by reversing a gradual noise-addition process, iteratively re- fining random noise into coherent visual content through multiple denoising steps. While these models produce high-quality images with exceptional detail, they require substantial computational resources and typically involve 25-50 sequential denoising steps, limiting their applicability in cesource-constrained or real-time scenarios.
Autoregressive models [Eser et al., [021 Yu et al., 2022 Van Den Oord al. 2017 Razavi al. 2019 Lee et al.) 2022) offer an alternative approach by discretizing images into token sequences and predicting each token conditionally on previous ones. Traditionally, these models generated images in raster-scan scan order püxeel-y-piixe or patch- batch-by-patch), which limited their ability to capture global structure and produce high-resolution outputs (Yu al. (022). However, recent innovations in hierarchical autoregressive modeling, particularly Visual Autoregressive modeling (VAR) (Tian) jet al., 2024), have transformed this landscape by introducing a coarse-to-fine generation strategy called "next-scale prediction." This approach progressively claborates visual details across multiple resolution scales, requiring significantly fewer sampling steps while maintaining global coherence.
The efficiency advantages of VAR make it particularly well-suited for neural decoding applications, where computational constraints are significant and the hierarchical nature of the generation process aligns conceptually with theories of visual processing in the brain. AVDE builds upon this paradigm, leveraging its computational efficiency and hierarchical structure to establish a more direct mapping between EEG signals and visual representations.
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B
HYPERPARAMETER SETTINGS
Table 5: Hyperparameters for the LaBraM-based EEG encoder.
Hyperparameters
Values
Iput channels
{1,8,8)
Output channels
(8,8,8)
Temporal Encoder
Kernel size
{15,3,3)
Stride
(8,1.))
Padding
(7.1,1)
Transformer encoder layers
12
Hidden size
200
MI.P size
800
Attention head number
10
Batch size
128
Peak learning rate
2e-3
Minimal learning rate Learning rate scheduler
1e-5
Cosine
Optimizer
AdamW
Adam /
(0.9,0.99)
Weight decay
0.05
Total epochs
50
Warmup epochs
5
Drop path
0.1
Layer-wise learning rate decay
0.8
Contrastive loss factor À
0.8
Table 6: Hyperparameters for the visual autoregressive transformer.
Hyperparameters
Values
Number of layers
16
Hidden size
1024
ML. size
4096
Number of heads
16
Number of scales/steps
10
Size of scales
(1, 2, 4, 5, 6, 8, 10, 13, 16)
Batch size
128
Peak learning rate
2e-5
Minimal learning rate
2e-6
Learning rate scheduler
Cosine
Optimizer
AdamW
Adam 3
(0.9,0.95)
Weight decay
0.05
Total epochs
30
Warmup epochs
0
Drop path
0.1
Normalization e
le-6
Drop rate
0
Attention drop rate
0
Condition drop
0.1
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C RESULTS ON EEG-IMAGENET
C.I DATASET DESCRIPTION
To verify the generalizability of our method, we also conducted experiments on the first 8 subjects from the EEG-ImageNet dataset (Zhu al., 2024). The visual stimuli consisted of 80 object cate- gories drawn from subset of ImageNer2Ik. Each category contained 50 manually curated images, yielding 4,000 EEG-image pairs per subject. After randomizing the category order, the 50 images in each category were sequentially presented using the RSVP paradigm, with each image shown for 500 ms. EEG signals were recorded from 62 channels, band-pass filtered between 0.5 and 80 Hz, and sampled at 1,000 Hz. Following the protocol of the original study, we used the first 30 images of each category for training and the remaining 20 for testing.
C.2 CLASSIFICATION
Table 7: Overall accuracy of 80-way classification on EEG-ImageNet. Each cell presents the Top-1 accuracy on the first line and the Top-5 accuracy on the second line. For each subject, the highest accuracy values are indicated in bold.
Method
Sub-01
Sub-02
Sub-03
Sub-04
Sub-05
Sub-06 Sub-07
Sub-08
0.297
0.241
0.206
0.139
0.177
0.165
0.152
0.097
BEGNetV4 (Lwhhem et al.
2018
0.612
0.607
0.639
0.402
0.553
0.689
0.672
0.357
0.301
0.263
0.194
0.141
0.209
0.173
0.151
0.102
REGConformer Song et al.
