Neural-Driven Image Editing
Authors: Pengfei Zhou
Jie Xia
Xiaopeng Peng
Wangbo Zhao
Zilong Ye
Zekai Li
Suorong Yang
Jiadong Pan
Yuanxiang Chen
Ziqiao Wang
Kai Wang
Qian Zheng
Xiaojun Chang
Gang Pan
Shurong Dong
Kaipeng Zhang
Yang You
Venue: NeurIPS 2025 poster ()
Keywords: Brain-Computer Interface
Image Editing
Visual Generation
Generative Models
Diffusion Models
Primary area: applications
TL;DR: Loong-X enables hands-free image editing using multimodal neural signals, achieving performance comparable to text-driven methods by combining BCIs with the proposed diffusion-based generative methods.

Abstract: Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. 
LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent.
To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT).
Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language.
Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0.4812 vs. 0.4637) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https://loongx1.github.io.

Neural-Driven Image Editing
Pengfei Zhou Jie Xia Xiaopeng Peng"t, Wangbo Zhao', Zilong Ye Zekai Li', Suorong Yang' Jiadong Pan2*, Yuanxiang Chen', Ziqiao Wang', Kai Wang', Qian Zheng,, Xiaojun Chang', Gang Pan", Shurong Dong" Kaipeng Zhang Yang You! 'NUS ZZJU 'RIT "NJU UUTC Shanghaii AI Lab 'SII "Hangzhou RongNao Tech zp/4wp@outlokk.com [jiexia, dongshurong @ zju.edu.cn. zhangkaipeng @ pjlab.org.cn
Motion
INIRS
PPG
EEG
Speech
BCI
INIRS
LED transmitter
PPG
PD receiver
EEG
EEG electrode
Molion
Reference
Figure
Illustration of LoongX for hands-free image editing via multimodal neural signals.
Abstract
Traditional image editing typically relies on manual prompting, making it labor- intensive and inaccessible individuals with limited motor control language abilities. Leveraging recent advances in crain-computer interfaces (BCIs) and generative models, we propose LoongX, hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of ttate-of-the-ar the- art diffusion models trained on comprehensive dataset of 23.928 image editing pairs, each paired with synchronized electroencephaalgraphh (EEG), functional near- infrared spectroscopy (fNIRS), photoplethy.moorrahyy (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specine features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semanties via fine-tuning Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experi- ments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0..8112 vs. 0.4636) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https://loonge github. io.
diffusion transformer (DiT).
'Equal contribution; 'Core contributor; 'Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Introduction
Image editing involves manipulating digital visuals to achieve desired effects, significantly impacting fields like advertising, entertainment, and scientific visualization [1]. Traditionally, this task demands extensive manual effort and technical expertise. Advances in generative models have streamlined instruction- basse image editing through automated pipelines [2-4]. Nevertheless, these methods still heavily depend intensive user inputs, such as text prompts [5, 6], visual references like masks or sketches [7, 8). and physical operations like dragging [9-12]. Such reliance limits efficiency and accessibility, especially for users with motor or communication impairments.
To address these challenges, alternative imput modalities have been explored [13-15] for image editing. Among these, computrr interfaces (BCIs) provide a promising possibility with their recent advancement in hardware precision [16, 17]. Starting from early attempts in passive tasks such as mental state recognition [18] and neural activity analysis [19, 20], BCIs have begun to be involved in more active generative tasks such as neural-driven chat [21] and visual content creation [22, 23].
However, existing approaches remain limited to the use of single-modalit data such as electroen- cephalography (EEG) [22, 24] or functional magnetic resonance imaging (fMRI) [25], which is insufficient capture nuanced user intentions for enabling complex editing tasks. In practice, physio- logical signals from different modalities can offer complementary insights into cognitive states such as attention, motivation, and emotional regulation [26-32], underscoring the need for multimodal neural information integration.
Given the limited exploration of this emerging area, here we ask three key research questions (RQs):
RQI. Can neural signals alone drive instruction- image editing?
RQ2. If yes, what kind of information do multimodal neural signals contribute?
RQ3. How do neural-signal conditions compare and complement natural- languagg instructions?
To answer these questions, we construct L-Mind, comprehensive multimodal dataset compris- ing 23,928 image pairs with synchronously collected EEG, functional near infrared spectroscopy (INIRS) [33], photoplethysmography (PPG) [34], head motion, and speech signals from 12 partici- pants conceiving image editing tasks. Captured using a wireless, lightweight BCI system that supports unconstrained head movements and speech [35], L-Mind offers higher ecological validity under natural real-world conditions and supports robust training of brain-supervised generative models.
Building L-Mind, we propose LoongX, hands-free image editing approach that innovatively integrates the proposed multimodal neural signal fusion strategy with a diffusion transformer (DiT) to translate neural intent into image edits. Unlike prior single-modal methods, LoongX integrates EEG, fNIRS, PPG, and head motion signals, extracting explicit user intentions from EEG signals across multiple scalp regions, incorporating cognitive load and emotional valence data from fNIRS, and capturing stress and engagement indicators through PPG and motion signals. We introduce two new modules manage diverse multimodal input: cross-scale state space (CS3) encoder for robust feature extraction and a dynamic gated fusion (DGF) module for comprehensive multimodal integration. These encoders are pretrained via contrastive learning on combined large-scale datasets and L-Mind to align neural features with semantic text embeddings.
Extensive experiments qualitatively and quantitatively demonstrate the feasibility of neural-driven image editing. Integrated multimodal neural signals achieve performance comparable to text-driven baselines (CLIP-I: 0.6605 0.6558; DINO: 0.4812 12 0.4637). Combined neural signals with speech instructions surpass text prompts alone (CLIP-T: 0.2588 vs. 0.2549). Ablation studies verify the effectiveness of proposed modules and further explore the contribution of each signal, showing that EEG + fNIRS contribute most among signals, and the Oz and Fpz sites, as EEG input channels, represent the key brain region. These findings underscore LoongX's potential to facilitate intuitive. inclusive image editing and inspire future Au interaction.
Our main contributions are summarized as follows:
1) I.MMind, a multimodal dataset with 23,928 image-editing pairs featuring synchronized EEG, INIRS, PPG, motion, and speech signals collected natural settings.
2) LoongX, a novel neural-driven editing method with CS3 and DGF modules for effective feature extraction and multimodal integration (see the effect in Fig. 1).
3) Extensive experiments validate multimodal neural signals' effectiveness and provide insights into modality contributions and their synergy with speech-based inputs.

---

2 Related Works
2.1 Brain-supervised Generation
As an emerging technology. brain-computer interface (BCI) builds direct communication between the brain and devices by decoding neural signals [36, 37]. Advances in machine learning have improved its accuracy, enabling brain- brain-guided generative methods for visual content creation. Several recent methods integrate neurophyssological data (e.g., IMRL, EEG, or INIRS) with generative models [22, 38, 39]. For instance, CMVDM aligns IMRI features with semantics for image synthesis [400] and the MindEye series further lifts the resolution of generated images from decoded fMRI [41, 42].
OneLLM leverages the large fMIR dataset for multimodal alignment in a multimodal large language model [16]. DreamDiffusion produces images from EEG via temporal masked modeling [22].
EiGe extends the idea to dynamic video content [43]. While Davis et al. [24] initially explore
brain-guided semantic image editing using a generative adversarial network, this work is limited to facial images and EEG signals. Moreover, Adamic et al. [44] reconstructs visual images from brain
activity measured by fNTRS.
Unlike previous studies, our data are collected using a wireless BCI system (Fig. 2) as participants conceive instruction-based image edits. Compared with fMRI methods, our framework combines lightweight EEG, INIRS, PPG, and head-motion signals, which can support greater portability and broader real-word world applicability. To the best of our knowledge, this is the first work to fully leverage all these signals for instruction-basee image editing, focusing on improved neural feature encoding and optimized multimodal fusion strategies.
2.2 Instruction-based Image Editing
Recent generative models like GPT-4o [45] and Gemini [46] have evolved from basic question answering to advanced image editing by interpreting user instructions. Modern instruction- bbsed image editing agents integrate multimodal inputs. including text, images, and videos, to accurately identify and apply visual edits [13]. Leveraging learned multimodal representations, these agents interpret instructions from input, localize relevant regions, and perform targeted modifications [47- 491. Recent approaches. such InstructPix2Pix [1], UltraEdit [2], MagicBrush [50]. MIGE [51]. and ACE [52] improve
edits guided by natural language prompts. Speech-driver image editing [14] was also explored, highlighting the feasibility of hands-free interaction but still limited
by linguistic expressiveness in recorded speech.
Despite these advancements, achieving efficient, delicate, prompt-free image editing remains chal- lenging. Our work addresses this gap, exploring neural-signal-drin cognitive intent directly for image manipulation. significantly enhance accessibility and interaction efficiency in future BCI-enabled applications, particularly benefiting individuals with physical disabilities.
editing agents to decode is believed that the findings in this work can
3 Dataset
3.1 Data Collection
We collect 23,928 editing samples (22,728 training, 1,200 testing) from 12 participants using the setup depicted in Fig. 2. Participants wear our multimodal sensor while viewing image-text pairs sourced from FEED-Data-Edii [53] a 25-inch monitor (resolution: 1980 1080). The measured EEG, INIRS, and PPG physiological signals are streamed in real-time via Bluetooth 5.3, synchronized and aligned via lab streaming layer by the proprietary Lab Recorder software [54]. Participants simultancously read displayed editing instructions aloud, providing audio speech signals.
Experiments are conducted in a quiet, temperature-controlle room (24 consistent humidity), starting at AM daily. EEG signals are collected via non-invasive hydrogel electrodes, replaced every five hours to maintain signal quality. The experimental room is shielded from sunlight prevent interference with fNIRS and PPG signals. Sessions start and end with participant-controie audio recording and are marked by image names. Data from inactive intervals excluded.
Each session (Fig. 2) starts and ends with user-initiated audio recording and labeled by the paired image. A 1-second cross- cross-fixation fixation follows each image pair, with breaks every 100 images. Twelve
3

