MindPilot: Closed-loop Visual Stimulation Optimization for Brain Modulation with EEG-guided Diffusion
Authors: Dongyang Li
Kunpeng Xie
Mingyang Wu
Yiwei Kong
Jiahua Tang
Haoyang Qin
Chen Wei
Quanying Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Neuroscience
Brain Modulation
EEG
Closed-loop
Brain Coding
BCI
Generative Model
Black-box Guidance
Encoding Model
Primary area: applications to neuroscience & cognitive science
TL;DR: We propose an EEG-guided black-box optimization framework for visual stimulation that can regulate human brain activity.

Abstract: Whereas most brain–computer interface research has focused on decoding neural signals into behavior or intent, the reverse challenge—using controlled stimuli to steer brain activity—remains far less understood, particularly in the visual domain. However, designing images that consistently elicit desired neural responses is difficult: subjective states lack clear quantitative measures, and EEG feedback is both noisy and non-differentiable. We introduce MindPilot, the first closed-loop framework that uses EEG signals as optimization feedback to guide naturalistic image generation. Unlike prior work limited to invasive settings or low-level flicker stimuli, MindPilot leverages non-invasive EEG with natural images, treating the brain as a black-box function and employing a pseudo-model guidance mechanism to iteratively refine images without requiring explicit rewards or gradients. We validate MindPilot in both simulation and human experiments, demonstrating (i) efficient retrieval of semantic targets, (ii) closed-loop optimization of EEG features, and (iii) human-subject validations in mental matching and emotion regulation tasks. Our results establish the feasibility of EEG-guided image synthesis and open new avenues for non-invasive closed-loop brain modulation, bidirectional brain–computer interfaces, and neural signal–guided generative modeling.

Published as a conference paper at ICLR 2026
MINDPILOT: CLOSED-LOOP VISUAL STIMULATION OPTIMIZATION FOR BRAIN MODULATION WITH EEG- GUIDED DIFFUSION
Dongyang Li
Kunpeng Xie
Mingyang Wu²
Yiwei Kongl.2
Jiahua Tang
Haoyang Qin'
Chen Wef
Quanying Liu5.4,5f
'outhern University of Science and Technology
University of Delaware
SPSL Research University
'Omni-Inteligeence "Shenzhen Loop Area Institute
ht https://im https://github
ABSTRACT
Whereas most brain commnneertter interface research has focused on decoding neural signals into behavior or intent, the reverse challenge- -usng controlled stimuli to steer brain activity-remains
less understood, particularly in the visual domain. However, designing images that consistently elicit desired neural responses is difficult: subjective states lack clear quantitative measures, and EEG feedback is both noisy and non-differentiabl.. We introduce MindPilot, the first closed-loop framework that uses EEG signals as optimization feedback to guide naturalistic image generation. Unlike prior work limited to invasive settings low-level flicker stimuli, MindPilot leverages non-invasive EEG with natural images, treating the brain black-box function and employing a pseudo-model guidance mechanism to iteratively refine images without requiring explicit rewards or gradients. We validate MindPilot in both simulation and human experiments, demonstrating efficient retrieval of semantic targets, (ii) closed-loop optimization of EEG features, and (iii) human-subject validations mental matching and emotion regulation tasks. Our results establish the feasibility of EEG-guides image synthesis and open new avenues for non-invasive closed-loop brain modulation, bidirectional brain-computer interfaces, and neural signal-guided generative modeling.
INTRODUCTION
The ability modulate brain activity with precisely designed visual stimuli could open new avenues for cognitive enhancement, neurorehabilltation, and bidirectional human-AV interaction. However, designing images that reliably steer neural responses remains largely unexplored. Such modulation can be understood as steering the brain toward specific internal states, as reflected in neural signals like EEG (Epstein Kanwisher 1998 Qiu al. 2023). Conceptually, this task resembles steering a deep visual encoder, where the aim is to design images that elicit particular internal representations. Yet, unlike artificial networks, the human brain poses unique challenges: humans' subjective states lack clear quantitative measures, real EEG responses noisy and variable, and the brain itself is fundamentally non-differen non-differentiable.
Recent advances in controllable generation, particularly lext-conditioned diffusion models (Li et al. 2019; Rahmani et al. 2022 Epstein et al., 2023) Wei et al. 2024a), offer unprecedented flexibility in image synthesis. But these models are optimized for linguistic prompts, not neural feedback, and thus remain orthogonal to the challenge of brain-targeted generation. Prior efforts in closed-loop visual neuromodulation have shown that generative models can be guided by neuronal responses to synthesize maximizing stimuli (Ponce et al. [2019] Walker et al. 2019 Bashivan et al. 2019 Minai et al. 2024). However, these approaches are typically invasive, relying on small-scale cortical recordings and targeting low-level neuronal activity rather than cognitive states. On the non-invasive
$ Equal Contribution.
Conresponding Author.

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Published as a conference paper at ICLR 2026
A
Optimization Goal
Visual Stimulation
C
Black Box Modeling
V
Target State
Target State
Signal
Current State
Current State
Error
Black-box model
(2) Feature Extraction
V4
V1
Visual Stimuli
EEG Latent
Approximate
Pseudo
Model
V
IP-Adapter
(4) Update Stimuli
Target
(3) Guided Generation
+
CLIP Encoder
I
# Denoising
Bladi-box
Diffusion
Guidanca Diffusion
Figure 1: Conceptualization of MindPilot. A. The goal of MindPilot is to continuously optimize visual stimuli drive the brain latent state to the target. B. A pseudo-model provides surrogate gradients to iteratively refine images with respect to neural targets (e.g., semantic feature, spectral feature). C. The closed-loop visual optimization.
side, EEG provides large-scale, distributed measures brain activity that are more directly linked to cognition. For example, (Luo et al.) 2024b) introduced the VEP Booster to modulate EEG with flickering visual stimuli. Yet such methods are constrained to low-level visual domains and cannot generate semantically rich, naturalistic images that engage higher-order brain representations.
We introduce MindPilot, a closed-loop framework that unifies black-box optimization with diffusion- based generation for GEG-guided image design (Fig. D MindPilot employs surrogate srroogat--guiedd strategy that replaces explicit reward gradients with pseudo modell updates, enabling gradient- free optimization toward diverse neural targets such semantic similarity EEG spectral features. Through both surrogate simulations and human ERG experiments, MindPilot achieves efficient convergence within limited iterations while producing interpretable, naturalistic images aligned with neural selectivity and perception. Our main contributions are:
EEG-guided visual stimuli optimization: a general framework that leverages the black- box proxy model guide diffusion to synthesize stimuli capable of steering latent brain states.
Black-box guidance generation: novel pseudo-model strategy for flexible, gradient-free optimization diverse neural targets.
Comprehensive validation: extensive experiments in simulation and human EEG experi- ments, showing consistent feasibility in various neural targets, from EEG semantic features to EEG spectral features and human emotion ratings.
RELATED WORK
Neural Selectivity and Invariance with FEG. EEG captures distributed neural signatures reflect- ing both selectivity (e.g., the N170 ERP for faces (Fimer, 2011) and invariance (different inputs yielding equivalent neural responses (Baroni et al. (023 ). Multivariate decoding methods have shown that EEG patterns can reliably differentiate object categories and semantic content (Holm) ctt al., 2024). However, most work treats EBG as an offline readout rather than an optimization signal for stimulus design.
Closed-loop Visual Neuromodulation. Generative models have been used to design stimuli that maximize neural activity invasive recordings (Bashivan et al. 2019: Walker et al. 2019 Pierzch- lewicz et al. 2023) and to refine low-level EEG biomarkers through flicker-based paradigms (Luo et al. 2024b). In the semantic domain, pioneering non- pon-invasive works have explored boodback-rrivee
stimulus selection (Grizou al.) (025) or generation based on binary attributes (Davis et al.! 2022).
However, these approaches often operate on discrete choices or limited semantic subspaces, restricting
the diversity of synthesized images. Similarly, methods relying on heuristic latent averaging (de la
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New Iteration
(4)
Chuseloop(U, Yucue))
B
Feature type 1: Semantic Feature
Image Feelure 21(
EEG Featura
Target Fonture
EEG Signal
CLIP
EEG Ensoddr
Similcrity
Pseudo-madel
Image Batch U
[(zx)
Datsboon
MLP
EEG Feature
C
Feature type
PSD Feature
Similanky
Search by image similarity
PSD Feeture
Target activation
EEG Encodar
EEG Signal
(3)
EEG Signal
(2)
Generation
Score
Target Feature
P Pidaseer
Viswet
s )
C
SDXL
ERISS Encoder
Feature Simiirity
+
Frowen
indiforentable
Target Signal
V (#x) (#x)
Traning
Cozine arit
Figure 2: Framework. Each MindPilot iteration involves four steps: 1): The black-box proxy model g. which maps images to synthetic EEG, is designed as a black-box proxy model to predict the brain responses X. 2): The EEG Encoder / identifies different kinds features from BEG. MindPilot calculates the similarity score sim)((((())) Vtarger) as rewards based on EEG features. 3): Update the image embedding using gradient descent. 4): The image with a higher brain similarity score is selected and passed back to the image generator to optimize stimuli. B. Semantic feature from pre-trained EEG encoder f. aligned with CLIP embedding.
C. Brain energy feature using
Power Spectral Density (PSD) features. For more details, refer to Section 3.2
Torre-Ortiz al. 2020 face challenges in sample efficiency during the search process. Consequently, MindPilot aims to bridge these gaps by establishing a framework for efficient, continuous, and higllfidelity naturalistic image synthesis driven directly by non-invasive EEG.
Brain-conditioned Controllable Generation. Text-conditioned diffusion has become the de facto
standard for controllable image synthesis (Nichol et al. 2022 Ramesh et al. 2022) Ye al. 2023
Podell et al. 2024). conI-coodi image generation has been extensively developed (Wang
al.
2024 Xia et 2024] Scotti al. 2024 [Shen et al.) 2024 Gong et al.
2025). Extensions
neural conditioning include gradient-based brain-guided synthesis (Luo et al
2023 2024a Cerdas
al.
2025) and encoding- model alignment with fMRI (Gu al. 2023 Bao al. (025). However,
EEG-condiiioned generation has mostly been explored for decoding or reconstruction (Bai et al.