2022
0.629
0.618
0.636
0.421
0.552
0.701
0.667
0.365
0.293
0.258
0.205
0.138
0.196
0.184
0.149
0.095
NICE Song ct al.
2023
0.621
0.607
0.640
0.419
0.561
0.683
0.671
0.359
0.306
0.245
0.213
0.151
0,205
0.187
0.146
0.091
ATM Li et al.
2024
0.633
0.624
0.641
0.428
0.557
0.699
0.676
0.353
0.308
0.270
0.227
0.154
0.218
0.329
0.301
0.144
AVDE (Ours)
0.634
0.628
0.643
0.435
0.566
0.703
0.695
0.388
C.3 RECONSTRUCTION
Table Quantitative assessments EEG-based visual reconstruction quality on EEG-ImageNet.
Low-level
High-level
Subject
PixCorr t
SSIM t
AlexNet(2) t AlexNet(5) + Inception +
CLIP t
SWAV |
Sub-01
0.106
0.283
0.577
0.664
0.556
0.539
0.626
Sub-02
0.096
0.277
0.564
0.643
0.547
0.527
0.639
Sub-03
0.095
0,264
0.541
0.617
0.525
0.501
0.651
Sub-04
0.080
0.249
0.505
0.578
0.480
0.479
0.677
Sub-05
0.086
0.253
0,528
0,592
0.504
0.482
0.664
Sub-06
0.124
0.318
0.616
0.701
0.603
0.568
0.605
Sub-07
0.119
0.308
0.599
0.694
0.581
0.553
0.613
Sub-08
0.077
0.232
0.480
0.553
0.461
0.457
0.690
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Table 9; Reconstruction quality on EEG-ImageNet with baselines.
(a) Li et al
2024
Low-level
High-level
Subject
PixCorr f
SSIM f
AlexNe((2) t
AlexNet(5) Inception f
CLIP t
SWAV +
Sub-01
0.099
0.269
0.559
0.629
0.529
0.511
0.648
Sub-02
0.092
0.264
0.543
0.611
0.532
0.501
0.659
Sub-03
0.091
0,255
0,525
0.603
0,508
0.480
0.675
Sub-04
0.078
0.236
0.491
0.563
0.466
0.460
0.698
Sub-05
0.081
0.240
0,504
0.569
0.485
0.460
0.692
Sub-06
0.119
0.307
0.589
0.678
0.584
0.545
0.628
Sub-07
0.114
0.296
0.584
0.669
0.565
0.538
0.642
Sub-08
0.074
0.224
0,462
0.529
0.448
0.435
0.699
(b) CognitionCapturer (Zhang et al. 2025
Low-level
High-level
Subject
PixCorr f
SSIM t
AlexNe((2) t AlexNet(5))
Inception
CLIP t
SwAV 4
Sub-01
0.105
0.267
0.551
0.631
0.536
0.509
0.662
Sub-02
0.098
0.263
0.540
0.606
0.539
0.499
0.646
Sub-03
0.088
0.262
0.515
0.593
0.509
0.481
0.679
Sub-04
0.091
0.243
0.494
0.570
0.461
0.456
0.690
Sub-05
0.075
0.244
0.492
0.567
0,483
0.460
0.690
Sub-06
0.113
0.305
0.601
0.691
0.584
0.549
0.616
Sub-07
0.109
0,293
0.595
0.662
0.564
0.553
0.643
Sub-08
0.077
0.213
0.472
0.532
0.449
0.430
0.693
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D RESULTS ON THINGS-MEG
D.1 DATASET DESCRIPTION
We also verified our method on the THINGS-MEG dataset (Hebart et al. 2023 to examine its gen- cralizability across neural modalities. The training set consists of 1854 categories, with 12 images in each category, and the test set contains 200 categories. Each image in the dataset was displayed for 500 ms. The preprocessing pipeline includes bandpass filtering of [0.1. 40] Hz, downsampling to 200 Hz and baseline correction. Similar to THINGS-FEES we ran the retrieval and reconstruction experiments on this dataset.
D.2 RETRIEVAL
Table 10: Overall accuracy of 200-way zero-shot retrieval on THINGS-MEG. Each cell presents Top-1 accuracy on the left and Top-5 accuracy on the right. For each subject, the highest accuracy values are indicated in bold.