---

(a) Multimodal Sensing
Stimuli
(b) Image Editing Database
Wireless
Synchronized
Streaming
Add Bunglosses and
Coño the oare
Meosu rement dessione
Background
Speech
Recording
lerrive
Obfece
Speech
H4pp
reprreuor
tedane
L-Mind
Global Edhing
EEG
Remove the two charecters
recaine
testame
Fertulia smorth
Baating ang
teatove egg
INIRS
texture rasecc
aaurr add
PPG
Motion
Rest
Multimodal Signals
The I-Mind dataset comprises 23,928 multimodal editing samples, each including an original image, ground truth text editing instruction, ground truth edited image, well as measured EEG, INIRS, PPG, motion and speech signals. (a) Multimodal data collection pipeline; (b) Illustration and statisties of 35 types of image editing tasks.
Figure 2:
healthy college students (6 female, 6 male; mean age: 24.5 # 2.5 years old) with normal or corrected vision participated. All participants gave informed consent and received financial compensation. The study was officially approved by the corresponding institute's ethics committee.
3.2 Data Preprocessing
EEG. Four EEG channels (Pz, Fp2, Fpz, Oz; sampled at 250 Hz) undergo band-pass filtering (1-80 Hz) and notch filtering (48-52 Hz) to remove drifts, noise, and powerline interference. Ocular artifacts in Fp2 and Fpz are retained to capture eye movements.
INIRS. Six-channel INIRS signals (735 nm, 850 nm) converted to relative hemoglobin concentra- tion changes (HbO, HbR, HbT) using the Modified Beer-Lambert law. Optical density change is computed AA(A) = log (A))))((AA). Concentration changes caleulated
AHbO AHbR
AA(AI) AA(A))
Hbo
HbR
DPF L
EHO
HbR_
Hemodynamic signals (AHbO, AHbR, and АHbT, where AHPT = АHbO + AHbR) are band- pass filtered (0.01-0.5 Hz) isolate relevant neural responses, averaged per hemisphere to reflect task-related brain activity.
PPG and motion Four-channel PPG signals (735 nm, 850 nm) averaged per hemisphere via adaptive average pooling and filtered (0.5-4 Hz) to extract cardiac-related hemodynamic signals that reflect heart rate variability. Motion data from six-axis sensor (12.5 Hz), capturing triaxial linear acceleration and angular velocity, characterizes head movements. See supplement for more details.
4 Method
As illustrated in Fig. 3, LoongX extracts multimodal features from diverse neural signals and fuses them into a shared latent space in pair-wise manner. Using Diffusion Transformer (DiT), the original image is translated into an edited image conditioned on the fused features. Following three research questions, we conduct a multi-label classification experiment (Sec. A.2) showing that EEG outperforms noise by 20%, and fusing all signals yields the highest score. Combining neural signals with text achieves the best mAP, confirming modality complementarity. An input length of 8, 199 gives the best performance with higher computational cost, motivating our framework's 's design: cross-scale state-space encoder for long sequences and dynamic gated fusion for feature integration.

---

Diffusion Transformer (DiT)
Original Image
hr
In
DGF
é DGF
CLIPT
DGF
* Speech Dec
CS3
Speech Enc
Rep Repiece background with Slany Night duplicate cats onto
000 EEG
D PPG
D00000
BBE INIRS
000 Motion
Neural-Driven Edited Image
Specch (Optional)
è Trehable Params
Frozen Params
Irput Sigral
Feature Pyramid Σ2
Concrtecate
Content Embedding
Conditional Embedding
AAPON
F
Pemmic
GAP
:
X x
BXCXL
AAPr
Layer
Sigmoid
MLP
MLP
Split
Mask
MLP
B OXCXL
S3M
Embedted Fasture
Gating Net
Afline Net
dm
x
S3M
AAPM)
Normalization
Length
Embedsing
x XCX&4 Xx
x
① Ellet-stir sodttion
Illm Mtkepticttinn
Space (CS3)
Dynamic Rusion (DGF)
Figure 3: Overview of our proposed LoongX method for hands-free image editing. Receiving an input image, LoongX outputs edited image using neural signals (and optional speech) as conditions.
4.1 Cross-Scale State Space Encoding
CS3 encoder extracts multi-scale features from diverse signals using an adaptive feature pyramid. To further capture dynamic spatio-temporal patterns beyond the fixed pyramid, CS3 uses a structured state space model (S3M) [55] for efficient long-sequence encoding with linear complexity. To manage cost, it uses cross-fcature mechanism that separately encodes temporal and channel information.
Pyramid Encoding. A single modality input signal CC is fed into -llayer adaptive average pooling (AAP) module:
(P 1, ..., N) AAP Lo (S), 8 d 2
where set N = and d = 64 for BEG. The extracted embedding is computed as the concatenation of the feature pyramid P = Concat (P.}))
State Space Encoding. To fully exploit both temporal and channel-wise dependencies in neural signals, we design a cross-shaped spatiotemporal encoding scheme, where one axis focuses on temporal patterns and the other on channel-wise dynamics.
Specifically, the input signal S is padded from length Lo to L, where Spad RC with signal intensity normalized to [-1, The padded signals and its permuted version Spm R are passed to two parallel S3M blocks, (3) and (3) respectively:
(1) (3) (Spaa):
z (Spm),
(3)
where each S3M block uses the continuous-time diagonal state-space model:
é(t) = Ac(t) + Bs(t), z(t) = Ce(t) + Ds(t).
(4)
where e(() denotes the latent state at time and A, B, Ć. D are diagonal matrices that parameter- ize state transitions, input injection, state-to-output mapping, and direct input-to-output mapping, respectively. Due to the diagonal parameterization, the S3M block admits efficient computation with
linear complexity O log L). Through the S3M blocks, ( is down-sampled from length L to dm- yielding (1) E IBC Cxdm (2) is permuted and down-sampled via an AAP, giving (2) e Cxd,,
Cross-Pyramia Aggregation. channel dimension, resulting in:
The encoder merges multi-scale and temporal streams along the
F = ANP (att, ( P. 7 CCxd).
(5)
where d' = d,, + dp N2i The concatenated feature was projected via Adaptive Nonlinear Projection (ANP), which consists of two fully-connected layers, LayerNorm, ReLU, and dropout. The final embeded feature F e C''xL' is obtained.

---

4.2 Dynamic Gated Multimodal Fusion
We propose the Dynamic Gated Fusion (DGF) module to dynamically bind a pair of content and condition embeddings to a unified latent space. which is further aligned with text embeddings. Our DGF includes gate mixing, adaptive affine modulation, and dynamic masking block.
Gated Mixing. We calculate instance-wise and layer-wise mean μ and variance or from input content embedding (e.g., EEG) X E further fusion into H E R x while emphasising informative channels and suppressing noise:
"iRxxx x'xxxxxx and condition embedding (e.g., PPG) Y R x for
Xx,, Tix Xx,l 1c
x (inst) 2 +E
Txx
Minst
iistt
10
Player
Inyer
E(xor Mayer) ) +
C' ott
C'lix
each entry EReX1
(6)
where is regularization term for numerical stability and 1 is a unit vector. A 1-D Gating Network (eoLU adaptively mixing statisties:
G(.) = o (Conv ReLU - Conv) is used to compute per-channel weights E [0, c
from C,
/ g C Pinst + (1 - g
Player:
Fiist + (1 Olayer- g
(7)
The content feature is then normalized by the adaptively gated mean and standard deviation as: X X uo/o.
Adaptive A Affine Modulation. The conditional feature was averaged by global average pooling (GAP) Y -, Y.,., This averaged feature is then passed the Affine Network v.. which consists of a multi-layer perceptron (MLP). The output is split into two affine coefficients 7 and 3:
hy, 3] (Y), = = (1+ X +
(8)
Dynamic Masking. Channel importance scores Sc
E, e, are computed select the top-k channels (A: = [,Y], p 0.7) among the modulated features. Additionally, binary mask Me (0, 1] is applied:
H.: M. H..-
Finally, the fused latent feature H. is residually fused with the original prompt/text embeddings before being fed into DiT decoder. Because DGF operates on arbitrary (C, L) tensors, it handles four types of modality fusion in LoongX: EEG-PPG, fNIRS-Motion, neural-prompt, and neural-pooled-promee
4.3 Conditional Diffusion
The fused latent representation conditions DiT backbone [56] for image editing. The DiT model accepts the encoded input image and fused latent feature h and outputs the edited image aligned with the semantic intention via fine-tuning.
Specifically, DIT predicts a velocity vo t, h) that used iteratively refine the latent image in T uniform steps,
1
1 I
ve 1I t, h),
Vt 11/T.2/1 1).
(10)
T
At inference time we apply (10) until = 0, yielding the edited image I.
4.4 Pre-training and Finetuning
We adopt two-phase process: 1) neural signal encoders (EEG is the most important one) are pretrained on neuro-text corpora, compressing public data and L-Mind, 2) The full stack is optionally fine-tuned with paired original images and ground truth edited images.