2024
Li et al., 2024 Fu et al. 2025: Lopez et al. 2025 Guo al., 2025). and has not yet been integrated into closed-loop optimization frameworks. Our work addresses this gap by introducing
MindPilot, which directly optimizes naturalistic image generation guided by EEG responses.
3 METHODS
We introduce MindPilot, closed-loop framework for generating the optimal visual stimuli to align with the target brain activity (Fig. A)). At its core, MindPilot treats the brain (or its EEG readout) as black-box proxy model and optimizes stimuli by iteratively maximizing the similarity between predicted neural responses and a target embedding. This formulation enables flexible objectives: e.g., aligning with semantic features in CLIP space (Fig. 2 EEG spectral featuree (Fig. 2C).
3.1 PROBLEM SETUP
Let u be visual stimulus (an image) and let æ CRC be the corresponding multi-channel EEG response it evokes, where is the number of channels and is the number of time points. We model the brain's visual processing the function g can represent either the actual human brain or a pre-trained, black-box neural network serving as its proxy.
unknown, con-differentiable forward process: z g(u), where
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regression layer, where each unit corresponds to a Published as a conference paper at ICLR 2026
Our goal is modulate the brain state towards a specific target. This target brain state is defined by a neural feature vector, Marget. which is extracted from the specified target EEG signal Turget via a feature encoder, f CR IR.. Thus, Yuarge = f( (unge The objective is to find an optimal image, u*, that generates EEG response whose features maximally similar to the target features. This can be formulated as an optimization problem: u = arg maxu sim( (f (((()), Yorge); where sim((:-) the cosine similarity between the EEG representation evoked by visual stimuli u and the target brain state Yurget-
3.2 CLOSED-LOOP OPTIMIZATION
MindPilot proceeds iteratively find images matching a target feature Yurget- Starting from a uniform prior where every image has an equal selection probability t. MindPilot dynamically updates the score (r) for each image in the database at iteration I. These scores define a non-uniform selection probability (t( that guides the sampling for the next round. Let Thest denote the set of indices corresponding to the top-k images in step with the highest similarity scores (i.e., [Tees = k).
Step A: Direct Reward Update. First, we compute an intermediate score S by applying a direct reward to all images whose indices are in the best set Thest. The score for each of these top-k images updated via an Exponential Moving Average (EMA):
(u,)) = (1 a)- S., + a sim(f(g(u:)), Yurget) (u,))
if E Thest $ Toests
where is the importance weight for the direct reward.
Step B: Spreading Update. Next, we "spread" the rewards from the top- & images to other similar images in the database Q2 by adding an aggregated spreading term, which is the average of the rewards spread from each of the top-k images, weighted by their CLIP embedding similarity
,, uN) The updated score for the j-th image, S is calculated
exp(s ui uy))
S = (1- (1-3) (),) + I
Σ ((()))
(2)
E exp(s (s( uu u))
iE
where 3 is the hyperparameter. This ensures that an image u, receives a stronger score boost if is similar to multiple images in the high-scoring set Thea. The reward being spread from each best item is proportional to its own updated score, (1,)).
Having defined valid score St-1(u)) for all candidate images E {1..... N with Eq. (1) and 2, the updated scores are converted into a probability distribution for the next selection round using the softmax function:
Put1( = exp(S1+1(1))
Σ exp( (()))
The framework then samples images based on this updated probability P4t)( to continue the search and generation process in the subsequent iteration t
3.3 BLACK-BOX PROXY MODEL
Direct real-time EEG acquisition is costly and impractical for large-scale closed-loop experiments. To address this, MindPilot is designed interface flexibly with EEG black-box proxy model g, serving as a black-box proxy of the human brain, which is treated as a non-differentiable black- box in optimization. The proxy model combines pre-trained backbone with regression head trained to minimize Mean Square Error (MSE) against real EEG recordings. Specifically, we replace the classification layer of the backbone with flattened EEG channel-timepoin signal.
To demonstrate the framework's flexibility, we instantiated diverse set of black-box proxies, ranging from classic CNNs (AlexNet (Krizhevsky et al. 2012), ResNet50 (He et al. 2016)) CORnet-
S (Kubilius et al.) 2019)) to recent sulf-super and ission-transformes architectures (MoCo He
et al.] 2020). VIT-B B-32 (Dosovitskiy al. 2021). OpenCLIP ViT-B-32 (Cherti et al. 2023), DINO2-
VTT-B-I B-14 (Oquab et al. 2024), DINO- VIT B-16 (Caron et al. 202I), SYNCLR VIT-B (Sundaram
al. 2024)), reported in Tab.
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Table 1: Evaluation of black-box proxy models. We compared the predicted visual-evoked EEG response against the real EEG response over the [60, 500] ms post-stimulus window. We report the averaged Pearson's 's correlation coefficient R and the Noise Ceiling. Note: This metric aggregates performance across a broad window, which inherently dilutes peak correlations. Time-resolved analysis reveals significantly stronger predictive power key latencies (reaching r se 0.6 at ~ 100ms, see Appendix Fig. A.8).
Metricx(S))
AlexNet
ResNe150
CORnet-S
MoCo
VIT
OpenCIIP
DINO
DINO2
SYNCIR
Noise Ceiling
R1
16.41
16.89
8.74
16.26
13.05
12.78
13.55
13.84
12.89
18.18
3.4
INTERACTIVE SEARCH
To bootstrap optimization with an unknown target image, drawing inspiration from interactive retrieval methods (Ferecatu & Geman 2007). we propose similarity-weighter sampling approach. Starting from random candidates, MindPilot updates the sampling distribution using a roulette-we whette selection weighted by similarity score. Over iterations, the distribution sharpens around stimuli eliciting neural activity closer to the target (Algorithm[]
The process begins with a randomly selected set images Up. without prior knowledge of the specific features of the target image. We use the roulette wheel selection algorithm to choose from current images based on the similarity score sim(J [ (u )), Durget). The system updates the probability P.(u(,) for each image in the database belonging to the target class, based on the response model's prediction Y = (U,) RN Subsequently, MindPilot calculates the similarity score between the target and the feature predicted by the image selected. See more implementation details in Appendix 1.3 Once an image is identified as the best a iteration, the likelihood of similar images in the search space belonging the target class is increased.
3.5 HEURISTIC GENERATION
Searching in a fixed image pool is limiting. We therefore integrate E.EG-guided diffusion generate new stimuli. Specifically, we employ the pretrained guided diffusion model SDXL-Lightning (Lin et al. 2024) with black-box guidance: Gaussian Process (GP) surrogate predicts reward gradients in latent space for DEGdg image generation.
EEG-driven Black-box Guidance Recently, black-box guidance diffusion has proved be suc- cessful at generating high-quality images and drug discovery (Fan et al. 2023 Black et al.) (2024] Tan al. 2025). To replace directly calculating the gradient on the black-box encoding model, we use pseudo target embedding 3 by the pseudo target model with input æx. For the Gaussian Process (GP) update case, we set the pseudo target embedding 8* as the one gradient step update using the gradient of the mean prediction of the GP surrogate model as follows:
žバ fzzi Z"),
where K is the number of diffusion sampling steps, 17 is the step size that decays linearly, #x denotes the CLIP embedding from generated latents and 2 [z], =).. The f(zK; Z") denotes the prediction of the GP surrogate model (Seeger 2004) evaluated at 2k, which has closed-form as below:
f(zk: Z²) k(zK. )
(5)
where Z" = 12 ... 2 and y = _ v=] denotes the collected data from the previous - 1 step and its corresponding rewards in each sampled set U., respectively. We define each reward y by the scaling factor 7 as y = simif ((()))), Yuuge) x 7
Online Iteration Algorithm We integrate an EEG-guided genetic algorithm to optimize stimuli towards the evolution direction of the target neural activity. The specific procedural steps of our algorithm are outlined in Algorithm 2 Unlike the interactive searching process in Algorithm [ after sampling the stimulus image in each step we perform crossover and "mutation" embeddings and then sample new images from the image space. See Appendix AA.1.4 for additional details the evolution process. Throughout this process, the relative order of the original CL.P
the image
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features is preserved within each dimension to ensure that the mutated images remain semantically coherent and interpretable by humans.
Figure 3: EEG-guided Interactive Search. A. Similarity score between MindPilot's 's neural rep- resentations (steps 1, 2, last, and best-step) and the target, versus random stimuli. B. Cross-modal correlation between image and EEG embedding similarity (R = 0.23, P < 0.01). C. Similarity score improvement across all subjects using semantic features. D. The correlation between image embedding similarity and EEG semantic feature similarity across all subjects. The vertical axis represents the similarity score between the EEG features at the current step and the target.
A
Average similrity differntt
B
Model FIt
C
Improvement acroes subjects
Corretation analysis between EEG and CLIP
iteration steps
Image Feature Similarity
p= 232
Confidenca interva
EBG Enibudding Simibrity
Sinilarity Score 1%6
1%0 Booe
80
60
40-
Sandar
20-
81
Sro and
EEG
Similarity
53
50510
0.30
0.25
CUP Smilanty
0.25
4 EXPERIMENTS
4.1 SETUP
EEG readout from proxy models We trained family of predictors to approximate EEG responses evoked by visual stimuli using the THINGS-EEG2 dataset (Gifford et al.) [022] Grootswagers (et al., 2022). Each model regressed 17-channel x 250 timepoint EEG signals from pre-trained visual features. Nine variants were evaluated across participants, with performance quantified by Pearson's 's r and noise ceiling (Tab. [ Training used all repetitions (four trials per image) and was computationally efficient, fitting within 48 GB memory Appendix A.I.I for details). As shown in Tab. I even relatively simple CNN-based proxies (c.g., AlexNet, ResNet50) achieved competitive prediction accuracy. These results confirm that MindPilot can operate effectively with a wide spectrum of black-box proxies, highlighting the framework's generality: it is not tied particular backbone, but instead offers a and--ay recipe for closed-loop optimization with any image-to-EEG predictor.