Method
Sub-01
Sub-02
Sub-03
Sub-04
EEGNetV4 (Lawhern et al.
2018)
0.118 / 0.327 0.136 / 0.351
0.171 0.382
0.192 0.459
EEGConformer (Song et al.
2022)
0.071 / 0.218 0.086 0.234
0.119
0.259
0.129 0.324
NICE (Sog et al. 2023)
0.173 0.427 0.162 0.322
0.185
0.487 0.198 0.449
ATM et 2024
0.196 / 0.455 0.153 0.381
0.235 0.512 0.242 0.489
AVDE (Ours)
0.221 /0499 0.211 /0.466
0.244 / 0.533
0.266 0.494
D.3
RECONSTRUCTION
Table 1I: Reconstruction quality on THINGS-MEG with baselines.
Li et al.
2024
Low-level
High-level
Subject
PixCorr
SSIM f
AlexNet(2) 1 AlexNet(5) 1
Inception 1
CLIP 1
SwAV +
Sub-01
0.119
0.327
0.691
0.802
0.681
0.699
0.642
Sub-02
0.094
0.276
0.661
0.744
0.627
0.673
0.664
Sub-03
0.128
0.315
0.714
0,826
0.716
0.728
0.645
Sub-04
0.066
0.281
0.702
0.754
0.664
0.661
0.677
(b) AVDE (Ours)
Low-level
High-level
Subject
PixCorr
SSIM 1
AlexNet(2) 1 AlexNeu(5)) 1 Inception CLIP 1
SwAV |
Sub-01
0.121
0.329
0.721
0.804
0.692
0.699
0.658
Sub-02
0.109
0.351
0.732
0.789
0.681
0.712
0.649
Sub-03
0.152
0.344
0.740
0.801
0.668
0.725
0.654
Sub-04
0.113
0.333
0.719
0.808
0.699
0.742
0.641
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E ADDITIONAL RESULTS ON THINGS-ERS EEG
E.I RETRIEVAL.
Table 12: Standard deviation values of Top-1 and Top-5 accuracy on the 200-way zero-shot retrieval task. These results are complement to Table
Method
Sub-01 Sub-02 Sub-03 Sub-04 Sub-05 Sub-06 Sub-07 Sub-08 Sub-09 Sub-10
Within-subject: train and
subject
Lawher al. 2018
0.012
0.008
0.008
0.019
0.020
0,012
0.015
0.015
0.017
0.009
0.045
0.041
0.025
0.031
0.010
0.031
0.035
0.034
0.026
0.028
ERGConfarmer Song et al.
2022
0.014
0.015
0.013
0.020
0.017
0.010
0.009
0.011
0.018
0.019
0.009
0.008
0.044
0.043
0.017
0.015
0.026
0.040
0.018
0.041
0.010
0.014
0.012
0.016
0.012
0,008
0.015
0.031
0.010
0.009
NICE Song
2023
0.030
0.028
0.037
0.039
0.027
0.043
0.037
0.033
0.044
0.042
ATM Li ct al.
2024
0.016
0.006
0,022
0.015
0.011
0.027
0.013
0.022
0.016
0.020
0.017
0.022
0.015
0.018
0.011
0.024
0.026
0.026
0.023
0.028
0.018
0.011
0.015
0.012
0.017
0.027
0.018
0.023
0.012
0.017
AVDE (Ours)
0.020
0.023
0.016
0.021
0.019
0.025
0.028
0.019
0.025
Suosettubject:
one subject out
EBGNetV4 Lawhem al. 2018
0.012
0.010
0.009
0.014
0.013
0.011
0.012
0.013
0.011
0.012
0.025
0.022
0.021
0.023
0.020
0.022
0.024
0.025
0.021
0.022
0.010
0.009
0.009
0.013
0.012
0.009
0.008
0.008
0.011
0.011
EBGConformer Song ct al.