---

Pretraining, Signal encoders are pretrained to align with semantic embeddings using large- scale cognitive datasets [57, 58] and L-Mind. CS3 encoders (EEG+ PPG and fNIRS Motion, respectively) are aligned to frozen text embeddings via symmetric NT-Xent loss:
Loo
1
Σ log Σj
M
log
e
(11)
2M
e%
E
where sij (z. (//T//TTT 2, and 4j neural and text embeddings, and M is the number of neural modalities. During pretraining, signal encoders are learned while text encoders stay frozen.
Finetuning, Encoders and the DiT are finetuned jointly on L-Mind, mapping user neural patterns to editing target following a standard diffusion objective that minimizes the mean-squared velocity error. For an input image 1o and Gaussian noise E E~N I), where a is the cumulated noise schedule:
MSE = E, 00 (T, h) - (Vor - v1 oi Io)
(12)
v(I,,t,))
Experiment
To answer the research questions (RQs) asked Sec. 1, we conduct comprehensive evaluation to validate the effectiveness of LoongX on the test set of L-Mind. This section first describes the experimental setup, evaluation metrics, and implementation details, and presents results of comprehensive quantitative evaluations, detailed breakdown analyses, and qualitative assessments.
5.1 Experimental Setup
Implementation Details. All models trained eight NVIDIA H100 GPUs. Text prompts embedded by T5-XXL [59] and CLIP [60]; neural signal streams are encoded by the proposed CS3. Unless stated otherwise, EEG montage (Fz, Fp2. 02, Pz. Cz) is sampled at 256 Hz and down-sampled to 32 Hz after band-pass filtering. Inference runs at 8 steps with classifier-free guidance w 4. We choose OminiControl [48] as our baseline as it supports the rext-conditioned image-editing based on DiTs. We also implement LoongX using only neural signals (EEG, INIRS, PPG and Motion) and using both text prompts and neural signals. We load the pretrained weights from FLUX. 1-dev and use low-rank approximation (LoRA) for fine-tuning (learning rate 1.0, weight decay 0.01).
Evaluation Metries. We mainly use five metries for quantitative assessment following [50]:
1) L1 Distance (Mean Absolute Error): Calculating the average absolute difference between corresponding pixels in edited and ground truth tar images.
2) L2 Distance (Mean Squared Error): Computing the average squared difference between pixels. Penalizes large errors more heavily than LI.
3) CLIP-I Score:
Evaluating semantic similarity between model-edited images and ground truth target images, which focuses on global semanties of editing results.
4) DINO Score:
Assessing feature similarity between editing results and ground truth. Com- pared with CLIP, is believed that DINO features capture fine- grained structural similarity that correlates with perceived preservation of identity, pose, and local geometry [61].
5) CLIP-T Score: Evaluating semantic similarity between image and textual prompts.
5.2 Reliability of Neural Signals
Neural signals can serve as reliable indicators to drive image editing, outper- forming text-instructed baselines on key metrics.
Answer RQI:
As shown in Table 1, neural-signallonly LoongX outperforms the text-based OminiControl baseline in semantic discriminability (CLIP-I: 0.6605 vs. 0.6558) and robustness (DINO: 0.4812 vs. 0.4636),

---

Table Comparison between baseline methods and two LoongX paradigms: (i) neural signals only and (ii) neural signals enhanced by speech. Mean + 95% confidence interval (CI) over three runs.
Mcthods
L1 (1)
1.2 (L)
CLIP-I
DINO (t)
CLIP-T (1)
OminiControl (Text)
0.2632 20.006
0.1161
20.010
0.6558 10.010
0,4636 20.017
0.2541 20.005
OminiControl (Speech)
0.2714 20.006
0.1209 10.005
0.6146 1: ooa
0.3717 20.013
0.2501
10.005
LoongX (Neural Signals)
0.2509 1: ops
0.1029 10.000
0.6605 = now
0.4812 2: 01s
0.2436
1:0 008
LoongX (Signals+Speech)
0.2594 = 0006
0.
0.1080 20.000
0.6374 a: 0o8
0.4205 2 014
0.2588
10.009 009
CUP-T (Dur)
CLI--
cueg
CUP4
DNO
DIOO
All Neural Signele e Speech
CLIPTT (Oun))
L1
EEG ThIR8 + PPG
BEG INRRS + PPG Molion
CLP en
EEG TNIRS
EEG Only
L2
BEG FD2)
EEGIPH PPa)
Metric
GLIPT (GT)
EEG Fea
BEG
CLIP4
GLP-T (Ouns)
CUP-
TLL
CLI-TT (Ours)
ONNO
DIN
GUIP
CUP- (01)
CUP-T CUP-T (eT)
(Ouru)
CLIP-T
0.0
0.1
0.2
0.3
0.4
0.5
0.8
0.7
CLIP
Score
Figure 4: Evaluation of different signal combi-
Figure Evaluation results on different brain re- gion signals where LoongX is trained and tested on each respective EEG channel.
nations on the proposed DGF module.
which demonstrates the potential of neural signals as a standalone modality that carries rich semantic information for image editing. The slightly higher LI and L2 errors indicate better preservation of semantic fidelity over pixel-level accuracy. Combining speech cues with neural signals boosts semantic alignment, reaching the highest CLIP-T score of 0.2588 and demonstrating their joint effectiveness in capturing nuanced user intentions for hands-free image editing.
5.3 Ablation Studies on Modality Contribution
Answer RQ2: Different neural signal modalities contribute complementary strengths, enhanc- ing discriminabilltyy robustness, and semantic precision, respectively.
Modality contributions are compared in Fig. EEG signals alone enable basic high-level semantic editing, supported by the semantic discriminability of the extracted features (CLIP-I: 0.5457). Inte- grating fNIRS significantly improves feature robustness (DINO: from 0.2963 0.4811), highlighting the complementary nature of hemodynamic responses in enhancing signal completeness and structural fidelity. Including PPG and Motion improves global physiological awareness and indicates sensitivity to subtle engagement patterns (e.g., heart rate and user movements) that express editing intent. They both contribute to the features' robustness and completeness to ensure stable CLIP-T score gains.
We show the contribution of each individual EEG channel in Fig. 5, where each channel corresponds specific scalp region as detailed in Table 3. The occipital cortex channel (Oz), which is in charge of visual processing, emerges as dominant in global editing effect (CLIP-I: 0.6619) and robustness (DINO: 0.4873) finer details, affirming its critical role in basic visual perception and processing tasks. Conversely, the frontopolar cortex (Fpz) provides superior semantic alignment (CLIP-T: 0.2481), consistent with association with more complex cognitive processes. Specifically, Fpz provides decision control and attention regulation compared with basic visual perception provided by Oz, which precisely confirms the discovery patterns medical anatomy. This channel-specific analysis provides insights valuable for targeted applications constrained hardware settings.
8

---

5.4 Breakdown Analysis: Neural vs. Language-based Conditions
Answer RQ3: Neural signals excel in low-level visual edits, while language excels in high-level semantics; combining both yields comprehensive and optimal control.
The analysis of text and neural-driven image editing is shown in Fig. 6. Using pure neural sig- nals (N) is particularly effective for global tex- ture editing, with higher CLIP-I scores highlight- ing their strong visual and structural consistency. Neural signals also outperform other modalities in several tasks like object removal and back- ground blur, reflecting their strength in convey- ing intuitive intent, though they remain limited in handling complex semantics like text editing. Text instructions (T) are inherently stronger in high-level semantic tasks (e.g., image restora- tion), which indicates their advantage in deserib- ing instruction details. Combined neural and speech (N+S) signals achieve the highest seman- tic alignment (CLIP-T: 0. 0.2588), showcasing the superior effectiveness of hybrid conditioning in capturing complex user intentions. Overall, neu- ral signals are more effective for low-level visual edits, while neural and text-based approaches each provide complementary advantages.
sanipe
saturate
CUP-T MH0
restore
DIMO
exposure
CLING
add
contrast
modify
recolor
remove
colorize
add
color adjust
adjust
Global
BG
Text
bright
CLIP-T
blur
blur
remove
adjust
replace
restore
restore
repostt
add
roplace
comoui
rocgior
leohal
adiuat
Figure Breakdown results of text and neural- driven image editing. BG: background.
5.5 Ablation Studies on Model Architecture
Each component in the LoongX architecture contributes uniquely, and their composition (especially with pretraining) maximizes the performance potential, leading to improved editing accuracy across metries. The ablation study in Table 2 is conducted on the fused neural signals and speech to evaluate the impact of each proposed module. It is found that CS3 encoder enhances feature completeness and smoothness, leading to 5% reduction in L2 error. The DGF module improves semantic alignment with textual instructions, yielding a 3.5% increase in CLIP-T. Supplemented by pre-tree pre-training. LoongX reaches optimal performance, indicating the important role of robust and representation learning and multimodal alignment in maximizing editing performance.
Table Ablation studies on the architecture of LoongX. Mean 95% CI over three runs.
Pretrain
CS3
DGF
L1 (L)
L2 (4)
CLIP-I(1)
DINO (t)
CLIP-T (f)
0.2645 .0.000
0.1099
20.003
0.5966
20.000
0.3948
20.011
0.2584 40.010
0.2567 +0.008
0.1047
+0.004
0.6408
+0.010
0.4588
+0.012
0.2248 +0.007
0.2629 20.005
0.1106
000
0.6025
20.009
0.3992
20.011
0.2620
1:0.007
0.2648 +0.008
0.1124 00.004
0.6319
-00000
0.4162
+0.012
0,2534
+00 008
0.2594 20. 006
0.1080 1: 004
0.6374
1: on9
0.4205
20.012
0.2588 1.0.00
5.6 Qualitative Analysis and Discussion
Qualitative examples confirm LoongX's intuitive editing capabilities, though performance may vary slightly in scenarios with complex or ambiguous intentions. Qualitative results presented in Fig. 7 demonstrate that neural-driver LoongX can successfully achieve various visual and structural modifications, such as background replacement and global adjustments. However, the fused neural- language method better captures nuanced instructions involving abstract semantics (e.g.. "modify the text information" Fig. 7(d)).
Limitations and future work. It is noted that while the neural signals and combined methods perform better in multiple tasks such as object manipulation (e.g., letting the cat look down in Fig. 7(a)), the text- based method handles spatial manipulation more effectively (c.g., "place the cat

---

Original
Ground Truth
Neural (Ours)
Neural + Speech (Ours)
Text Only
Background Editing
Keep cat, replace background with stadium, place looking down
Object Editing
Add giant mushrooms and flowers magical garden girl's size to scene
Global Editing
Crop the size of the image make close-up of the face
Found Cun't GALA
(d) Text Editing
Modify the text information on tablet the man's hand
Figure 7: Qualitative evaluation comparing the text prompt-based method and our neural-driven
methods for four editing categories: (a) background, (b) object, (c) global, and (d) text editing.
above" in Fig. 7())). Despite significant advancements with multimodal fusion, entity consistency (e.g., the style of the little girl in Fig. 7(b)) remains a challenge, which is limited by the capabilities of backbone image editing models the time this work was mainly completed. Moreover, highly abstract or ambiguous instructions occasionally still pose challenges (e.g., "winged white animal in Fig. (f)".. Several more failure cases shown in Fig. 14, indicating areas where further refinement in entity interpretation and neural-data based disambiguation remains necessary.
Overall, the experiments validate LoongX's 's efficacy robust, intuitive interface leveraging neu- ral signals for image editing. Multimodal integration, particularly neural and linguistic fusion, emerges essential for capturing comprehensive intent. These findings advocate for future research focusing on improving semantic interpretation fidelity from neural signals and exploring adaptive methodologies for further enhancing accessibility and precision in cognitive-driven ereative technologies, Incremental fine-tuning for new unseen users is also worth exploration in practice.
6 Conclusion
We presented LoongX, a novel framework for hands-free image editing by conditioning diffusion models on multimodal neural signals, achieving performance comparable or superior traditional text-driven baselines. Looking ahead, the portability of our wireless setup opens exciting possibilities real-world applications immersive virtual environments. Future directions include integrating LoongX with VR/XR to support intuitive cognitive interaction and aligning neural representations with emerging world models [62, 63] project human intention into interactive synthetic world, enabling mind-drived control in virtual realities.
10