single NVIDIA L40 GPU (see
Target Features of the evoked EEG We considered two neural targets: (i) semantic embeddings, extracted from ATM-S (Li et al. 2024 and aligned with CLIP ViT-H-14 features, and (ii) spectral signatures, derived from EEG power spectral density (PSD). Semantic targets were used in the retrieval task, while both semantic and PSD targets were used in the generation task, ensuring that optimization was cognitively meaningful. Moreover, to test whether MindPilot framework can be extended the subjective state modulation even without an explicit EEG feature the target, we considered emotion regulation under visual stimulation using self-reported scores in real human experiment in Appendix 4.4.1
4.2 EEG-GUIDED INTERACTIVE SEARCH
We first tested whether MindPilot can retrieve target stimulus from the THINGS-EEG? search space (50 categories x 12 conditions 600 images). Starting from 10 random images, the model iteratively updated sampling distribution based on EEG CLIP similarity. We set slippage factor (a = 0.1) and reward propagation factor (3 = 0.1). Although the set hyperparameters were not subjected to thorough search, they were sufficient to bring about an increase in iterations.. Results are summarized in Fig. 3 Convergence: Similarity scores improved consistently across iterations, surpassing random sampling baselines (Fig. A Alignment: EEG embeddings exhibited significant correlations with CLIP representations across participants, confirming CLIP similarity valid proxy for neural alignment (Fig. 3B. Subject-level robustness: Per-subject performance (averaged across five seeds and three target images) showed significant improvements, confirmed by paired

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Visual stimul generation by offiine guided diffusion
Montal matching arror
Performance by guidance
Moan absolute ener ratio 1N6
200
b
Better
Linar
Avernged
60
20
EER
random
Offine
Clesed-lcop
Guidence
Figure 4: EEG semantic feature-guided visual stimuli generation. Mean 1.1 error reduction relative to the random stimuli baseline and offline EEG- BEG-guided generation, averaged over five closed- loop experimental targets. B. CLIP similarity scores for semantic reconstruction across three different estimators in the pseudo-model setup. C. Ablation study optimization guidance modalities. Top: Original ground-truth target images. Middle (Upper Bound): Generation results optimized directly via target image features (CLIP). Bottom (Ours): Generation results optimized via EEG features (MindPilot), demonstrating the model's capability to bridge the modality gap under real-world noisy conditions.
t-tests (Fig. CC). Dynamics: Iterative updates progressively aligned EEG and CLIP embeddings (Fig. 3D) These results establish MindPilot as an effective closed-loop retrieval system under realistic BEG constraints.
4.3 BEG-GUIDED STIMULUS GENERATION
We next evaluated whether MindPilot can generate optimized stimuli rather than search a fixed pool. Four candidates were selected for the next round via roulette sampling. We conducted a closed-loop stimulus optimization experiment within the 600-image space (50 categories x 12 conditions) of THINGS-EEG2. Starting from 10 randomly selected images (out of 200), we used Stable Diffusion XL-Lightnng (Lin et al., 2024) with IP-Adapter (Ye et al. 2023) to generate 2 new images per iteration. From the resulting image feature space, candidates were selected via roulette sampling for the next round of stimulation. Tab. reports different feature- based Semantic Similarity Score (SS) and Intensity Similarity Score (IS) scores, showing that our method effectively identifies target stimulus within limited iterations. Additional quantitative results are in Appendix AA.2 These results highlight the efficiency and generalizability of MindPilot for brain modulation tasks.
Table 2: Performance of MindPilot EEG semantic-driven image generation. We compare MindPilot with two high-performance EEG-to-image methods (i.e., ATM-S (Li al.; 2024), Cong- Capturer (Zhang et al. 2025)), where ATM-S serves as the upper bound. The chance- baseline for Su is also shown. Bold indicates the best performance (optimal model). Underlined indicates the second-hest performance (suboptimal model).
Low-level
High-level
Type
Method
PixCor
SSIM
AlexNet(2)
AlexNet(5)
Inception 1 CLIP
SwAV
ATM-S (Upper bound)
0.14
0.32
0.80
0.85
0.72
0.76
0.58
BEG-to-image
CongCapturer
0.15
0.33
0.73
0.81
0.65
0.68
0.59
Chance-level
0.05
0.28
0.49
0.49
0.50
0.48
0.69
Modulation
MindPilot (Ours)
0.09
0.35
0.70
0.73
0.58
0.67
0.60
Semantic-driven generation In a 10-round EEG cenaatic-gguded visual stimuli generation experi- ment, MindPilot was compared against random selection and offline black-box diffusion baseline. Across five target images, closed-loop optimization consistently reduced LI error relative to baselines (Fig, 4A). Generated images preserved semantic fidelity, as illustrated by comparisons with offline GP-based estimators (Fig. 4gg.3--) In Fig. 4 the middle row serves as conceptual ceiling, confirming that our mooxy-guided optimization algorithm is highly effective when provided with a noiseless target signal. Importantly, Tab. lab.22 benchmarks MindPilot against specialized BEG-to-iman decoders (ATM-S (Li et al.) 2024], CongCapturer (Zhang et al.) (025)). Note: These reconstruction models serve a theoretical upper bound because they map ground-truth EEG directly images, whereas
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A
Correlation anslysis between
B
Random
Offire
Obeedlccp
Regget
Improvement across subjects
EEG and CLIP
Channel OO (Indox C)
1.00
Improvement (%6
0.08
P50 Similrity
Ampliude
Channel PCz lindex 51
6
0.56
0.94
Baxten 1.
635
Channel P3 (ndox 10)
Pundoen
Channel P6 Ondex 15)
01
02
0.3 CUP Similarity
04 0.5 0.6 0J7
08
0.0
51
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Figure 5: EEG PSD-driven generation. A. Neural-visual correlation analysis. We report the correlation coefficients between EEG PSD feature similarity and image CLIP feature similarity across 10 subjects in the THINGS-EEG2 dataset. B. Examples of heuristic generation guided by PSD feature across EEG channels. C. Improvement in feature similarity scores across all 10 subjects, with statistical significance determined by paired t-tests.
MindPilot must iteratively search for the target without seeing the ground truth. Despite this more challenging setting, MindPilot substantially outperforms chance-level baselines (e.g.. SSIM: 0.35 vs. 0.28) and achieves high-level semantic alignment comparable to the upper bound (e.g., CLIP-2 way: 0.67 vs. 0.76 for ATM-S), validating the efficacy of closed-loop modulation.
These results highlight that closed-loop optimization in MindPilot alone can effectively steer image generation toward neural targets with competitive semantic fidelity, achieving performance surpris- ingly close to specialized decoders, even though our framework is designed for general-purpose closed-loop brain-stimulus optimization rather than task-specific semantic decoding.
Table 3: Performance of closed-loop iteration using semantic embedding. We used the pretrained
AlexNet as the backbone of the black-box proxy model. We evaluate the performance using two metries: Semantic Similarity Score (SS) and Intensity Similarity Score (IS). We reported per-subject SS and IS values, from initial stimuli (Random) and from optimized stimuli (Step-1 and Best). Ratios of improvements by the closed-loop framework (i.e. ASS, AIS) are reported.
Random
Step-1
Step Best
Improved by MindPilot
Subject
SS
IS
SS
IS
SS
IS
ASS (%)
AIS (%)
0.5174
0.9632
0.6686
0.9729
0.8375
0.9976
16,8859
2.4790
0.5197
0.9678
0.6675
0.9764
0.7372
0.9998
6.9701
2.3406
0.5113
0.9883
0,6597
0.9927
0.7871
0.9980
12.7402
0.5306
0.5065
0.9650
0.6498
0.9836
0.8299
0.9963
18.0136
1.2690
0.5315
0.9788
0.6937
0.9768
0.8418
0.9979
14.8151
2.1055
0.6747
0.9836
0.8099
0,9856
0.8826
0.9961
7.2634
1.0461
7
0.8838
0.8955
0.9410
0,9033
0.9500
0.9742
1.8237
7.0879
8
0,5077
0.8344
0.6838
0,9435
0.8568
0.9925
17.3066
4.8947
0.8465
0.9602
0.9251
0.9751
0.9597
0.9997
3.4662
2.4597
10
0.5128
0.8172
0.6707
0.9705
0.7687
0.9934
9.8032
2.2849
Average
0.6012
0.9354
0.7370
0.9680
0.8451
0.9946
10,9088
2.6498
PSD-driven generation MindPilot also optimized toward PSD features, directly modulating neural spectral patterns. Correlation analysis confirmed progressive alignment between image features and EEG PSD (Fig.BA). The comparisons across the random bascline, initial step (step- 1), and optimized step (step-best) demonstrate that our iteration effectively aligns neural spectral patterns with visual semantics. Neural alignment particularly evident in the early post-stimulus window 50-500 ms), where closed-loop images evoked EEG responses closely matching the target (Fig. (3). Across subjects and seeds, PSD-guided generation significantly improved similarity scores (Fig. 5P).
Together, these findings demonstrate that MindPilot extends beyond search to actively design stimuli that align with both semantic and spectral targets.
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A Whe
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Figure 6: Real-time closed-loop human experiments validate the control effects MindPilot. A.
Overview of the experimental paradigm, involving sequential image presentation and rating blocks. B. Target-guided similarity judgment: participants (N=10) first memorized a target image, then rated subsequent images their similarity score to the target, ranging from 0 [least similar] similar]. C. Example correlation between model-derived similarity scores and human ratings for single target from one participant (Participant, Target 1). D. The mean and std. of similarity score from step-1 to step-10 stimuli generated by MindPilot across all 10 participants. E-F. Correlation between the similarity score and human ratings.
[highest
4.4 REAL-TIME CLOSED-LOOP HUMAN EXPERIMENTS
To test generalization beyond proxy models, we conducted behavioral experiments with 10 partici- pants (approved by the institutional ethics committee). All participants provided written informed consent. the mental matching task, participants memorized a target image, then rated optimized images on a [0.1] similarity scale (Fig. [6. These ratings served as ground-truth labels for assess- ing MindPilot's s ability to steer latent brain states without explicit supervision. EEG was recorded concurrently to provide real-time feedback for adaptive stimulus optimization. More experimental details are provided in Appendix A.1.2
Results are shown in Fig. 6CFFF Specifically, model -erived similarity scores from EEG responses strongly correlated with human judgments (Fig. 6 C). Across iterations, participant ratings increased steadily, consistent with model predictions (Fig. 6)). These effects held across both semantic and PSID-driven targets (Fig. F)..). Crucially, the "mental matching" task (Fig. (7C-E was deployed as an ambitious "stress test" to probe the limits fine-grained semantic decoding. The moderate performance reflects the intrinsic "sim-to-real" semantic gap and the physiological constraints of non- invasive EEG, establishing a realistic baseline for this frontier challenge. Importantly, when applied to task with clearer ground muth-emooee regulation (see Fig, 4.4.1) -MiddPillo achieved highly sigmificant modulation. This contrast confirms that the closed-loop optimization framework is robust, effectively steering brain-stimulus alignment whenever the neural target is definable.