2022
0.009
0.008
0.021
0.022
0.014
0.013
0.017
0.021
0.015
0.020
NICE Song
2023
0.009
0.010
0.009
0.011
0.010
0.008
0.011
0.017
0.008
0.007
0.019
0.018
0.024
0.025
0.017
0.021
0.023
0.020
0.025
0.023
ATM Li et al. 2024
0.013
0.008
0.015
0.011
0.010
0.018
0.011
0.015
0.013
0.014
0.015
0.016
0.013
0.015
0.011
0.017
0.018
0.018
0.016
0.019
0.014
0.010
0.012
0.011
0.013
0.019
0.014
0.017
0.011
0.013
AVDE (Ours)
0.016
0.017
0.013
0.015
0.014
0.018
0.015
0.020
0.015
0.018
E.2 RECONSTRUCTION
Table 13: Quantitative assessments of EEG-based visual reconstruction quality on all subjects.
Low-level
High-level
Subject
PixCorr f
SSIM f
AlexNet(2) t AlexNet(5) f Inception
CLIP f
SwAV +
Sub-01
0.138
0.362
0.750
0.837
0.725
0.721
0.609
Sub-02
0.120
0.388
0.754
0.811
0.710
0.737
0.604
Sub-03
0.168
0.379
0.763
0.827
0.704
0.746
0.603
Sub-04
0.126
0.361
0.748
0.835
0.731
0.765
0.593
Sub-05
0.145
0.373
0.743
0.814
0.691
0.732
0.586
Sub-06
0.128
0.364
0.768
0.832
0.707
0.717
0.584
Sub-07
0.126
0.351
0.760
0,827
0.733
0.731
0.588
Sub-08
0.188
0.396
0.817
0.889
0.765
0.795
0.557
Sub-09
0.156
0.339
0.753
0.810
0.713
0.726
0.600
Sub-10
0.173
0.349
0.807
0.871
0.764
0.800
0.543
19

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Published as a conference paper at ICLR 2026
Table 14: Reconstruction quality on THINGS-EEG with baselines.
(a) Li et al
2024
Low-level
High-level
Subject
PixCorr f
SSIM 1
AlexNe((2) t
AlexNet(5)) Inception f
CLIP t
SwAV +
Sub-O1
0.136
0.354
0.720
0.831
0.710
0.728
0.589
Sub-02
0.108
0.301
0.688
0.775
0.657
0.705
0.618
Sub-03
0.145
0.340
0.747
0.858
0,751
0.759
0.590
Sub-04
0.075
0.305
0.729
0.782
0.694
0.688
0.623
Sub-05
0.133
0.352
0.738
0.791
0.637
0.707
0.612
Sub-06
0.140
0.363
0.753
0.834
0.686
0.742
0.595
Sub-07
0.155
0.360
0.769
0.851
0.684
0.697
0.608
Sub-08
0.163
0.345
0.786
0.868
0.730
0.770
0.575
Sub-09
0.134
0.338
0.736
0.788
0.626
0.652
0.606
Sub-10
0.112
0.329
0.735
0.843
0.674
0.708
0.570
(b) CognitionCapturer (Zhang et al.
2025
Low-level
High-level
Subject
PixCorr f
SSIM f
AlexNe((2) t AlexNet(5)+ f
Inception f
CLIP t
SWAV 1
Sub-O1
0.148
0.334
0.741
0.626
0.666
0.711
0.592
Sub-02
0.147
0.344
0.764
0.618
0.661
0.725
0.590
Sub-03
0.140
0.307
0.715
0.549
0.690
0.710
0.603
Sub-04
0.126
0.355
0.801
0.660
0.701
0.765
0.543
Sub-05
0.130
0,343
0.731
0.639
0,594
0.655
0.611
Sub-06
0.122
0.337
0.748
0.620
0.646
0.688
0.630
Sub-07
0.145
0.355
0.777
0.623
0.731
0.721
0.576
Sub-08
0.175
0.366
0.760
0.610
0.721
0.744
0.577
Sub-09
0.148
0.337
0.731
0.623
0.625
0.692
0.605
Sub-10
0.152
0.389
0.773
0.664
0.657
0.736
0.569
Median
Figure 6: Subject-08's Best, Medium, and Worst reconstructions selected based on PixCorr.
20

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Published as a conference paper at ICLR 2026
Median
Worst
Figure 7: Subject-08's Best, Medium, and Worst reconstructions selected based on SwAV.
Median
Figure 8: Subject-10's Best, Medium, and Worst reconstructions selected based on PixCorr.
21

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Published as a conference paper at ICLR 2026
Median
Worst
Figure 9: Subject- 10's Best, Medium, and Worst reconstructions selected based on SwAV.