---

Acknowledgments and Disclosure of Funding
We sincerely thank Prof. Tat-Seng Chua (National University of Singapore) for his valuable comments and suggestions during the drafting of this work. We also thank Dr. Zhiwei Tang (Alibaba) for his constructive guidance during the rebuttal. This work is sponsored by the NUS Startup Grant (Presidential Young Professorship, Singapore MOE Tier-1 Grant, ByteDance Grant, NUS ARTIC Grant, Apple Grant, Alibaba Grant, and Google Grant for TPU usage, and also supported by the STI2030-Major Projects (No. 20212D0200401,, Zhejiang Province Key R&D Programs (No. 2024C03001, 2024C03007, 2024C01031, 2025C01 137, 2025C02165), Zhejiang Province High- Level Talent Special Support Plan (No. 2022R52042), and the Fundamental Research Funds for the Central Universities (No. 2025ZFJH01).
References
[1] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to fol- low image editing instructions. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392-184022 2023.
[2] Haozhe Zhao, Xiaojian Shawn Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. UltraEdit: instruction-based fine- ine-grained image editing at scale. Advances in Neural Information Processing Systems, 37:3058-300 -0993, 2024.
[3] Bingyuan Wang. Qifeng Chen, and Zeyu Wang. Diffusion-based visual art creation: A survey and new perspectives. ACM Computing Surveys, 57(10))1-377, 2024.
[4] OpenAI. Introducing 4o image generation, 2025.
[5] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfrr Aberman, Yael Pritch, and Daniel Cohen-on Prompt-to-prompt image editing with cross-attention control. In International Conference on Learning Representations, 2023.
[6] Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. Emu edit: Precise image editing via recognition and generation tasks. In /EEE/CVF Conference Computer Vision and Pattern Recognition, pages 8871- 8879, 2024.
[7] Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, Jose Lezama, Lu Jiang. Ming- Hsuan Yang, Kevin Patrick Murphy, William T Freeman, Michael Rubinstein, et al. Muse: Text-to-image generation via masked generative transformers. In International Conference on Machine Learning, pages 4055-4075, 2023.
[8] Yu Zeng, Zhe Lin, and Vishal M Patel. Sketchedit: Mask-free local image manipulation with partial sketches. In IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 5951-5961, 2022.
[9] Yujun Shi, Chuhui Xue, Jun Hao Liew, Jiachun Pan, Hanshu Yan, Wenqing Zhang, Vincent YF Tan, and Song Bai. Dragdiffusion: Hamessing diffusion models for interactive point- based image editing. In JEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8839-8849. 2024.
[10] Joonghyuk Shin, Dachyeon Choi, and Jaesik Park. InstantDrag: Improving interactivity in drag-based image editing. In SIGGRAPH Asia, pages 1-10, 2024.
[11] Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Zhiheng Liu, Qifeng Chen, and Yujun Shen. Magicquill: An intelligent interactive image editing system. arXiv preprint 2rXiv:2411.09700 2024.
[12] Ziqi Jiang, Zhen Wang, and Long Chen. CLIPDrag: Combining text-based and drag-based instructions for image editing. In International Conference on Learning Representations, 2025.
[13] Xincheng Shuai, Henghui Ding, Xingjun Ma, Rongcheng Tu, Yu-Gang Jiang, and Dacheng Tao. A survey of multimodal-guided image editing with text-to-image diffusion models. arXiv preprint arXiv:2406.1 14555, 2024.
[14] Yuming Jiang. Ziqi Huang, Xingang Pan, Chen Change Loy, and Ziwei Liu. Talk-to-edit: Fine-grained facial editing via dialog. IEEF/CVF International Conference on Computer Vision, pages 13799-13800 2021.

---

[15] Yue Yang. Kaipeng Zhang, Yuying Ge, Wenqi Shao, Zeyue Xue, Yu Qiao, and Ping Luo. Align, adapt and inject: Audio rudio-guided image generation, editing and stylization. In IEEE International Conference on Acousties, Speech and Signal Processing, pages 3475-3479, 2024.
[16] Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xiangyu Yue. OneLLM: One framework to align all modalities with language. In IEEE/CVE Conference on Computer Vision and Pattern Recognition, pages 26584-265955 2024.
[17] Emily J Allen, Ghislain St- Yve, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, et al. A massive 7t fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nature Neuroscience, 25(1):116-126, 2022.
[18] Yiming Wang, Bin Zhang, and Lamei Di. Research progress of EEG-based emotion recogni- tion: a survey. ACM Computing Surveys, 56(1 1):1-49, 2024.
[19] Yiqun Duan, Jinzhao Zhou, Zhen Wang, Yu-Kai Wang, and Chin-teng Lin. Dewave: Discrete encoding of eeg waves for EEG to text translation. Advances in Neural Information Processing Systems, 16:9907-9918 2023.
[20] Yohann Benchetrit, Hubert Banville, and Jean-Remi King. Brain decoding: toward real-time reconstruction of visual perception. In International Conference on Learning Representations, 2024.
[21] Dünya Baradari, Nataliya Kosmyna, Oscar Petrov, Rebecah Kaplun, and Pattie Maes. Neu- roChat: A neuroadaptive AI chatbot for customizing learning experiences. arXiv preprint arXiv:2503.0m 07599, 2025.
[22] Yunpeng Bai, Xintao Wang. Yan-Pei Cao. Yixiao Ge, Chun Yuan, and Ying Shan. DreamDif- fusion: High-quality EEG-to-image generation with temporal masked signal modeling and CLIP alignment. In European Conference on Computer Vision, pages 472-488, 2024.
[23] Xuan-Hao Liu, Yan-Kai Liu, Yansen Wang. Kan Ren, Hanwen Shi, Zilong Wang, Dongsheng Li, Bao-Liang Lu, and Wei-Long Zheng. EEG2Video: Towards decoding dynamic visual perception from EEG signals. Advances in Neural Information Processing Systems, 37:72245-
72273, 2024.
[24] Keith M. Davis, III, Carlos de la Torre-Ortiz, and Tuukka Ruotsalo. Brain-supervised image editing. In IEEF/CVF Conference Computer Vision and Pattern Recognition, pages 18480- 18489, 2022.
[25] Jingyang Huo, Yikai Wang, Yun Wang, Xuelin Qian, Chong Li, Yanwei Fu, and Jianfeng Feng. Neuropictor: Refining fMRI-to-image reconstruction via mnulti-individull pretraining and multi-level modulation. In European Conference on Computer Vision, pages 56-73, 2024.
[26] Jing Cai, Alex E. Hadjinicolaou, Angelique C. Paulk, Daniel J. Soper, Tian Xia, Alexan- der F. Wang, John D. Rolston, R. Mark Richardson, Ziv M. Williams, and Sydney S. Cash. Natural language processing models reveal neural dynamics of human conversation. Nature Communications, 16(1):3376, 2025.
[27] Niklas Brake, Flavie Duc, Alexander Rokos, Francis Arseneau, Shiva Shahiri, Anmar Khadra, and Gilles Plourde. A neurophysiological basis for aperiodic eeg and the background spectral trend. Nature Communications, 15(1):1514, 2024.
[28] Yuxuan Li, Jesse K. Pazdera, and Michael J. Kahana. EEG decoders track memory dynamics. Nature Communications, 15(1):2981, 2024.
[29] Yipeng Yu, Cunle Qian, Zhaohui Wu, and Gang Pan. Mind-controlled ratbot: A brain-to-brain system. In IEEE International Conference on Pervasive Computing and Communication Workshops, pages
231, 2014.
[30] Xuyang Si, Hao He, Jun Yu, and Dong Ming. Cross-subject emotion recognition brain- computer interface based on INIRS and DBJNet. Cyborg and Bionic Systems, 4:0045, 2023.
[31] Muhammad Arsalan and Muhammad Majid. Human stress classification during public speak- ing using physiological signals. Computers in Biology and Medicine, 133:104377, 2021.
12