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4.4.1 HUMAN EMOTION REGULATION EXPERIMENTS
Emotion regulation experiment. Participants rated each image based on the evoked emotional valence, using a continuous scale from 0 (negative: sad, unpleasant) to (positive: happy, pleasant). This score served proxy similarity signal guide the closed-loop optimization process, enabling MindPilot to modulate emotional states without requiring explicit target EEG features. The control goal was to steer the mind states toward positive valence, as reflected by ratings. Fig. 7 a schematic diagram of the emotion regulation experiment driven by ratings. The results in Figure B show that the rewards estimated by the pseudo model are significantly correlated with the ratings given by human participants (R = 0.714, P & 0.001), and the regulatory effect on the group is very obvious in Figure 7C (0.45 0060).
A Emotion Regulation Task
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Figure 7: A. Affective evaluation: participants rated along affective dimension, ranging from [sad] to [happy]. B. Correlation between the predicted rewards from the estimator and human ratings in the emotion regulation task (i.e., Participant 4, Target 1). C. The mean and std. of similarity score from step-1 to step-10 stimuli generated by MindPilot across all 10 participants.
5 DISCUSSION AND CONCLUSION
We presented MindPilot, closed-loop framework for EEG-guided visual optimization that syn- thesizes naturalistic images to modulate brain activity. Across proxy readouts, simulation-based optimization, and human validation, MindPilot consistently converged toward neural targets while maintaining semantic interpretability and high visual fidelity. These results establish the feasibil- ity of treating the brain black-hox function and position MindPilot general approach for brain-stimulus co-optimization. This work represents a pioneering proof-of-concee ing ing-for the first time that closed-loop generation is viable even under the rigorous constraints of non-invasive EEG and naturalistic visual stimuli, expanding the frontier of non-invasive neuro-AI interfaces.
demonstrat-
Technical Impact MindPilot introduces a surrogate-guided optimization strategy that enables gradient-free guidance diffusion models toward EEG-derived objectives. This design not only preserves image quality but also demonstrates that semantically meaningful stimuli can steer neural representations associated with cognition and affect. By explicitly aligning visual features with EEG embeddings, framework provides functional link between perception and brain states, opening new avenues for brain brain-come computer interfaces (BCIs), cognitive neuroscience, and neuromodulation therapies (Jang et al. 2021 Alamia et al. 2023).
Limitations and future directions While our results are promising, several challenges remain. First, multiple distinct stimuli can elicit similar EEG responses, echoing Metamers (Feather et al. 2023). Future work should disentangle the representational factors underlying such degeneracy of EEG-conditioned generation. Second, current models not explicitly address inter-subject variability. Incorporating subject-adaptive transfer learning strategies could improve robustness and individual control accuracy (Alamia et al.) 2023). Moreover, our current experiments validate feasibility of control but are not optimized for latency. Extending MindPilot to real-time operation would enable continuous, adaptive interaction between human and Al systems. Lastly, is important to extend MindPilot other modalities to provide richer neural feedback and support higher-level control objectives (e.g., attention, memory, or affective modulation).
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ETHICS STATEMENT
All human-subject experiments were conducted in accordance with the Declaration of Helsinki. The experimental protocol was reviewed and approved by the institutional ethics committee local university. All 10 participants were informed of the experimental procedures and goals before the study and provided written informed consent prior to their participation. The experiments involved non-invasive EEG recordings while participants performed visual perception tasks, including mental matching and emotion regulation, by viewing images and providing subjective ratings. Participants compensated for their time. In addition to the data collected for this study, research also utilized the publicly available THINGS-HEE EEEG dataset, which was collected and shared under its own established ethical guidelines.
ACKNOWLEDGEMENT
This work was supported by the National Natural Science Foundation of China (62472206), National Key R&D Program of China (2055FFC3410000), Shenzhen Science and Technology Innovation Committee (RCYX2023121 1090405003, 109900000,1 JCYJ202208 (100213029) GuangDong Basic and Ap- plied Basic Research Foundation (2025A15150) 1645 ZC.I.), Shenzhen Doctoral Startup Project (RCBS202312 21 1090748082 to XK.S.), Guangdong Provincial Key Laboratory of Advanced Biomate- rials (2022B 121 22122100000 20100003), and the open research fund of the Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, the Center for Computational Science and Engineering Southern University of Science and Technology.
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SUPPLEMENTARY MATERIAL:
MINDPILOT: CLOSED-LOOP VISUAL STIMULATION OPTIMIZATION FOR BRAIN MODULATION WITH EEG-GUIDED DIFFUSION
A
APPENDIX
Appendix includes the following sections.
Sec.
A.I:
More Implementation Details
Sec.
A.2
Additional Experimental Results
Sec.
A.3
EEG black-box Proxy model Experiment Details
Sec.
A.4
Additional Interactive Searching Examples
Sec.
A.5
Additional Heuristic Generation Examples
A.1
MORE IMPLEMENTATION DETAILS
A.1.1 PUBLIC DATASETS
We conducted our experiments using the training set of the THINGS-EEG2 dataset (Gifford
2022; Grootswagers et al. 2022), which consists of a large EEG corpus from 10 human subjects
performing a visual task. The experiments used the Rapid Serial Visual Presentation (RSVP) paradigm for orthogonal target detection tasks to ensure participants' attention to the visual stimuli. All 10 participants underwent 4 equivalent experiments, resulting 10 datasets with 16,540 unique training image conditions, each repeated 4 times, and 200 unique testing image conditions, each repeated 80 times. total, this yielded (16,540 training image conditions x 4 repetitions) + (200 testing image conditions x 80 repetitions) 82, 160 image trials. The original data were recorded using a 64-chamnel EEG system with a 1000 Hz sampling rate. For preprocessing, the data were first downsampled to 250 Hz, and 17 channels were selected from the occipital and parietal regions, which are closely related to the visual system. These channels are O1, Oz, O2, PO7. PO3, POz, PO4, POS, P7, P5, P3, P1, Pz, P2, P4, P6, and P8. The EEG data were then segmented into trials, spanning from 0 to 1000 ms post-stimulus onset, with baseline correction applied using the mean the 200 ms pre-stimulus period. Multivariate noise normalization was applied to the training data (Guggenmos) et al. 2018).
A.1.2 HUMAN EXPERIMENTAL DATA ACQUISITION
Participants The Medical Ethics Committee of SOUTHERN UNIVERSITY OF SCIENCE AND TECHNOLOGY approved the experimental protocol, and participation in the neurophysiological experiment was advertised to the university population. The mean age of the participants was 21 t 3 years. All participants of the study's purpose and provided written consent before participation. Upon completion of the experiment, participants received compensation corresponding to the duration of their involvement.
cisgender males and 4 females). All participants were informed
Mental matching task The "mental matching" is a common psychological task (Geman 2006): participants memorize the target image beforehand, then view new images interactively, and their EEG is recorded while they are viewing the new images. Based on the feedback from the EEG signals, the image in the participant's 's mind can be restored. In our experiment, only the judgments from participants are known, both the target EEG signal and the target image unknown. MindPilot aims to regulate brain states to target states, rather than recovering the image. We need to optimize the visual stimuli based on the target EEG features (known priors) and the feedback from each participant. Eventually, MindPilot achieves the goal of regulation.
The mental matching task integrated real-time EEG acquisition with an image- stinulstimllation paradigm. At the beginning of each trial, the server randomly selected a target image from the THINGS-EEOO EEG2 dataset (comprising 200 images). Participants were instructed to view and mentally encode the
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target image for duration of one minute. Subsequently, they were presented with sequence of server-gent geverregeee or retrieved images, during which their EEG signals were continuously recorded. Each image was shown for seconds, and a trigger signal was sent to the EEG acquisition device at image onset to accurately mark the temporal window corresponding to image perception.
Emotion regulation task The emotion regulation task involved an online image-stimulation and self-assessment system designed to probe participants' affective responses. Participants viewed series of images presented by the server, either retrieved from an image pool or generated dynamically. Each image was displayed for seconds, after which participants rated their emotional response using a scale ranging from 0.00 to 1.00, with higher scores indicating stronger positive affect. This setup allowed for the real-time capture of subjective emotional evaluations in response to visual stimuli. Participants were instructed as follows: "If the image evokes positive, warm, or happy feelings, please assign a higher score. If the image evokes negative, sad, or disgusting feelings, please assign lower score. Try to quantify your feelings as precisely as possible and avoid extreme ratings unless strongly warranted."
Image Set The emotion regulation task utilized curated pool of 127 images. Of these, 105 were sourced from a validated Picture Database for Discrete Emotions, constructed using images drawn from established affective image sets including the International Affective Picture System (IAPS) (Lang et al. 1997), ArtPhoto (Machajdik & Hanbury 2010). and the Geneva Affective Picture Database (GAPED) GlanSGlauser & Scherer 2011], as well as selected online platforms. All images in this database were validated through large-scale subjective ratings. An additional 22 images were selected from EmoSet, large-scale dataset designed for affective computing research (Li al. 2022).
EEG recording The participants were seated in front of 24" LCD screen, with a resolution of 1920 x 1080 pixels and the refresh rate is 60 Hz. EEG data were recorded using the Neuracle NeuSen W4 wireless EEG acquisition system. A total of 59 scalp electrodes were used in accordance with the international 10-20 system, providing dense spatial coverage of frontal, central, parietal, occipital, and temporal regions. Independent reference and ground electrodes were included in the system. The sampled at frequency of 250 Hz, sufficient for capturing the temporal dynamics of cognitive and affective processing.
EEG signals
The full set of recording sites included: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6. F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, CI, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, POS, PO6, PO7, PO8, Oz, O1, and 02.
EEG preprocessing Following each image stimulus, EEG signals recorded by the acquisition device were immediately transmitted to the server. The server identified event markers (triggers) within the data stream and segmented the EEG signals accordingly, aligning each segment precisely with its corresponding image presentation. Each EEG epoch, corresponding to single image, was then subjected to real-time preprocessing to ensure low-latency signal analysis.