Median
Figure 10: Subject-03's Best, Medium, and Worst reconstructions selected based on PixCorr.
22

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Published as a conference paper at ICLR 2026
Median
Worst
Figure 11: Subject- 03's Best, Medium, and Worst reconstructions selected based on SwAV.
23


Reviews:
[{"review_id": "rNTH5YyCRw", "reviewer": "Reviewer_xLwT", "summary": "This paper proposes AVDE, a lightweight and efficient framework designed for decoding and reconstructing visual images from EEG signals. The authors critique current methods that depend on complex, multi-stage adaptations or computationally intensive diffusion models, arguing that these approaches are impractical for real-world applications in BCI.", "questions": "Please see weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "+ This work thoughtfully addresses the challenge of computational overhead seen in models like diffusion, achieving remarkable results with just 10% of the parameters. \n\n+ Moreover, the autoregressive approach shines with its simplicity and efficiency. By utilizing the EEG embedding as the initial token for the transformer, it establishes a clever architectural foundation. \n\n+ This model outperforms more complex multi-stage and diffusion-based counterparts in both retrieval and reconstruction tasks, proving that a lighter model can indeed be more effective.", "weaknesses": "- Autoregressive models can sometimes face the challenge of error accumulation, where an initial mistake in an early token or pixel patch can amplify throughout the rest of the generation. State how we can address this.\n\n- While this approach offers great efficiency and performs well on standard metrics, we may notice that the qualitative fidelity of its reconstructions doesn’t always match that of larger, slower models, such as diffusion, in more complex scenes. It's all about striking a balance, and share your thoughts on this.\n\n- Moreover, the effectiveness of the reconstruction greatly hinges on the pre-trained image tokenizer, which translates images into tokens. If the tokenizer’s performance is lacking, it can limit the model's potential. Your insights would be useful.", "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": "IvMLPOKHeJ", "reviewer": "Reviewer_TTYP", "summary": "This paper introduces AVDE, an autoregressive framework for reconstructing visual stimuli from EEG signals. The approach combines a pre-trained EEG encoder (LaBraM), fine-tuned via contrastive learning with CLIP embeddings, and a transformer-based autoregressive generator (VAR) built on a pre-trained VQ-VAE. The authors aim to simplify the complex multi-stage diffusion-based pipelines used in previous EEG-to-image decoding work. Experiments on THINGS-EEG report modest improvements in retrieval and reconstruction metrics, while significantly reducing computational cost and model size.", "questions": "1.\tCould you clarify the specific contribution of LaBraM—does its advantage come mainly from large-scale pretraining, or from its architectural design? Supplementary experiments isolating these factors would help clarify its role.\n2.\tWhat practical or theoretical advantages does the VAR-based autoregressive generation offer compared to diffusion models, beyond computational efficiency? Do you observe qualitative differences in reconstruction behavior?\n3.\tHave you considered extending the validation to other neural modalities (e.g., MEG, fMRI) or tasks to test whether AVDE generalizes beyond the THINGS-EEG dataset?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tThe combination of a large-scale pre-trained EEG encoder (LaBraM) and an autoregressive generative model (VAR) is a technically reasonable and promising direction. Pretraining-based decoding is becoming a general paradigm, and applying it here may help bridge the data scarcity of EEG.\n2.\tVAR offers computational advantages over diffusion-based pipelines, including lower latency and reduced parameter count. The paper quantifies these gains and reports consistent, if moderate, improvements in retrieval and reconstruction tasks.\n3.\tThe writing is clear and well-organized. Figures effectively illustrate the hierarchical decoding process, and ablations on efficiency and encoder replacement provide supporting evidence for the engineering claims.", "weaknesses": "1.\tThe research motivation remains underdeveloped. While AVDE is presented as an alternative to diffusion models, it does not fully resolve the fundamental challenges of EEG-to-image decoding. The work should be viewed as an exploratory step rather than a definitive advance.\n2.\tThe methodological innovation is limited. The approach primarily substitutes diffusion with an autoregressive transformer and incorporates LaBraM pretraining. These are incremental modifications rather than new modeling ideas.\n3.