---

[32] Amir Tazarv, Shervin Labbaf, Steven M. Reich, Nikil Dutt, Amir M. Rahmani. and Marco Levorato. Personalized stress monitoring using wearable sensors in everyday settings. In Annual International Conference of the IEEF Engineering in Medicine & Biology Society, pages 7332-7335, 2021.
[33] Lu Cao, Dandan Huang, Yue Zhang, Xiaowei Jiang, and Yanan Chen. Brain decoding using INIRS. In AAAI Conference on Artificial Intelligence, pages 12602-12611, 2021.
[34]
Chang-Hee Han, Euijin Kim, and Chang-HWang Im. Development of brain brain-computer interface toggle switch with low false- soss---osivvee rate using Sensors, 20(2):348, 2020.
modulated photoplethysmography.
[35] Boyu Li, Mingjie Li, Jie Xia, Hao Jin, Shurong Dong, and Jikui Luo. Hybrid integrated wearable patch for brain EEG-INIRS monitoring. Sensors, 24(15):4847, 2024.
[36] Bradley Edelman, Shuailei Zhang, Gerwin Schalk, Peter Brunner, Gernot Müller-Putz, Cuntai Guan, and Bin He. Non-invasive brain-computer interfaces: State of the art and trends. IEEE Reviews in Biomedical Engineering, 18:26-49, 2025.
[37] Seif Eldawlatly. On the role of generative artificial intelligence in the development of brain-computer interfaces. BMC Biomedical Engineering, 6:4, 2024.
[38] Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In /EEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463 2023.
[39] Kalyan Tripathy, Zachary E Markow, Andrew K Fishell, Arefeh Sherafati, Tracy M Burns- Yocum, Mariel L Schroeder, Alexandra M Svoboda, Adam T Eggebrecht, Mark Anastasio, Bradley L Schlaggar, et al. Decoding visual information from high-density diffuse optical tomography neuroimaging data. Neurolmage. 226: 2021.
[40] Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao. Xiaolong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, and Baochang Zhang. Controllable mind visual diffusion model. In AAAI Conference on Artificial Intelligence, pages
2024.
[41] Paul S. Scotti, Atmadeep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Ethan Cohen, Aidan Dempster, Nathalie Verlinde, Elad Yundler, David Weisherg, Kenneth A. Norman, and Tanishq Mathew Abraham. Reconstructing the mind's 's eye: fMRI-to-image with contrastive learning and diffusion priors. Advances in Neural Information Processing Systems, 2023.
[42] Paul S. Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva, Reese Kneeland, Tong Chen, Ashutosh Narang, Charan Santhirasegaran, Jonathan Xu, Thomas Naselaris, Kenneth A. Norman, and Tanishq Mathew Abraham. MindEye2: Shared-subject models enable fMRI- to-image with I hour of data. In International Conference Machine Learning, pages 44038-44059, 2024.
[43] Xuan-Hao Liu, Yan-Kai Liu, Yansen Wang, Kan Ren, Hanwen Shi, Zilong Wang. Dongsheng Li. Bao-Liang Lu, and Wei-Long Zheng. EEG2Video: Towards decoding dynamic visual perception from EEG signals. Advances in Neural Information Processing Systems, 2024.
[44] Michel Adamic, Wellington Avelino, Anna Brandenberger, Bryan Chiang, Hunter Davis, Stephen Fay, Andrew Gregory, Aayush Gupta, Raphael Hotter, Grace Jiang, et al. Progress towards decoding visual imagery via INIRS. arXiv preprint arXiv:2406. 07662, 2024.
[45] OpenAI. GPT-4 technical report, 2024.
[46] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312. 1805, 2023.
[47] Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, and Yi Yang. In-context edit: Enabling instructional image editing with in-context generation in large scale diffusion transformer. arXiv preprint arXiv:2504.20690 2025.
[48] Zhenxiong Tan, Songhua Liu, Xingyi Yang. Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for diffusion transformer. arXiv preprint arX5v.2411.15098, 2024.
[49] Zhenxiong Tan, Qiaochu Xue, Xingyi Yang, Songhua Liu, and Xinchao Wang. OminiControl2: Efficient conditioning for diffusion transformers. arXiv preprint 2rXiv:2503.08280, 2025.
13

---

[50] Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for cnarruction-uudeee image editing. Advances in Neural Information Processing Systems, 36:31428 31449, 2023.
[51] Xueyun Tian, Wei Li, Bingbing Xu, Yige Yuan, Yuanzhuo Wang, and Huawei Shen. MIGE: A unified framework for multimodal instruction-basee image generation and editing. arXiv preprint arXiv:2502.21299 2025.
[52] Chaojie Mao, Jingfeng Zhang, Yulin Pan, Zeyinzi Jiang, Zhen Han, Yu Liu, and Jingren Zhou. ACE++ Instruction-based image creation and editing via context-aware content filling. arXiv 2025.
preprint arxiv:2501.024:4000
[53] Yuying Ge, Sijie Zhao, Chen Li, Yixiao Ge, and Ying Shan. SEED-Data-Fddi technical report:
A hybrid dataset for instructional image editing. arXiv preprint 2tte::2005000000 2024.
[54] Tristan Stenner. Chadwick Boulay, Matthew Grivich, David Medine, Christian Kothe, Tobias Herzke, Christian Schausner, Giso Grimm, X Loem, Arthur Biancarelli, Boris Mansencal, Paul Maanen, Jérémy Frey, Jidong Chen, Kyle Crane, Samuel Powell, Pierre Clisson, and Paul Scen/liblsl: v1.16.2, 2023.
Fix.
[55] Albert Gu, Karan Goel, and Christopher Re. Efficiently modeling long sequences with structured state spaces. In International Conference on Learning Representations, 2022.
[56] William Peebles and Saining Xie. Scalable diffusion models with transformers. In IEEE/CVF
International Conference on Computer Vision, pages 4195
4195-4205 4205, 2023.
[57] Nicolás Nieto, Victoria Peterson, Hugo Leonardo Rufiner, Juan Esteban Kamienkowski, and Ruben Spies. Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition. Scientific Data, 9(1):52, 2022.
[58] Matthew Ning, Sudan Duwadi, Meryem A Yücel, Alexander Von Lühmann, David A Boas, and Kamal Sen. fnirs dataset during complex scene analysis. Frontiers in Human Neuroscience, 18: 1329086, 2024.
[59] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1-67, 2020.
[60] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark. et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8766 8763, 2021.
[61] Yang Zhou, Zichong Chen, and Hui Huang. Deformable one-shot face stylization via dino semantic guidance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7787-7796, 2024.
[62] Xiaofeng Mao, Shaoheng Lin, Zhen Li, Chuanhao Li, Wenshuo Peng. Tong He, Jiangmiao Pang, Mingmin Chi, Yu Qiao, and Kaipeng Zhang. Yume: An interactive world generation model. arXiv preprint arXiv:2507. 2025.
[63] Chaojun Ni, Guosheng Zhao, Xiaofeng Wang. Zheng Zhu, Wenkang Qin, Guan Huang, Chen Liu, Yuyin Chen, Yida Wang, Xueyang Zhang, et al. Recondreamer: Crafting world models for driving scene reconstruction via online restoration. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 1559-1566 2020,
[64] Peter Praamstra, Luc Boutsen, and Glyn W. Humphreys. Frontoparietal control of spatial attention and motor intention in human eeg. Journal of Neurophysiology. 94(1):764-774, 2005. PMID: 15744008.
[65] Ankita Sengupta, Sanjna Banerjee, Suhas Ganesh, Shrey Grover, and Devarajan Sridharan. The right posterior parietal cortex mediates spatial reorienting of attentional choice bias. Nature Communications, 15(1):6938, 2024.
[66] Naomi P. Friedman and Trevor W. Robbins. The role of prefrontal cortex in cognitive control and exccutive function. Neunopsychopharnccooog.. 47(1):72-89, 2022.
[67] Jae-Hyun Kim, Dong-Hyun Ma, Funji Jung, Ilsong Choi, and Seung-Hee Lee. Gated feed- forward inhibition in the frontal cortex releases goal-directed action. Nature Neuroscience, 24(10):1452-1466 2021.
14

---

[68] Bülent Y. Özkara. An introduction to the event-related potential technique. Journal of Consumer and Consumption Research, 11(2):437-441, 2019.
[69] Kavya Agrawal, Shashwat Mishra, Shashwat Sinha, Vineeta Khemchandani,, Sushil Chandra, learning-based based workload identification
and Nachiket Milind Wadalkar. Chapter 29 machine learning- using functional near-infrared spectroscopy (fnirs) data. In M.A. Ansari, R.S. Anand, Pragati Tripathi, Rajat Mehrotra, and Md Belal Bin Heyat, editors, Artificial Intelligence in Biomedical and Modern Healthcare Informatics, pages 299-312. Academic Press, 2025.
[70] Syed Hammad Nazeer, Noman Naseer, Muhammad Jawad Khan, and Keum-Shik Hong. Noninvasive brain- compuer interfaces using fNIRS, EEG, and hybrid EEG-INIRS. In Ayman S. El-Baz and Jasjit S. Suri, editors, Brain- Brain-Computer Interfaces, pages 297-326. Elsevier, 2025.
[71] Zhihan Lv, Liang Qiao, Qingjun Wang, and Francesco Piccialli. Advanced machine-learning methods for brain-computer interfacing. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1865):6888-166 1698, 2020.
[72] Xiang Zhang, Lina Yao. Shuai Zhang. Salil Kanhere, Michael Sheng, and Yunhao Liu. Internet of things meets brain-compute interface: A unified deep learning framework for enabling human- thing cognitive interactivity. IEEE Internet of Things Journal, 6(2):2084-209)) 2018.
[73] Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In /EEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463 June 2023.
[74] Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, and Mubarak Shah. Generative adversarial networks conditioned by brain signals. In IEEE International Conference on Computer Vision, pages 3430-3438, 2017.
[75] Jiaxuan Chen, Yu Qi, Yueming Wang, and Gang Pan. Mind artist: Creating artistic snapshots with human thought. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 27207-27217, 2024.
[76] Yasheng Sun, Bohan Li, Mingchen Zhuge, Deng-Ping Fan, Salman Khan, Fahad Shahbaz Khan, and Hideki Koike. Connecting dreams with visual brainstorming instruction. Visual Intelligence, 3(1):1-18, 2025.
[77] Daya Guo, Dejian Yang. Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-R1: Incentivizing reasoning capability in LLMS via reinforcement learning. arXiv preprint arXiv:2501. 12948, 2025.
[78] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song. Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2.5-VL. technical report. arXiv preprint wrXiv:2502.13922 2025.
[79] Xinjin Li, Yu Ma, Kaisen Ye, Jinghan Cao, Minghao Zhou, and Yeyang Zhou. Hy-facial: Hybrid feature extraction by dimensionality reduction methods for enhanced facial expression classification, 2025.
[80] Chaojun Ni, Xiaofeng Wang, Zheng Zhu, Weijie Wang, Haoyun Li, Guosheng Zhao, Jie Li, Wenkang Qin, Guan Huang, and Wenjun Mei. Wonderturbo: Generating interactive 3d world in 0.72 seconds. arXiv preprint arXiv:2504. 20000.00..2,,,, 2025.
[81] Yi Xin, Le Zhuo, Qi Qin, Sigi Luo, Yuewen Cao, Bin Fu, Yangfan He, Hongsheng Li, Guangtao Zhai, Xiaohong Liu, al. Resurrect mask autoregressive modeling for efficient and scalable image generation. arXiv preprint arXiv:2507. /3032, 2025.
[82] Jiuming Liu, Zheng Huang, Mengmeng Liu, Tianchen Deng, Francesco Nex, Hao Cheng, and Hesheng Wang. Topolidm: Topology-awars lidar diffusion models for interpretable and realistic lidar point cloud generation. arXiv preprint arXiv:2507.22454, 2025.
[83] Wangbo Zhao, Yizeng Han, Jiasheng Tang, Kai Wang, Yibing Song, Gao Huang, Fan Wang, and Yang You. Dynamic diffusion transformer. ICLR, 2024.
[84] Shuang Zeng, Xinyuan Chang. Mengwei Xie, Xinran Liu, Yifan Bai, Zheng Pan, Mu Xu, and Xing Wei. Futuresightdrive: Thinking visually with spatio-temporal cot for autonomous driving. arXiv preprint arXiv: 2025.
15