To accommodate the constraints of real-time processing, we did not apply computationally intensive artifact removal techniques such as Independent Component Analysis (ICA) for eliminating eye movement or muscle artifacts. Instead, lightweight preprocessing pipeline was implemented using Python's SciPy library, including a notch filter centered at 50 Hz to remove power line interference, a 1-100 Hz. band-pas, filter retain relevant neural activity, and optional signal resampling. Additionally, baseline correction was applied using the final 250 ms of the pre-stimulus period to minimize low-frequency drifts and slow fluctuations.
A.1.3 INTERACTIVE SEARCHING ALGORITHM PIPELINE
We provide a more detailed description of algorithm I The algorithm begins by initializing equal selection probabilities for each image in the candidate set, denoted (u,,) where N is the total number of images in the retrieval set. This initialization with equal probabilities reflects the absence of prior information, serving as exploratory phase. In each iteration, subset of images
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Algorithm I Closed-loop Interactive Search Algorithm
1: Initialize: Database S (u....., "v). Scores So(u,) for all j (1,... N).
2: Set uniform selection probability the initial scores So(u,) Po(uy) i
Set hyperparame-
N
ters a, S.
3: Set the number of best items to consider, k >=
4: repeat
5: Action Selection: Sample subset of images U, S2 based probabilities Pt.
6: Reward Calculation: For each image Us calculate its similarity score:
7: P/ sim (g(())) Murget)
8:
Find Top-k Best: Identify the set of indices best for the top i images in Up with the highest
scores.
9:
Step A: Direct Reward Update
10:
Compute intermediate scores Sy for all u, E U₁:
(wc) (1- (1-a) Si(u,) +..%%
Lest
11:
(1,)
i & best
12:
Step B: Spreading Update (Aggregated from Top-k)
Compute updated scores S for all uj Ω by averaging the spread from all top-k images: exp(e(u:.u,)) (1y) (1 B) (y(,)) + EiEIa (S((u) Ei exp(s(u..xz))
14:
p> is CLIP
embedding similarity.
Update Action Probabilities
16:
Update the probability Pe+1 using softmax for the next iteration:
(ug) exp(Se-1(u,)) 1+1+1
17:
exp(S++1(11)
18:
until convergence criteria met Return: arg en Su,,)
20:
U, = (ni, .2...., uNd is selected from the candidate images space based on the current selection probabilities P.(u).
Ui
Exchange
U Ui+1
Ui
u
Best Two Picks
Guided Generation
CLIP
Probabiity Sampling
Inherited from original
Figure A.1: Generating subsequent images based on the current round is achieved through crossover, mutation, and guided diffusion model. Both crossover and mutation operations preserve the relative ordering of CLIP features, thereby maintaining their semantic coherence.
For each image ut, in the subset U, the algorithm computes similarity score simn (u Utarger) by comparing the image's 's representation with the target. This similarity score acts as an immediate reward within the framework. The maximum similarity score among the subset is identified measure of the effectiveness of the current action. If sim does not meet predefined threshold, the reward is considered insufficient, and the algorithm returns the image selection step, effectively
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Algorithm 2 Closed-loop Heuristic Generation Algorithm
1: Initialize: Database Lo scores Su,),) for all i (I,..., probabilities Po(u.) So(u,)
N.
2: Parameters: parent population size N. top-k offspring count hyperparameters at, B.
3: Operators: parent selector SelectParents()) embedding crossover Crossover(:;") image genera- tor Generate(-).
4: repeat
5: Upaents - Sample(S4, Np)
Select parents based on global probabilities
6: Uoffspring - [Generate( CaanerattCCrosoverrty Crossover( ((u))) (ua. ub) SelectParents(U parents: St) |
7:
Calculate rewards (ry U U where rj < (fm((g(())) Marga).
8:
04+1 Qr u Votispring
Integrate new offspring into the database
9:
Extend St to cover 9441 with initial scores for new images.
Toest + arg topk, frr My E Uottspinng
Identify best new offssring
11:
Compute intermediate scores S% for all u, E 94+1:
((( (1 10--0 a)) Se(u.) + aa.r. T if E Theat (14(
12:
i E Thest
13:
Compute final scores 1 for all u, E Pc+1:
14:
( (1 ()s{ & EjEIm Sj(us): Eierg exp(o(u,,u1))
exp(o(us,;;)
15:
Update global probabilities Pi+1 for all Uj Q4+1: exp(5, (wx)))
16:
Pu))(u)) Eien, exp(St+1(u1)
17:
t-t+1
18:
until convergence criteria met Return: arg maX,,en), Si(uz)
19:
trying a new action within the same state. sim meets or exceeds the threshold, the algorithm proceeds to identify the images with the highest similarity scores.
As for each image / in Ut, its selection probability ( is updated by multiplying with constant factor, representing policy improvement step that prioritizes images likely to yield higher rewards. After updating, Softmax function is applied to normalize the probabilities, focusing selection weight on images more similar to the target. This normalization step reflects the transition to a new state with an updated policy. The iteration continues, with the algorithm transitioning through states by selecting or reach the upper limit of the number of iteration rounds. At this point, the loop terminates, as the algorithm has successfully identified an optimized subset of images that maximizes the similarity reward to the target.
subsets based on the refined probabilities, until simmax reaches threshold
A.1.4 HEURISTIC GENERATION ALGORITHM PIPELINE
We provide a more detailed description of algorithm As illustrated in Fig. A.1 each image set consists of three parts:
This phase integrates evolutionary operators with the diffusion generation process. Two parent instances, denoted as u, and us. are selected via roulette wheel sampling based on their fitness. We apply randomized crossover operation to their latent embeddings, employing stochastic starting index to maximize variability. The resulting hybrid embedding serves as the conditioning input for the diffusion model. This approach effectively expands the search space (exploration) while preserving the high-fidelity semantic structures the parent nodes (exploitation).
Latent Space Crossover and Generation:
To mitigate mode collapse and maintain population diversity, we intro- duce an exploratory sampling step. New candidates are drawn from the original dataset, strictly excluding instances utilized in prior iterations. This ensures the continuous introduc- tion of novel semantic elements, preventing the optimization process from stalling in local optima.
Novelty Injection:
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Elitist Preservation: We employ an elitist strategy by directly propagating the selected parent instances Ua and up to the subsequent generation. This preservation mechanism ensures monotonic improvement of the population quality and stabilizes the evolutionary trajectory.
By combining these three parts, we obtain new image set for the next iteration.
A.2
ADDITIONAL. EXPERIMENTAL RESULTS
A.2.1 JUSTIFICATION FOR THE THEORETICAL UPPER BOUND
To rigorously evaluate MindPilot, defined upper bound based the direct reconstruction performance of ATM-S. It is crucial to distinguish the roles of these two methods:
MindPilot (Our Method): Operates via iterative, closed-loop feedback. It searches for an image that matches a target brain state without ever accessing the ground-truth EEG signal corresponding to the target image.
Upper Bound: Represents a non-iterative, ideal scenario. It utilizes the ground-truth EEG signal (corresponding the known target image) directly reconstruct the visual content. This score represents the best possible performance achievable if one already possessed the perfect target brainwave, bypassing the iterative search challenge.
We selected ATM-S as this upper bound specifically because it represents the current state-of-the-an EEG-to- Image decoding. To validate this choice, we benchmarked numerous recent encoders on the SHINGS-EEGY retrieval task. As shown in Table A.T, ATM-S consistently and significantly outperformed other methods (including NICE, CogCap, and MindEyeV2) across metrics. Its superior ability to map EEG to visual semantic features makes it the most appropriate and rigorous ceiling for benchmarking our framework.
Table A.I: EEG-to-Imape -m-agee-mmae Encoder performance on THINGS-EEG2 (in-subject). We compared ATM-S against other leading decoding baselines. ATM-S achieves the highest retrieval accuracy across all w-way metrics, justifying its selection as the theoretical upper bound for our experiments.
Model
2-way Top-1
4-way Top-1
10-way Top-1
200-way Top-1
200-way Top-5
NICE Song
2024
93.23
83.93
69,22
21.67
$1.34
EEGNaiVa CogCap al 2025
2018
91.42
80.21
63.37
16.84
42.58
93.15
82.85
69.35
22.05
51.60
MB2C Wei el al 2024b
78.40
62.25
43.75
8.85
25.20
MindEyeV2 Scott et al.
2024
92.50
82,80
66.10
23.80
50.25
A et al. 2024
94.70
86,73
74.00
26.85
57.21
A.2.2 HYPERPARAMETER SENSITIVITY ANALYSIS
In the Interactive Searching experiments, we utilized fixed setting for the hyperparameters (moving factor) and 3 (reward propagation factor) based on initial pilot observations. To systemati- cally evaluate the robustness of MindPilot and identify the optimal configuration, we performed comprehensive grid search on the validation set. We explored the range of f0.1, 0.9] for both a and The results are summarized in A.2
The best performance is achieved when 0.1 and 8 0.1, yielding a similarity score of 0.6586+0.000 + 0.0879. The model maintains competitive performance across a wide range of configurations. However, we observe a general trend where lower values of 3 (controlling reward propagation spread) tend to yield higher scores, suggesting that more localized reward feedback is beneficial for this specific task. The grid search reveals that the results currently reported the main paper (which used suboptimal initialization) represent conservative lower bound of MindPilot's capabilities. The method effectively "survived" the suboptimal initialization and demonstrates a much higher performance ceiling with proper tuning (a = 0.1, 3 0.1).
In the Tab A.2 we found that the 3 parameter has a significant impact on the results. This might be because when implemented the codes, included standardized penalty term for direct rewards,
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Table A.2: Ablation study on hyperparameters a and B. The performance is evaluated on the validation set. The values represent the target score (mean i std). The best performance is achieved 0.1, 3 0.1, indicating that fner-grained updates and localized reward propagation favor
optimization.