\tExperimental data are restricted to THINGS-EEG, which is relatively small and homogeneous. Validation on richer or multimodal datasets (e.g., MEG or fMRI) would significantly strengthen the generality claim.\n4.\tThe paper lacks neuroscientific or interpretability analysis. The authors claim that AVDE “reflects the hierarchical nature of human visual perception,” but no neural or representational evidence supports this statement. Analyses of temporal, spatial, or feature-level EEG correlations would be necessary to substantiate such claims.\n5.\tFigure 1 depicts the prior unCLIP pipeline rather than the proposed AVDE model, which may confuse readers. Showing the AVDE architecture first would better emphasize the paper’s own 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": "MbmIslcP3K", "reviewer": "Reviewer_ZRBw", "summary": "Summary:\r\nThis paper presents AVDE, a simple but effective framework for reconstructing visual stimuli from EEG signals. The AVDE method consists of two stages: (1) aligning EEG and image representations by fine-tuning a pre-trained EEG encoder (LaBraM) via contrastive learning, and (2) generating images using a hierarchical autoregressive transformer that predicts visual tokens from coarse to fine scales. The authors evaluate AVDE on two datasets (i.e., THINGS-EEG and EEG-ImageNet) and demonstrate superior performance in both image retrieval and reconstruction tasks compared to existing methods, while using only 10% of the parameters of prior diffusion-based models.", "questions": "1.**Performance on Cross-Subject and Real-World Data.** \nWhile the paper presents strong within-subject results, the cross-subject performance (Top-1 accuracy of 0.143 and Top-5 accuracy of 0.329 in Table 1) suggests that the model's ability to generalize across different individuals is still limited. I understand that subject variability is a well-known challenge in EEG-based decoding. But including analysis of subject-specific factors (e.g., signal quality, attention level) and exploring personalization or domain adaptation strategies would further improve the quality of this study.\n\n2.**No Real-Time or Latency Evaluation.**\n\nThe paper emphasizes efficiency but does not provide concrete metrics such as inference time or latency on standard hardware. This is particularly important for BCI applications, where real-time feedback is often critical.\nIn comparison, Scotti et al. (2023) and Zhang et al. (2025) highlight the computational bottlenecks when training large models on complex EEG-image tasks. This paper would benefit from a more detailed ablation study exploring how different training parameters (e.g., learning rate, batch size, number of epochs) affect resource consumption and performance.\n\n3.**Model Interpretability**\n\nThe hierarchical, coarse-to-fine image generation process is a black-box operation, and while the paper visualizes the intermediate stages (Figure 4), it remains unclear how the model's decision-making process works in finer detail. For example, how does the EEG embedding influence specific features of the generated image at each scale? Are there any cases where the EEG signals lead to inconsistent or inaccurate image generation, and if so, how could the model be improved to handle such cases?\n\nThe lack of transparency in how the model generates certain image details from EEG signals is an issue, as Li et al. (2024) showed the brain regions and time for image reconstruction. Neural decoding methods should provide not only accurate predictions but also clear insights into how decisions are made from complex neural data. Future work could focus on improving the interpretability of the autoregressive process, possibly by integrating attention mechanisms or using explainable AI methods.\n\n\nRef:\n\nScotti, A., Banerjee, A., Goode, J., Shabalin, S., Nguyen, A., Dempster, A., Verlinde, N., Yundler, E., Weisberg, D., Norman, K., et al. (2023). Reconstructing the mind's eye: fMRI-to-image with contrastive learning and diffusion priors. NeurIPS\n\nZhang, K., He, L., Jiang, X., Lu, W., Wang, D., & Gao, X. (2025). CognitionCapturer: Decoding visual stimuli from human EEG signal with multimodal information. AAAI\n\nLi, D., Wei, C., Li, S., Zou, J., Qin, H., & Liu, Q. (2024). Visual decoding and reconstruction via EEG embeddings with guided diffusion. NeurIPS", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. **next-scale prediction**. AVDE has an interesting idea (“next-scale prediction”, rather than traditional multi-stage diffusion models). It is conceptually novel. Unlike earlier methods that suffer from error propagation across multiple stages, AVDE generates visual content progressively, starting from coarse EEG embeddings and refining them to more detailed image representations. This approach improves coherence between EEG inputs and reconstructed images.\n\n2.