---

[85] Rongchao Zhang, Yu Huang, Yiwei Lou, Yi Xin, Haixu Chen, Yongzhi Cao, and Hanpin Wang. Exploit your latents: Coarse-grained protein backmapping with latent diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 1111-1119, 2025.
[86] Xiaoling Zhou, Mingjie Zhang. Zhemg Lee, Wei Ye, and Shikun Zhang. Hademif: Halluci- nation detection and mitigation in large language models. In The Thirteenth International Conference on Learning Representations, 2025.
[87] Zhen Xu, Kunyuan Ma, Yunbo Liu, Wenying Sun, and Youzhu Liu. Causal representation learning for robust anomaly detection complex environments. 2025.
[88] Sibei Liu, Yuanzhe Zhang, Xiang Li, Yunbo Liu, Chengwei Feng, and Hao Yang. Gated multimodal graph learning for personalized recommendation. INNO-PRESS: Journal of Emerging Applied AI, 2025.
[89] Yuyuan Li, Yizhao Zhang, Weiming Liu, Xiaohua Feng, Zhongxuan Han, Chaochao Chen, and Chenggang Yan. Multi-bjecttive unlearning in recommender systems via preference guided pareto exploration. IEEE Transactions on Services Computing, 2025.
[90] Yiming Zeng, Wanhao Yu, Zexin Li, Tao Ren, Yu Ma, Jinghan Cao, Xiyan Chen, and Tingting Yu. Bridging the editing gap in Ilms: Fineedit for precise and targeted text modifications, 2025.
[91] Xiaoling Zhou, Wei Ye, Zhemg Lee, Lei Zou, and Shikun Zhang. Valuing training data via causal inference for in-context learning. IEEE Transactions on Knowledge and Data Engineering, 2025.
[92] Wangbo Zhao, Jiasheng Tang, Yizeng Han, Yibing Song, Kai Wang, Gao Huang, Fan Wang, and Yang You. Dynamic tuning towards parameter and inference efficiency for adaptation. 2024.
NeurlPS, 37:1 177111476-11444
[93] Shuang Zeng, Dekang Qi, Xinyuan Chang, Feng Xiong, Shichao Xie, Xiaolong Wu, Shiyi Liang, Mu Xu, and Xing Wei. Janusvin: Decoupling semantics and spatiality with dual implicit memory for vision-language navigation. arXiv preprint arXiv:2509.222 22544, 2025.
[94] Xinjin Li, Yu Ma, Yangchen Huang, Xingqi Wang, Yuzhen Lin, and Chenxi Zhang. Synergized data efficiency and compression (sec) optimization for large language models. In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS), pages 586-591, 2024.
[95] Zhenjun Zhao. Balf: Simple and efficient blur aware local feature detector. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3362-3372, 2024.
[96] Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li. Yibing Song, Kai Wang, Zhangyang Wang, and Yang You. A stitch in time saves nine: Small vlm is a precise guidance for accelerating large vims. In CVPR, pages 19814,19824, 2025.
[97] Yujia Wang, Lung-Lue Zhang, and Neil Dodgson. Target scanpath-guide 360-degree image enhancement. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pages 1179, 2025.
[98] Chaocan Xue, Bineng Zhong, Qihua Liang, Haiying Xia, and Shuxiang Song. Unifying motion and appearance cues for visual tracking via shared queries. IEEE Transactions on Circuits and Systems for Video Technology, 2024.
[99] Yi Xin, Juncheng Yan, Qi Qin, Zhen Li, Dongyang Liu, Shicheng Li, Victor Shea-Jay Huang. Yupeng Zhou, Renrui Zhang, Le Zhuo, et al. Lumina-mgpt 2.0: Stand-alone autoregressive image modeling. arXiv preprint arXiv:2207.
2025.
[100] Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu, Juncheng Yan, Yan Tai, Jiayi Lei, Yuewen Cao, Keqi Wang, Yibin Wang, et al. Lumina-dimoo: An omni diffusion large language model for multi-modal generation and understanding. arXiv preprint arXiv:2510.06308 2025.
[101] Jiuming Liu, Guangming Wang, Weicai Ye, Chaokang Jiang, Jinru Han, Zhe Liu, Guofeng Zhang. Dalong Du, and Hesheng Wang. Difflow3d: Toward robust uncertainty-awar scene flow estimation with iterative diffusion-based refinement. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 15109-151199 2024.
16

---

[102] Rongchao Zhang, Yu Huang, Yiwei Lou, Weiping Ding, Yongzhi Cao, and Hanpin Wang. Synergistic invention-guider cascaded graph diffusion model for complementarity determining region synthesis. IEEE Transactions Neural Networks and Learning Systems, 2024.
[103] Chaojun Ni, Jie Li, Haoyun Li, Hengyu Liu, Xiaofeng Wang, Zheng Zhu, Guosheng Zhao, Boyuan Wang, Chenxin Li, Guan Huang, al. Wonderfree: Enhancing novel view quality and cross-view consistency for 3d scene exploration. arXiv preprint arXiv:2506.20590 2025.
[104] Chaocan Xue, Bineng Zhong, Qihua Liang, Yaozong Zheng, Ning Li, Yuanliang Xue, and Shuxiang Song. Similarity- -guided layer-adaptive vision transformer for uav tracking. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 6730-6740, 2025.
[105] Zhangquan Chen, Xufang Luo, and Dongsheng Li. VisrI: Intention-driven visual perception via reinforced reasoning. arXiv preprint arXiv:2503.07523 2025.
[106] Nasim Raufi and Leonardo Longo. Evaluating eeg alpha-to-theta and theta-to-alpha band ratios as indexes of mental workload. arXiv preprint arXiv:2202. 12937, 2022.
[107] Malek Adjouadi, Mercedes Cabrerizo, Ilker Yaylali, and Prasana Jayakar. Interpreting eeg functional brain activity. IEEE Potentials, 23(1):8-155 13, 2004.
[108) Isabelle Constant and Nada Sabourdin. The eeg signal: window on the cortical brain activity.
Pediatric Anesthesia, 22(6):539-552 2012.
[109] Claudio Babiloni, Raffaele Ferri, Davide V Moretti, Andrea Strambi, Giuliano Binetti, Gloria Dal Forno, Florinda Ferreri, Bartolo Lanuzza, Claudio Bonato, Flavio Nobili, et al. Abnormal fronto-parietal coupling of brain rhythms mild alzheimer's 's disease: multicentric eeg study. European Journal of Neuroscience,
(000))233229900 2004.
[110] Jane E Adcock and Chrysostomos P Panayiotopoulos. Occipital lobe seizures and epilepsies. Journal of clinical neurophysiology, 2965)3377-477, 2012.
[111] Chenlin Meng, Yutong He, Yang Song, Jiaming Song. Jiajun Wu, Jun- Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations.
[112] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances neural information processing systems, 33:6840-6851, 2020.
[113] Inbar Hluberman-Spiegellee
Vladimir Kulikov, and Tomer Michaeli. An edit friendly ddpm noise space: Inversion and manipulations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12469-12478, 2024.
[114] Manuel Brack, Felix Friedrich, Katharia Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, and Apolinário Passos. Ledits++: Limitless image editing using text-to- image models. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 8861-8870, 2024.
17

---

A Supplementary Key Information
A.1 Preliminary: Analysis of Brain Region Function and Mechanism
We employ a noninvasive multimodal sensing system that synchronously records neural, hemody- namic, and peripheral vascular signals, including EEG, fNIRS, and PPG (Fig. 8(a)). This setup enables comprehensive monitoring of brain activity during human-computer interaction. fNIRS employs near-infrared light in the 690-930 nm range to penetrate scalp and skull tissues. Neural activation leads to increased local cerebral blood flow and metabolic demand, resulting in a rise in oxygenated hemoglobin (HbO) and reduction in deoxygenated hemoglobin (HbR). These changes cause detectable variations in light absorption at the detector. fNIRS thus provides second-level sensitivity the slow hemodynamic responses associated with cortical processing. EEG recorded via hydrogel-bast based electrodes placed over the scalp, capturing millisecond-scal voltage fluctuations resulting from synchronized postsynaptic potentials cortical pyramidal neurons. This modality of- fers high temporal precision for observing rapid fluctuations in cortical excitability and sensorimotor responses. PPG detects volumetric changes in blood flow via near-infrared light, enabling continuous measurement of pulse rate and vascular compliance. The sensor is co-located with fNIRS optodes, sharing similar optical pathways but tuned for peripheral cardiovascular features.
This multimodal approach provides a synergistic view of neural function, combining the high temporal resolution of EEG with the metabolic and vascular insights from fNIRS and PPG. Such cross cross-modal sensing is crucial for modeling the perception-decision-cce cycle in neuroadaptive systems.
(a)
EEG
(b)
Hydragel electrode
EEG Spatial attention
Photodetector
EEG
Altention control Emotion regulation Motivational drive
visuomotor integration visuarintornttegraaion
Absorpiion
Nerve
Fz & Fp2
Oz
Rettection
Scatlering
EEG
HBR
aMPFC
Visual perception Image processing
Less Neural activity
More Neural acsivity
INIRS: High-level cognition
PPG: Heart information
myythm
Figure 8: (a) Signal acquisition mechanism. (b) Cognitive function of different brain regions.
Table 3: Functional Roles of Multimodal Neural Signals in Hands-Free Image Editing.
Signal (Channel)
Cortical Region
Primary Function
Roles in Image Editing
EEG Ch (Pz)
Parietal cortex
Spatial attention, visuo- motor integration
Focuses specific image areas, tar-
gets object localization [64]
EEG Ch (Fp2)
Prefrontal cortex
Emotion regulation, tivational drive
Generates and regulates intentional editing actions [65]
EEG Ch (Fpz)
Prontopolar cortex
Attention control, task initiation
Triggers editing intention, starts/stops
editing operations [66, 67]
EEG (Oz)
Occipital cortex
Visual perception, image processing
Perceives visual changes, evaluates whether edits meet expectations [68]
INIRS (aMPFC)
Anterior medial prefrontal cortex
High-level motivation, emotional valence
cognition,
Indicates editing intent intensity, emo- tional confidence, and mental work- load [69]
PPG (aMPFC)
Cardiovascular or autonomic system
Heart rate arousal, stress
variability,
Monitors cognitive stress or emotional arousal during editing [31, 32]
18