0.1
0.3
0.5
0.7
0.9
a
0.1
0.6586 = 0.0879
0.6336 + 0.0584
0.6284 + 0.0644
0.6167 + 0.0658
0.6224 + 0.0600
0.3
0.6586 + 0.0879
0.6336 + 0.0584
0.6284 + 0.0644
0.6203 + 0.0709
0.6224 + 0.0600
0.5
0.6586 = 0.0879
0.6336 1 0.0584
0.6264 + 0.0667
0.6203 + 0.0709
0.6224 + 0.0600
0.7
0.6586 = 0.0879
0.6292 + 0.0592
0.6264 + 0.0667
0.6203 + 0.0709
0.6224 + 0.0600
0.9
0.6586
= 0.0879
0.6298 + 0.0599
0.6264 + 0.0667
0.6217 + 0.0700
0.6221 + 0.0600
in order to ensure a stable acquisition of rewards, which made finding the distribution of potential target rewards more important than single reward.
A.2.3 NECESSITY OF EEG-DRIVER OPTIMIZATION
To conclusively demonstrate that EEG feedback provides essential and unique information driving the optimization process, we conducted a rigorous control experiment comparing MindPilot against theoretical lower and upper bounds. We define the Brain Modulation task under the strict constraint that the target image is unknown to the system, and only the target brain feature is the known goal. Based on this, we situate our method's performance within three setups:
Random Image Guidance (Lower Bound/Null): No optimization performed. Removing the EEG renders the system without any optimization direction. This establishes the baseline performance.
2. EEG Feature Guidance (MindPilot): Optimization is driven solely by EEG feedback, where the target the specific brain state.
3. Target Image Guidance (Upper Bound/Ceilingg:: Optimization is driven directly by the ground-truth target image's CLIP features. We frame this as a theoretical ceiling (or Oracle) rather than bascline, because it utilizes the exact answer (the target image), which is precisely the information unavailable in our task.
The quantitative results are presented in Tab, A.3 We observe that MindPilot significantly outperforms the Random baseline across all metrics. For instance, in the closed- loop setting, MindPilot achieves a EEG score of 0. 0.70655 + 0.0537 compared to the random baseline of 0.5746 + 0.0206. This statistically significant improvement proves that the EEG signal provides essential, unique information to steer the generation. While the Target Image Guidance (Ceiling) represents the theoretical optimum, MindPilot effectively bridges the gap toward this performance level using only noisy neural feedback.
Table A.3: Control experiments with different guiding targets. Comparison of MindPilot (EEG guidance) against the theoretical ceiling (Target image guidance) and the lower bound (Random guidance). MindPilot significantly outperforms the random baseline, confirming the necessity of EEG-driven optimization.
EEG feature guidance (MindPilot)
Target image guidance (Celling)
Random image guidlance (Nall)
Type
score
CLIP score
score
CLIP score
score
CLIP score
Offline (200 pairs)
0.5369 + 0.0160
0,6580 0.0492
0.5452
0.0122
0,8464
0.0553
0.5223 + 0.0160
0,5849 0.0286
Closed-loop (10 loops)
0.5461 = 0.0141
0.7065 0.0537
0.5440 = 0.0121
0.7838 0.0699
0.5236 = 0.0132
0.5746 0.0206
A.2.4 COMPARISON WITH BLACK-BOX OPTIMIZERS AND EFFICIENCY ANALYSIS
In this section, provide detailed theoretical justification for choosing a GP-based "Surrogate Gradient" approach (MindPilot) over standard Bayesian Optimization (BO) and present empirical comparisons regarding convergence speed and query efficiency.
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Pseudo Model vs. BO We explicitly chose GP-based "Surrogate Gradient" approach rather than standard BO for two primary theoretical reasons, particularly given the high-dimensional nature of the generative latent space:
The Curse of Dimensionality: Standard BO relies on optimizing an acquisition function (e.g.. Upper Confidence Bound (UCB) or Expected Improvement (EI) over the input space. This process becomes computationally intractable and sample-ineffiiient the high-dimensional latent space of diffusion models (e.g., 64 64 4 R 16, 384 dimensions). Even when optimizing within the same CLIP embedding space (with 1024 dimensions) of Mindpilot, convergence is still extremely difficult. MindPilot circumvents this by using the proxy to guide gradient estimation rather than performing global optimization over the raw input space.
Local vs. Global Search: Standard BO aims for global optimization, which requires extensive exploration and is expensive in terms of query budget. In contrast, MindPilot uses GP to model the local landscape around the current generation to estimate an update direction. This allows us to leverage the efficiency of gradient-based guidance (similar to RL) while utilizing GP's superior ability to model EEG uncertainty and noise.
To further demonstrate the performance-speed trade-off, we conducted comprehensive benchmark comparing MindPilot (Pseudo-model) against state-of-the-art Reinforcement Learning (RI.) methods (DDPO, DPOK, D3PO) and traditional black-box optimizers (Standard BO, CMA-ES). We evaluated all methods using different query budgets (5, 10, 50, and 200 samples). For the assessment stage, selected the minimum of 5 (num budget // 2) images for the evaluation of EEG scores. Running time represents the wall-clock time required to complete the optimization process. As shown in Tab. A.4 MindPilot (Pseudo model Offline) achieves a superior trade-off between speed and performance:
1. vs. RL Baselines (DDPO/DPOK/D3PO) MindPilot converges orders of magnitude faster. For example, at 200 samples, MindPilot (Offline) takes * 73s. whereas DDPO takes 2e 1279s and D3PO takes R 1614s.
2. vs. Black-box Optimizers (BO/CMA-ES): While BO is fast at low sample counts, its computational cost scales poorly with history size due to matrix inversion (cubic complexity). At 200 samples, MindPilot (Offline) is not only faster (* 73s vs. 229s for BO) but also achieves a higher EEG score (0.5384 0.5242), validating its effectiveness in high- dimensional optimization.
Table A.4: Efficiency and Performance Benchmark. Comparison of EEG Scores (Higher is better) and Running Time (Lower is better) across different optimization methods. MindPilot demonstrates trade-off between efficiency and scalability, especially at larger sample sizes.
Method
Sunples
Samples
Samples
Samples
BBG
Tine
DOPO at 2014
0,5125 = 0.0139
107.2 19-42
0,3995 à U.000
220.27
4 17.29
0,5154 : 0.9126
653 42 =: 38.57
0,5125
0.0136
1379.59
S 87.56
BPOK
0.5093
= 0.0121
116.1 - 21.75
0.113
à 0.0124
221,99
12.14
0.5108
0.0154
692
70- 40.37
0.5101
0.0174
1332.15
164.56
DSPO
Tilg af MAN
0.5155
2: 00163
117.70
499
45115
2: 00154
285.58
2: 9.56
0,5000 0.0186
489
109.96
0,3193
2 0.0104
1614.16
128.63
05227
0,5241
220.06
0.0076
5.40
03300 100066
0..4
0,3195
0.0057
0,5227
0,0096
MindPhinn (Oilinn))
0.5229 L 0.0067
17:94 1.61
U.2264 1. 1UN777
30.96 1.2
0.9201 1 0.0015
27.74 L 4.89
0.5884 1 0.0114
7366 L: 19:2
MindPlot (Closed
0.5208 + 0.0007
39.63 7.37
0.5268 + 0.0100
42.67 9.9.3
0.5327 - 0,0091
64.67 4 10.92
0.534] - 0,0071
219.78 + 40.33
A.2.5 ADDITIONAL. RECONSTRUCTED IMAGES RESULTS
Fig. A.2 visualizes the best, medium, and worst examples of our image generation results. To obtain these results, we used EEG data from Subject-01 viewing 200 test images. Each image in the test set was sequentially used as a target for an iterative optimization process. We then evaluated the generation quality by calculating the cosine similarity of CLIP embeddings between the generated and original images, selecting the 12 best, 12 medium, and 12 worst-performing examples.
In the best group, the generated images are highly consistent with the original images in both high- level semantics and low-level visual features. In the medium group, the generated images successfully capture the core semantics, but some lewelevel visual details are distorted or lost. In the worst group, the generated images fail to preserve either the semantic or the low-level features of the original images.
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Soon
Best
Generated
Soen
Median
Gentaral
Gonerated Soon
Worst
Figure A.2: Examples of EEG semantic feature-guided visual reconstruction. From top to bottom, we exhibit the best, median, and worst 12 generated images from Subject-01, respectively. We show the images subjects had seen and the generated images.
2. HUMAN EXPERIMENTAL CORRELATION ANALYSIS
We recruited 10 participants (6 males, 4 females) for the experiment. Due to poor data quality, we excluded 4 participants males, female), retaining males, 3 females) for analysis. Our analysis in Fig. (D) and Fig. (C) is derived from all 10 participants. In the correlation analysis of Fig. 6 (E-F), only the selected 6 participants were used.
A.2.7 ITERATION IMPROVEMENT FROM DIFFERENT SUBJECTS
Based the conclusions drawn from Fig. A.6 we employ the pre-trained AlexNet end-to-end model as the black-box proxy model and use ATM-S, which is based on semantic similarity score (both the training and testing signals are synthesized), to obtain semantic representations aligned with 1x1024 CLIP image features. The experimental design involves randomly selecting 50 categories, resulting in a searching space 50 x = 600 images. Specifically, we present the iterative performance improvements for three different targets randomly selected from the test set, with results reported for Subjects 1, 7, 8, and 10. As shown in Fig. A.55 we calculate the EEG feature similarity of Subject 1, 7, 8. and 10 random, step-1, and step-best the iterative process respectively.
A.2.8 PERFORMANCE OF DIFFERENT TARGET IMAGES ACROSS SUBJECTS
We report the results of iterative optimization using different targets in two different cases. The results for each subject are shown, along with the average percentage improvement across 5 random seeds. For the EEG semantic feature case, we determined that training and testing with synthetic ERG yielded the highest accuracy based on the retrieval performance shown Fig. A.6] For the PSD) feature, selected 3 images using the method described in Section and supplemented the iterative improvement performance.
A.3 VALIDITY VERIFICATION OF SYNTHETIC EEG
To evaluate the performance of our EEG encoding models, we compare the synthetic EEG signals generated by two deep neural networks (DNNs)-AlexNe, and CORnet-S-wii real EEG data. Here's 's step-by-step breakdown of how we processed and compared the data.