**Performance**\nThe proposed method demonstrates significant improvements in both image retrieval and reconstruction tasks, outperforming state-of-the-art methods while using only 10% of the parameters required by previous diffusion models. This is clearly evidenced by the performance comparison in Table 1 (Page 6), where AVDE shows superior accuracy in retrieval tasks (Top-1 accuracy of 0.300 and Top-5 accuracy of 0.582) under the within-subject setting, and substantial improvements in the cross-subject setting. These results illustrate the model's robustness and the efficiency of the framework.\n\nAdditionally, the reconstruction quality shown in Table 2 and Figure 3 further supports AVDE's high quality. AVDE achieves better low-level (PixCorr) and high-level (SSIM, AlexNet, Inception) metrics compared to existing methods, confirming that the method not only reconstructs clearer and more faithful images but also captures high-level semantic consistency.\n\n3.The paper is generally well-written.", "weaknesses": "1. The paper’s strongest results are within-subject; cross-subject transfer remains limited and the manuscript offers little analysis of what factors drive between-subject variance. Minor\n\n2. The efficiency comparison fixes diffusion at 50+4 steps and specific CFG/top-k settings while the proposed AR approach uses 10 steps; modern diffusion samplers (e.g., DDIM, DPM-Solver/DPMS++) can operate at 10–20 steps with competitive quality, and lighter priors are possible. As written, the comparison risks conflating algorithmic gains with configuration choices. Moderate\n\n3. Current ablations do not isolate which components most drive gains, making it hard to attribute improvements. More ablations are needed. Moderate\n\n4. Code cannot be accessed. Major\n\n5. The manuscript states that “the generative process reflects the hierarchical nature of human visual perception,” but current evidence is largely qualitative. Visualization (Fig.4) reflects a coarse-to-fine spatial refinement induced by the multi-scale architecture. However, the text interprets this as evidence for a biological temporal/area hierarchy (retina→V1→V2/V4→IT). That mapping is speculative here: you do not manipulate time or measure area-selective correspondences. As written, it risks conflating spatial scale with neural hierarchy. Moderate", "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": "1k7hCsU4mS", "reviewer": "Reviewer_p5Kj", "summary": "This paper proposes AVDE, a framework for reconstructing visual images from EEG signals, aiming to improve upon existing methods that often rely on complex, computationally expensive diffusion pipelines. AVDE uses a two-stage process: first, it fine-tunes a large pre-trained EEG model (LaBraM) using contrastive learning to align EEG and image (CLIP) representations. Second, it employs an autoregressive transformer, inspired by VAR's \"next-scale prediction\" strategy and using a pre-trained VQ-VAE, to generate images progressively from the aligned EEG embedding. The authors report state-of-the-art results on retrieval and reconstruction tasks using the THINGS-EEG dataset, highlighting significant improvements in efficiency (fewer parameters, faster inference) compared to diffusion-based predecessors.", "questions": "Could you clarify the ablation study in Table 4? To fairly assess the contribution of the VAR framework, could you provide results for a LaBraM + Diffusion baseline trained under identical conditions (using the same fine-tuned LaBraM encoder) as your main LaBraM + VAR model?\n\nRegarding the efficiency comparison in Table 3: How does AVDE compare to the diffusion baseline if the latter is optimized for speed using fewer sampling steps (e.g., 10-20, if image quality remains acceptable)?\n\nTable 2 focuses on Subject-08. How robust are the reconstruction performance gains across other subjects, particularly those with lower retrieval scores? An average comparison across subjects for reconstruction would be valuable.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Demonstrates substantial reductions in parameter count (~90%), computational cost (FLOPs), inference time, and memory usage compared to a representative diffusion-based baseline, making it more viable for practical BCI applications.\nSuccessfully leverages large pre-trained models (LaBraM, VAR) through fine-tuning and contrastive alignment, highlighting the benefits of transfer learning in this domain.", "weaknesses": "The ablation (Table 4) does not cleanly isolate the contribution of the autoregressive generative model (VAR) versus a comparable diffusion model when using the same high-quality LaBraM encoder. This makes it difficult to definitively conclude that the VAR approach itself is superior.\nThe framework primarily combines existing powerful components (LaBraM, VAR architecture, VQ-VAE, CLIP alignment) rather than introducing fundamentally new techniques.\nLinking specific generation scales to cortical areas (V1/V2/IT) is speculative without stronger supporting evidence .", "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)