---

To enable hands-free image editing, we integrate multimodal neural and physiological signals to decode user intent and cognitive state in real time. The functional roles of multimodal neural signals are provided in Table.3. Specifically, midline parietal EEG (Pz) reflects spatial attention and visuomotor integration, supporting the allocation of attention to target areas and coordination of motor plans during editing tasks [64]. Right prefrontal EEG (Fp2) is linked to emotional regulation and motivational drive, aligning with findings from frontal alpha asymmetry studies [65]. Frontopolar EEG (Fz/Fpz) tracks attention control and task initiation, facilitating the onset and modulation of editing operations [66, 67]. Occipital EEG (Oz) encodes visual perception and image processing load, enabling evaluation of visual changes and edit quality [68]. fNIRS over anterior medial prefrontal cortex (aMPFC) measures cognitive load, motivation, and emotional valence through hemodynamic activity [69]. PPG signals from the same region capture heart rate variability and autonomic arousal, which reflect user stress and engagement [31, 32]. This multimodal mapping (Fig. 8(b)) enables the system continuously adapt users' cognitive focus, affective state, and mental workload, thereby supporting a more responsive and intuitive editing experience.
A.2 Preliminary Analysis: Editing Type Classification Experiment
LoongX is proposed to address three key research questions:
1. Can neural signals serve as reliable conditions for image editing? (Does it really work?)
2. What kind of information is conveyed by multimodal neural signals? (What do they actually contribute to image editing?)
3. How do neural-based and language- conditions differ in image editing? Can we combine their strengths to enable hands-free editing more effectively?
In response to these problems, we conduct a premise exploration based on classification experiment and design the LoongX model based on the findings. Finally, we present modular architecture comprising unified multimodal encoding, dynamic multimodal data fusion, and diffusion- based conditional generation. Based on these, LoongX can perform robust hands-free image editing by translating user neural states into structured conditions for a diffusion model.
To examine whether neural signals can reliably encode semantic conditions for image editing, we perform an exploratory classification experiment where the task is to predict editing types from neural signals or text. We use the 22,691 training instances and 1,200 test instances in L-Mind. As each editing instance can involve multiple editing types, we implement a simple multilayer perception (MLP) with three mmnlinear-actvatee linear layers and conduct a multi-label classification experiment that recognizes all involved editing type labels for an instance, via text embeddings or neural signals, as condition. We compare models trained with random noise, only text prompts, EEG, INIRS, PPG, combinations of multimodal neural signals, and fusion of both text and signals.
As shown in Fig. 9, the classification results validate the informativeness and complementarity of neural signals for image editing conditions. Fig. 9a) shows that using neural signals, especially the EEG signal itself, can achieve significantly better classification performance than random noise (as seen from over 7% mAP improvement). fNIRS contributes to recall performance gain compared with random noise since it provides more complete and robust information, which is more important for recall. As image editing requires discriminant semantic features, it shows that neural networks can still effectively recognize the editing types (over 60% precision) based on EEG signals and can even achieve comparable performance with text prompts in some cases. Results can also initially respond to question 2. As precision depends on the discriminability of features, FEG contributes more discriminability with less noise. For recall, robustness and completeness contribute more. Therefore, INIRS becomes more stable with less fluctuation. Though PPG and motion do not affect classification performance significantly as their volatility is small, they are expected to provide richer background information to stabilize performance. Now, what will the performance change if we combine these signals?
Fig. 9(b) shows that binding all neural signals (via simple concat and MLP) achieves the best performance, and fusing only EEG and INIRS can achieve comparable performance with only a recall reduction. Moreover, though neural signals are not more discriminant and robust than text embeddings, fusing both can achieve even better performance than text only. That is, the two types of data can complement the missing key information to achieve better category recognition in image
19

---

(4) Different input moda comparison
(b) Different muttimodal
Impact length performance
0.8
0.8
0.8
0.6
0.8
0.4
0.4
02
0.2
Renson Nole
0.
Teat away
EEG Only
naRS Duay
PPG onty
Mutson Outy
Eao
0.0
BEO
EEG HEG-vddtton
PrRed aor etbiion
All Siguats
0.0
2"
2
Iapillangh of EEG
Predeion
Recall
F1 Score
mAP
Figure Multi-label classification result under different settings: (a) different single input modalities;
(b) different modality fusion combinations; (c) different EEG input sequence lengths.
editing tasks, which shows higher performance as more powerful condition for further generative models.
As the neural signals all have different shapes, unifying the input sequence into unified length is necessary. Simple padding and truncating to a fixed length is one of the most reliable methods for preserving most information in raw signals. We also investigate the influence of input sequence length for EEG as a neural signal representative. Results in Fig. 9() show that truncating and padding EEG signal sequences
unified 8,192 can achieve the best performance. While the longer sequence ensures a more reliable performance, the computation cost will become burden as the sequence length increases. A trade-off method is needed to encode the most valid information signals while not bringing unbearable computational costs. Therefore. we design the Cross-Scale State Space (CS3) model ensure the best trade-off between performance and computational cost.
A.3 More Limitations Discussion
While LoongX demonstrates strong performance in neural-driven image editing, several limitations remain. First, the current dataset was collected from relatively homogeneous group of 12 healthy young adults. Although the model performs well within this cohort, generalization to broader populations (e.g., different age groups or individuals with neurological conditions) not yet fully validated. Moreover, the neural signals were acquired using low-density EEG and fNIRS systems, which, despite their practicality and portability, offer limited spatial resolution compared to high- density invasive setups. This constraint may affect the system's 's ability to capture fine-grained neural representations.
The robustness of LoongX under varying data distributions and noisy conditions has not been systematically explored. Although some resilience expected from the multimodal design and the DUAF fusion strategy, comprehensive stress testing against motion artifacts, sensor dropout, or environmental interference is necessary next step for real-world deployment. Furthermore, while the BCI system we use is designed to work across participants, it may benefit from test-time adaptation or few-shot user-specific finetuning to account for individual variability in neural signatures, which can differ significantly across users.
Finally, the interpretability of the learned neural representations remains limited. While the CS3 encoder effectively distills signal patterns for downstream editing, it is not yet clear how these latent features relate to interpretable cognitive states or intentions. Improving the transparency and explanatory power of the system will be essential for broader acceptance and responsible deployment, especially domains involving sensitive user data.
A.4 Broader Societal Impacts
The proposed -onval-driven image editing technique has the potential for significant positive societal impact by improving accessibility for individuals with motor or communication impairments, enabling more inclusive creative workflows, and lowering the barrier to content creation. By enabling hands- free and intuitive interaction with generative models, this technology could foster greater participation in digital art, design, and communication, particularly for users with physical limitations.
20

---

However, as with other generative and brain-computer interface technologies, there are potential negative societal impacts. Malicious or unintended uses may include the creation of manipulated or deceptive media, privacy violations stemming from the misuse of neurophysiological data, or unauthorized surveillance. There are also concerns regarding fairness, as disparities device availability or neural signal quality across user populations could exacerbate existing inequities.
To mitigate these risks, recommend the implementation safeguards such as user authentication, audit trails for sensitive editing actions, and transparent communication regarding data usage and model limitations. Responsible deployment should include ongoing monitoring and mechanisms to address feedback and misuse.
A.5 Safety and Ethics
All participants signed consent forms before the experiment, which was approved by the Institutional Review Board's s Ethics Committee for Human Research Protections at Zhejiang University (Protocol ID: No. 067 (2019)).
Recognizing the potential for misuse of proposed models and multimodal neural datasets, we are committed to responsible release practices. We will require users requesting access to the models or data to undergo an application and review process, ensuring aligmment with ethical guidelines and legitimate research clinical objectives. Usage agreements will prohibit malicious activities, and access may be revoked in the event of violations.
For dataset release, we apply stringent filtering to remove personally identifiable, sensitive, or potentially harmful content. For model access, we will implement usage restrictions, safety filters, and ongoing monitoring to prevent abuse. Additionally, clear documentation outlining acceptable use cases, known limitations, and recommended best practices will be provided all users.
We will continuously evaluate the impact and usage of the released resources and remain open to community feedback to improve the effectiveness of these safeguards over time.
B Supplementary Literature Review
B.1 Brain Computer Interface
BCI is an emerging technology that establishes a direct communication pathway between the brain and external devices by interpreting neural signals. BCIs have broad applications in healthcare, communication, gaming, and assistive technologies [36, 37]. The primary goal is to help individuals, especially those with motor impairments, interact with digital systems without the constraints of traditional input methods.
Non-invasive BCIs, such EEG and INIRS, are widely studied for their safety and real-time signal acquisition capabilities [70]. EEG captures the brain's 's electrical activity and is particularly effective in detecting cognitive states such as attention and intention. While fMRI provides high-resolution whole-brain imaging, its use is constrained by high cost and limited mobility. In contrast, INIRS measures hemodynamic responses and offers portable, cost-effective solution for monitoring brain activity in real-world environments (as illustrated in Fig. 2 and Fig. 8).
With the rapid development of artificial intelligence, especially deep learning, BCI systems have seen significant improvements in decoding accuracy and robustness [71, 72]. Advanced models enable tasks such mental spelling, prosthetic control, and gaming interfaces. Recent trends include integrating BCIs with generative models such as diffusion networks to synthesize visual content directly from brain activity, demonstrated in work applying stable diffusion to fMRT decoding [73]. The integration of EEG and INIRS holds great promise for improving the efficiency of human-AI interaction without compromising the portability that makes non-in non-invasive systems suitable for real-world applications.
B.2 Brain-supervised Generation
Advances in machine learning have significantly enhanced BCI decoding accuracy and robustness, opening new possibilities for brain-supervised generative methods that transform neural signals into visual content [74, 75].
21