We selected 17 specific channels from the original 63-channel EEG dataset, focusing on those most relevant to visual processing. It ensured that we focused on neural regions most directly involved in responding to the visual stimuli. For each stimulus, resulting in a representative dataset for each stimulus. This reduced the dimensionality the data, making it easier to compare with synthetic data. We used pretrained end-to-end encoding model to
averaged the EEG signals across all trials,
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A
CLIP embedding
PSD feature
Emotion estimator
Similarity Score of Stimult
0.8
Simiarity Score Stimull
0.95
0.90
Predicted Rewards
0.7
sub-01
0.6
0.6
0.80
0.4
0.75
-0.082
0.70
R 00.199
0.2
R= = 0.474
P 0.517
p 0.315
0.006
0.00
0.25
0.50
0.75
Human Ratings
Ratings
Human Ratings
B
CLIP embedding
PSD feature
Emotion estimator
1.0
Similarity Score of Stimuli
Similrity of Stimuli
0.7
0.65
0.9
Predicted Rewards
sub-02
0.60
0.8
0.5
0.4
0.50
0.6
0.3
0.45
0.116
0.5
0.099
R = 0.660
0.360
0.437
< 0.001
0.25
0.50
0.75
0.2
0.4
0.6
0.2
0.4
0.6
Human Ratings
Human Ratings
Human Ratings
CLIP embedding
PSD feature
Emotion estimator
0.7
Similarity Score of Stimuli
Similority Score of Stimuli
0.7
0.6
Predicted Rewards
sub-03
0.6
0.4
0.5
0.5
0.2
0.4
0.022
0.221 P =
R 0.884
P
0.862
0.079
P 0.001
0.0
0.3
0.4 0.6
0.4 0.6
0.5 0.6 0.7
Ratings
Ratings
Human Ratings
Figure A.3: Correlations analysis for S1-S3. (Left and Middle) Correlations between the similarity score of stimulus and human rating in the mental matching task in Target 1. (Right) Correlations between the predicted rewards from the estimator and human ratings in the emotion regulation task in Target 1.
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A
CLIP embedding
PSD feature
Emotion estimator
Similarity Score of Stimult
0.8
Similarity Score of Stimull
0.8
Predicted Rewards
0.7
0.9
sub-04
0.6
0.6
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= 0.299
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Figure A.4: Correlations analysis for $4-S6. (Left and Middle) Correlations between the similarity score of stimulus and human rating in the mental matching task in Target (Right) Correlations between the predicted rewards from the estimator and human ratings in the emotion regulation task in Target
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Sub-01
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Figure A.5: Comparison of improved performance by different targets. We present the similarity scores EEG features generated by random stimulation, open-loop stimulation (step 1), and step-best stimulation, in comparison to the target features. Each subject randomly selected 3 images from the searching space as target images.
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sub-01-Pretrained
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A
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cifttww: 5
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Figure A.6: Retrieval accuracy under different training and test datasets. Zero-shot retrieval per- formance of EEG data from different sources in Subject 1 and Subject 8 using ATM-S in different Settings. AlexNet and CORnet-S used the first row were both pre-trained end-to-end models, and the second row was randomly initialized end-to-end.
generate synthetic EEG signals based on the visual stimuli. The model captures the mapping between the visual imput and the resulting EEG signals using deep neural networks. These synthetic signals represent the neural responses predicted by the model in response to the stimuli.
Table A.5: MSE Values for synthesized EEG
Pretrained
Random Init
Subject
Average
AlexNet CORnet-S
AlexNet
CORnet-S
Sub-01
0.1095
0.1126
0.1161
0.0994
0.1094
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0.0764
0.0788
0.0840
0.0994
0.0847
Sub-03
0.0787
0.0806
0.0816
0.0910
0.0830
Sub-04
0,0652
0.0664
0.0662
0.1011
0.0747
Sub-05
0.0493
0.0515
0.0704
0.0975
0.0672
Sub-06
0.0690
0.0719
0.0498
0.0966
0.0718
Sub-07
0.1267
0.1300
0.0914
0.1312
0.1198
Sub-08
0,0718
0.0727
0.1038
0.1165
0.0912
Sub-09
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0.0563
0.0781
0.0756
0.0657
Sub-10
0.1122
0.1151
0.0961
0.1149
0.1096
Average
0.0810
0.0832
0.0838
0.1023
0.0876
Tab. A.5 presents the mean squared error (MSE) between the synthetic EEG signals generated by AlexNet and CORnet-S, and the real EEG signals for 10 subjects in THINGS EEBG. The MSE was computed for each individual test sample and then averaged across the entire test set. Lower MSE values indicate better alignment between the synthetic and real EEG signals.
From the comparison shown in the Fig. A.6 the retrieval accuracy for S-S (both training and testing sets consist of generated signals) is significantly higher than other categories, including T-T (both
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training and testing sets consist of real signals), T-S (training set consists of real signals, testing set consists of generated signals), and S-T (training set consists of generated signals, testing set consists of real signals), under both AlexNet and CORnet-S models. This indicates:
Advantages of generated signals Supported by black-box ANN models (c.g., AlexNet and CORnet- S), generated signals perform significantly better in retrieval tasks compared real signals. In particular, the highest retrieval accuracy for S-S demonstrates the consistency and model adaptability of generated signals in this retrieval task. Different ANN models (e.g., AlexNet and CORnet-S) show consistent superiority in the retrieval tasks for generated signals, indicating that generated signals are more easily captured and distinguished by black-box proxy models.
Correlation analysis In Fig. A.7 for each time point, we compute the Pearson correlation between the real EEG signal and the synthetic signals. This analysis enables us to visualize how well each model replicates the temporal structure of real neural responses visual stimuli, with shaded regions representing the standard deviation across samples. Notably, the results reveal that supervised models (e.g., ResNet) frequently outperform representation learning models (e.g., CLIP) in capturing these temporal dynamics. This contrasts with fMRI studies prioritizing semantic alignment (Wang et al.) 2023), likely due to the high temporal resolution of non-invasive EEG, which is inherently more sensitive to transient, low-to-mid-level visual features (c.g., edges, textures) during early processing stages (Groen al., 2017). ResNet, optimized for hierarchical feature extraction, aligns better with these "shallower" visual dynamics than the abstract semantic representations of CLIP. Furthermore, the dimensionality reduction (PCA) inherent to the encoding pipeline may favor the robust hierarchical features of ResNet over the complex joint-space representations of contrastive models for the specific task of Image-to-BEGG synthesis.
Variance analysis In Fig. A.8 compute the variance across all samples and time points for each channel, providing a measure of the overall variability of the EEG signals in response to different visual stimuli and their temporal dynamics. This variance help identify channels with the highest variability, which may be useful for selecting specific channels for further analysis or modulation. In Fig. A.9 show the variance and standard deviation of the EEG signals computed across samples for each time point, and then averaged across channels. This analysis allows us to assess how signal variability evolves over time. By comparing the real EEG data with synthetic data generated by AlexNet and CORnet-S, we can evaluate how well each model captures the temporal variability present in the real EEG signals.
Correlation distribution analysis In Fig. A.10 we compute the pearson correlation coefficient between the averaged real EEG data and the synthetic data for each stimulus, measuring how well the synthetic data matches the real EEG per-sample basis. The histogram shows the distribution of correlation coefficients across all samples for both AlexNet and CORnet-S. A higher concentration of peaks near higher Pearson coefficients indicates better alignment between the synthetic data and the real EEG, reflecting superior model performance.
These findings highlight the robustness of our EEG encoding models, demonstrating their ability that not only mimic the structural features of real EEG data but also capture the realistic variability seen in neural responses visual stimuli. This suggests that our models are effective in approximating the neural representations underlying visual processing.
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Figure A.7: Time-resolved pearson correlation between ground truth and synthetic EEG signals. These synthetic signals were generated using a linearizing model fitted on feature maps from range of pre-trained deep neural networks (AlexNet, CORnet-S, ResNet50, MoCo, ViT-B-32, OpenCIIP. ViT-B-32, DINO2-ViT ViT-B-14 14, DINO-VTT-B-16. and
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Figure A.8: Variance across different channels for different visual stimulus and temporal dynamics
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A.4
ADDITIONAL SEARCHING EXAMPLES OF SEMANTIC REPRESENTATION
A.4.1
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Figure A.II: Some searching examples Subject 8, 4, 4, and By setting different targets, present examples where the stimulus retrieved at the end of the iterative optimization process increasingly approximates the true category.
A.4.2 SOME FAILURE EXAMPLES INTERACTIVE SEARCHING
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Figure A.12: Some failure examples from Subject 8. By setting different targets, we show examples where the stimulus retrieved at the end of the iteration is far from the true category. In these examples, the final retrieved stimulus exhibits varying degrees of similarity the target image.
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A.5
ADDITIONAL CONTROLLABLE GENERATION EXAMPLES OF PSD FEATURE
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Figure A.13: Illustration of the closed-loop iterative process for Subject 1. targets were presented, each based on a specific similarity measure (details in Target Features of EEG, Section 4.I), with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
Three distinct visual
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Figure A.14: Illustration of the closed-loop iterative process for Subject Three distinct visual targets were presented, each based on specific similarity measure (details in Target Features of EEG, Section 4.T. with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.15: Illustration of the closed-loop iterative process for Subject 3. Three distinct visual targets were presented, each based on a specific similarity measure (details in Target Features of EEG, Section 4.I), with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.16: Illustration of the closed-loop iterative process for Subject 4. Three distinct visual targets were presented, each based on a specific similarity measure (details in Target Features of EEG, Section I.I) with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and target features.
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Figure A.17: Illustration of the closed-loop iterative process for Subject 5. targets were presented, each based specific similarity measure (details in Target Features of EEG, Section 4.J. with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.18: Illustration of the closed-loop iterative process for Subject 6. Three distinct visual targets presented, each based specific similarity measure (details in Target Features of EEG, Section I.I.. with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.19: Illustration of the closed-loop iterative process for Subject 7. Three distinct visual targets were presented, each based on a specific similarity measure (details Target Features of EEG, Section 4.1) with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.20: Illustration of the closed-loop iterative process for Subject 8. Three distinct visual targets were presented, each based on specific similarity measure (details in Target Features of EEG, Section 4.1), 4.I), with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.21: Illustration of the closed-loop iterative process for Subject 9, Three distinct visual targets were presented, each based on specific similarity measure (details in Target Features of EEG, Section 4.1), 4.II, with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Figure A.22: Illustration of the closed-loop iterative process for Subject 10. Three distinct visual targets were presented, each based on a specific similarity measure (details Target Features of EEG, Section 4.I), with new visual stimuli iteratively generated for each target. The left panel illustrates the time-domain evolution of neural responses across iterations. The right panel depicts the changes in similarity (green curve) and loss (blue curve, scaled) between the current stage features and the target features.
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Reviews:
[{"review_id": "z0JZD8XrHI", "reviewer": "Reviewer_pRhZ", "summary": "This paper proposes MindPilot: a closed-loop visual stimulation optimization framework based on EEG feedback. It shows a participant a batch of images, records their EEG, and calculates a score indicating \"how similar\" their brain state is to a target state.", "questions": "Human experiment details need more transparency. The paper mentions excluding 4 participants due to poor data quality but does not specify the exact exclusion criteria (e.g., artifact proportion exceeding a certain threshold). It is recommended to clearly state the data quality threshold criteria to enhance the experiment's reproducibility and rigor.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "Novel and significant problem: Closed-loop visual modulation using non-invasive EEG, advancing towards \"neural feedback-guided generative modeling.\"\n\nPractical implementation: Gradient-free black-box guidance + simple score propagation + roulette wheel sampling, resulting in low engineering cost.\n\nMulti-level validation: Proxy simulation (Tables 1, 3), dual targets of semantics and PSD (Figs. 3–5), small-scale real-time human closed-loop experiments (Fig. 6), and the provision of anonymous code.", "weaknesses": "1. Motivation and advantages of the \"pseudo-model\" need highlighting: The current discussion is insufficient regarding why Gaussian Process was chosen as the pseudo-model over other black-box optimizers (e.g., Bayesian Optimization). It is recommended to add a brief discussion or comparative experiment in the main text or appendix to explain the rationale for selecting GP and its advantages relative to other methods.\n\n\n2. The hyperparameters α and β in Eqs. (3) and (4) are set as fixed values, but their specific chosen values or selection criteria are not reported, nor is there any sensitivity/ablation analysis. It is recommended to supplement the specific values, search ranges, and their impact on performance in the main text or appendix. Furthermore, the ambiguity caused by using the same notation as the crossover/mutation ratios in §4.3 should be clarified (or the symbols should be changed).\n\n\n3. The core premise of the paper is that the proxy model can substitute for the real human brain in closed-loop optimization. However, the data in Table 1 severely weaken this premise: the maximum Pearson correlation between any proxy model and real EEG is only ~0.17. This value is too low. Successful optimization demonstrated on the proxy model does not necessarily generalize to the real human brain. The authors are advised to provide evidence demonstrating a systematic link between the optimization trends observed in the proxy model and those in the real human brain.\n\n\n4. Figure captions could be more detailed. For instance, how exactly is the ordinate \"EEG semantic feature similarity\" in Fig.3.D calculated? What do the bold and underlining in Table 1 signify? The authors are recommended to briefly explain this.\n\n\n5. The current experiments cannot distinguish whether the success of the closed-loop optimization stems from the unique neural information in the EEG or from visual information related to CLIP features that the proxy model learned from the images. To conclusively prove that EEG drives the optimization, the following control experiment is essential: A CLIP-only baseline: Remove the EEG feedback and directly optimize the image similarity to the target within the CLIP space. This baseline serves to quantify MindPilot's performance gain over pure visual semantic optimization.", "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": "8BRpt7En4I", "reviewer": "Reviewer_h3JQ", "summary": "This paper focuses on a novel problem: given neural activity recordings evoked by a visual stimulus (e.g., EEG), how can we find or generate the most likely visual stimulus? I believe this problem has potential value for related research in neuroscience. In this work, the authors first propose an iterative optimization method that progressively updates the probability of each image in an image database by computing the correlation between EEG representations and the images, thereby identifying the most likely visual stimulus. Subsequently, the paper introduces an EEG-guided image generation model capable of attempting to generate the possible visual stimulus. The authors conducted corresponding evaluations, and I believe the evaluation is also sufficient.", "questions": "1. Normally, better visual encoders and representation learning strategies are expected to learn more accurate and robust visual representations. However, according to the results in Table 1, representations obtained from ResNet trained on image classification tasks perform the best. What could be the reason for this?\n\n2. Continuing from the above question, I have noticed a paper [1] emphasizing that visual representations obtained through contrastive learning are more consistent with human brain activity. Although that study focused on fMRI data, why does a different conclusion appear when it comes to EEG signals?\n\n3. How should we understand ATM-S as the performance upper bound for image generation in Table 2?\n\n4. In Figure 4C, what do the images in each row represent?\n\n[1] Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset. Nature Machine Intelligence.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper addresses a research problem that I believe has considerable practical value.\n\n2. According to the authors’ evaluation, the method proposed in this paper is effective.\n\n3. To facilitate a better evaluation, the authors conducted a visual rating experiment with human participants and demonstrated that the results obtained by their method are highly correlated with the ground truth provided by the participants.\n\n4. The evaluation in the paper is thorough.\n\n5. The paper is well-constructed, with figures and formatting presented nicely.", "weaknesses": "This paper has some limitations, but I think the authors have provided a thorough discussion of them in the “Limitations” section on page 9.", "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": "hcG1yt8Wed", "reviewer": "Reviewer_uwSG", "summary": "he paper presents MindPilot, an approach to control image generation from EEG-BCI. The area is under active research, and the present paper is a continuation of previous work.", "questions": "1. I would like to stress novelty compared to many previous works that are \"not closed loop\" -- as I think the present paper also has very limited closed-loop contribution\n\n2. I think the results in the most critical parts of the paper are weak and the authors are trying to oversell them.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "Strengths:\n\n- The paper tackles a challenging and interesting problem.\n- The paper is well-written and aims for rigorous experimentation.", "weaknesses": "Weaknesses:\n\n- The paper claims to be the first to tackle the problem, but the principles of the approach have been extensively explored in the last years: \n\nMany core-related works are missing, which hinders the novelty statement of the papers. Examples (not exhaustive list) of a few recent references of well-known papers and some even using similar CLIP approaches, generative modeling, and diffusion processes:\n\nhttps://proceedings.neurips.cc/paper_files/paper/2024/hash/84bad835faaf48f24d990072bb5b80ee-Abstract-Conference.html\nhttps://ieeexplore.ieee.org/abstract/document/10798967\nhttps://arxiv.org/abs/2306.16934\nhttps://arxiv.org/abs/2506.11151\nhttps://dl.acm.org/doi/abs/10.1145/3379337.3415821\nhttps://openaccess.thecvf.com/content/CVPR2022/html/Davis_Brain-Supervised_Image_Editing_CVPR_2022_paper.html\nhttps://arxiv.org/abs/2308.02510\nhttps://www.nature.com/articles/s42003-025-07731-7\nhttps://dl.acm.org/doi/full/10.1145/3716553.3750786\nhttps://proceedings.neurips.cc/paper_files/paper/2024/hash/4540d267eeec4e5dbd9dae9448f0b739-Abstract-Conference.html\nhttps://arxiv.org/abs/2508.20705\n\n- The reward and spreading operations are somewhat ad-hoc. I understand that the reward model cannot directly find the best matches, but this makes the robustness and replication of the approach problematic as the connection across the models is not straightforward. Figure 3d also shows that the alignment task is not easy and the results are not clearly demonstrating that simple CLIP with the underlying ad-hoc process is suitable.\n\n- There are good attempts to evalute the approach. \n\n- It is known that EEG does not generally contain fine-grained semantic codes; it means that the pattern of responses across time and sensors partially aligns with the structure of semantic space for their geometry. Thus, I feel that some of the wordings in the paper are not in line with this general understanding.\n\n- The closed loop experiment has results that do not support the message of the paper (Fig 6 C&D). In fact, when tested  “in-vivo” the model does not seem to perform.  This is also problematic as the paper claims novelty in closed-loop, but does not demonstrate that convincingly.\n\nMinors:\n\n- I don’t understand why it is highlighted that a brain is non-differentiable. I think we do not know the exact mechanisms of learning in the biological/cellular system, especially when making such simplified mathematical statements for analyzing EEG.  There is emerging evidence (https://academic.oup.com/pnasnexus/article/3/7/pgae261/7702306), but I am not sure how meaningful this is for the submitted paper.\n\n- Closed-loop is only a small portion of the paper, while most of the paper relies on a single existing dataset.", "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": "k9yHOtcmXX", "reviewer": "Reviewer_LDfZ", "summary": "This paper presents MindPilot, an innovative closed-loop EEG-guided visual stimulus optimization framework.The core concept of this framework is to treat the human brain as an indivisible black-box system, iteratively guiding diffusion models to generate images through EEG feedback signals.This framework introduces a pseudo-model to provide surrogate gradients, enabling gradient-free optimization for various neural objectives such as semantic or spectral EEG features.The paper validates MindPilot through agent-based model simulations and human experiments, demonstrating its potential in EEG-guided image retrieval, generation, and real-time human brain control.", "questions": "1.  Could the authors elaborate on whether MindPilot could be generalized to other modalities (e.g. fMRI)?\n\n2. How do pseudo-model guidance and traditional gradient-free reinforcement learning methods differ in their convergence properties?\n\n3. Additionally, there are minor inconsistencies in the formatting of references within the text. For example, several NeurIPS/ICLR-style citations only provide the conference name without volume, issue, or page information (e.g., Bashivan et al., 2019; Black et al., 2024; Luo et al., 2024a). Similarly, the citation for “Transactions on Machine Learning Research, 2024” (Oquab et al., 2024) omits both volume and page numbers. We recommend that the authors carefully review these entries and align them with the journal article format, ensuring that volume, issue, and page numbers are included where available.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Novelty: Innovation: First to propose a closed-loop image generation framework based on EEG signal diffusion models.\n\nTechnical soundness: Technical Rationality: The pseudo-model guidance mechanism is clearly designed and effectively replaces explicit gradient computation.\n\nExperimental breadth: Demonstrates stable convergence characteristics in both simulated and human experiments.\n\nPotential impact: Openes up new avenues for non-invasive neural modulation and human-machine collaborative adaptation studies.", "weaknesses": "EEG Individual Variability: Insufficient discussion of inter-subject EEG variability; a brief explanation could be added to emphasize the model's adaptability and generalization capability.", "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)