---

Several recent methods integrate neurophysiological data (c.g., fMRI. EEG, or INIRS) with gener- ative models [22, 38. 39]. For instance, CMVDM aligns fMRI features with semanties for image synthesis [40], and the MindEye series further lifts the resolution of generated images from decoded fMRI [41, 42]. DreamConnect translates brain signals into images based on fMRI, which less accessible for everyday interaction [76]. DreamDiffusion produces images from EEG via temporal masked modeling [22]. Video extends the idea to dynamic video content [43]. While Davis et al. [24] initially explore brain-guided semantic image editing using generative adversarial network, this work is limited to facial images and EEG signals. Moreover, Adamic et al. [44] reconstructs visual images from brain activity measured by fNIRS.
Unlike previous studies, our data were collected via a wireless BCI system, as shown in Fig. 2, from participants performing instruction- based image editing tasks. Compared with IMRI-based methods, this multimodal setup is portable and suited to daily use, which can support greater portability and broader weal-world applicability. To the best of our knowledge, our work is the first to comprehensively utilize the full information of EEG. JNIRS, PPG, and head-motion signals for image editing. We specifically delve deeper into strategies of extracting neural features and fusing multimodal data, optimizing their integration to serve image editing needs.
B.3 Instruction-based Image Editing
Instruction-based based editing tasks include adjusting color, contrast, and brightness; retouching objects or backgrounds; and applying filters or artistic effects. This progress ranges from global editing (c.g.. style transfer [1] to region-based image editing [2], which serves wide range of applications in advertising, entertainment, and social media [2].
The surge in multimodal data has fostered the development of large models capable of complex creative tasks [77, 78]. Recent models such as GPT-4o [45] and Gemini [46] have evolved from basic data analysis advanced image editing agents. These agents leverage generative methods to interpret user instructions for precise image manipulation.
Current instruction-based image editing agents integrate multimodal inputs, including text, images, and videos, to accurately identify and apply visual edits [13]. Leveraging learned multimodal representations, these agents interpret instructions from input, localize relevant regions, and perform targeted modifications [47-49]. Recent approaches, such as InstructPix2Pix [1], UltraEdit [2], MagicBrush [50], MIGE [51], and ACE [52] improve region-specific edits guided by natural language prompts. is noticed that Jiang et al. [14] explore speech-driven image editing, highlighting the feasibility of hands-free interaction but still limited by linguistic expressiveness in recorded speech.
Despite these advancements, achieving efficient, delicate, prompt-free image editing remains challeng- ing. Our work addresses this gap, exploring neural-signal-driver editing agents to decode cognitive intent directly for image manipulation, sigmificantly enhancing accessibility and interaction efficiency.
B.4 Future Research Prospects
Recent studies have expanded the frontiers of multimodal representation learning and diffusion-based
generation, which benifits the future improvement of this work [79-85]. For example, Zhou et al. [86], Xu al. [87] and Liu et al. [88] extended causal and gated frameworks toward robust multimodal
understanding. Li et al. [89] further introduced multi-objective unlearning, and Zeng et al. [90] advanced precise text editing for controllable LLM adaptation. These developments collectively reflect a broader shift toward interpretable, data-efficient, and causally grounded multimodal methods, which contribute to future neural-driven AI systems [91-94].
Complementary progress has also been made in visual information processing, multimodal per- ception and generative modeling [86, 89, 95-98J. Xin et al. [99, 100] who introduced scalable autoregressive and omni-diffusion architectures that can be further used in advanced image editing. Liu et al. [101] enhanced scene flow estimation via iterative diffusion, while Zhang et al. [102] demonstrated the potential of diffusion processes in molecular and structural synthesis. Other repre- sentative efforts [103, 104] continue to enrich multimodal benchmarks and architectures. is noted that intention-drinn -drivn visual reasoning [105] further reveal the emerging synergy between structured reasoning and perceptual modeling, inspiring the future direction of neural-drived methods toward reasoning models.
22

---

Collectively, these works point bright future research path, which leverages diffusion- driven priors, causal reasoning, and behaviorally grounded learning for more interpretable and controllable multimodal intelligence based on advanced neural-driven approach and updated BCI devices.
C
Supplementary Dataset Details
C.1 Background Details
Overall Mechanism. As illustrated in Fig. 8, EEG captures electrical activity along the scalp with high temporal resolution, enabling fine-grained monitoring of neural dynamics. Neural signals from Fpz, Fp2, Pz, and Oz electrodes serve complementary roles in facilitating hands-free image editing. Fpz and Fp2, located in the frontal cortex, are primarily responsible for attentional control and intentional decision-making, respectively. Notably, we deliberately preserve blink-related signals at Fpz and Fp2. allowing the system to retain ecologically valid user states and incorporate implicit ocular cues without requiring additional eye-tracking hardware. Pz supports spatial attention and target selection, while Oz reflects visual perception and validation of image modifications. Importantl, we avoid relying on conventional motor imagery regions (e.g., C3/C4), which are associated with imagined limb movements and commonly used in traditional BCI paradigms. These approaches often require extensive training, suffer from high inter-subject variability, and may be inaccessible to users with motor impairments. Instead, LoongX prioritizes frontal and parietal EEG channels that reflect universal cognitive processes such attention, intention, and visual evaluation. This design enables plug-and-play usability without the need for motor calibration, ensuring a more intuitive, low-burden, and inclusive experience across diverse user populations. To further enhance the decoding of user states, we incorporate INIRS signals from the left and right anterior medial prefrontal cortex (aMPFC), which provide critical hemodynamic insights into cognitive load, emotional valence, and motivation intensity. In addition, peripheral PPG signals are used to capture autonomic responses such as heart rate variability and arousal levels, enabling the system to monitor stress and engagement during editing tasks. It offers complementary physiological information, such as heart rate, blood oxygen saturation (SpO2), and peripheral blood flow variations. Given the susceptibility of these biosignals to motion motion-induced artifacts, particularly those arising from head movements, a triaxial accelerometer and triaxial gyroscope are employed capture translational and rotational motion of the head. This motion data is subsequently used for signal quality assessment and artifact mitigation, thereby enhancing the reliability of the acquired physiological measurements. Overall, this multimodal integration allows LoongX to decode user intent, attention, and emotional context in a holistic and adaptive manner, resulting more precise, reliable, and user-aware editing commands.
C.2 Subject Information
12 healthy college students (6 females and 6 males) were recruited as the subjects for data collection. They have mean age of 24.5++2..5 years and normal (or serretedd-tonormaal vision. All volunteers were informed of the experimental process and received financial compensation. All volunteers signed the consent forms prior to the experiment, which approved by the Ethics Committee of the ZJU Review Board for Human Research Protections (Protocol ID: No. 067 (2019)). The attention score in this study is objectively computed using the ratio between the power of the EEG alpha band (8-12 Hz) and the theta band (4-8 Hz), as shown below:
Alpha Band Power
Attention Score
(13)
Theta Band Power
This ratio has been widely used in cognitive neuroscience research as a neurophysiological index of attentional control and mental workload. For instance, Raufi and Longo [106] demonstrated that the Aipha-to-theta and theta-to-alpha band ratios are reliable indicators of self-reported mental workload levels in EEG-based studies. The attention scores and the neural-signal-bian based image editing errors of 15 subjects in our experiment are summarized in Table 4. Subjects 13-17 are regarded as unseen subjects, and the L-Mind training set does not include these unseen data for cross-subject evaluation.
C.3 Data Collection Details
Using the setup shown in Fig. 2, total of 23,928 pairs of effective data were collected from 12 participants. The specific details are given as follows.
23

---

Table 4: Attention and neural-signal--aase image editing scores of individual subject
Subject
Gender
Age
#Samples
Attention
L1
L2
CLIP-1
DINO
CLIP-T
1
Female 25
2003
0.0887
0.2657
0.1109
0.6370
0.4890
0.2196
2
Female 29
2000
0.0817
0.2416
0.0950
0.6575
0.5021
0.2249
Female
26
2001
0.1340
0.2448
0.0963
0.6660
0.4878
0.2337
4
Female
22
1999
0.0739
0.2533
0.1005
0.6394
0.4606
0,2270
5
Female
28
1992
0,1218
0.2552
0.1031
0.6144
0.4157
0.2260
6
Female
29
1964
0.0822
0.2511
0.1000
0.6449
0.4564
0.2213
7
Male
22
1988
0,0851
0.2711
0.1160
0.6515
0,4634
0.2234
8
Male
23
1993
0.1105
0.2528
0.1017
0.6638
0.4833
0.2242
9
Male
22
1988
0,1500
0.2497
0,0998
0,6355
0,4571
0.2212
10
Male
24
2000
0.1298
0.2657
0.1144
0.6194
0.4240
0.2220
Male
24
2000
0.0954
0.2744
0.1151
0.6386
0.4339
0,2299
12
Male
22
2000
0.0971
0,2551
0.1034
0.6213
0.4323
0.2250
13 (unseen)
Male
35
500
0.1210
0.2681
0.1174
0.6022
0.4418
0.2594
14 (unscen)
Female
30
500
0.0775
0.26688
0.1179
0.6051
0.4405
0,2553
(unseen)
Male
13
200
0.0727
0.2618
0.1001
0.6055
0.4472
0,2576
16 (unseen)
Female
62
100
0.0441
0.2660
0.1141
0.6196
0.4611
0,2472
17 (unseen)
Male
63
100
0.0520
0.2610
0.1133
0.6017
0.4588
0.2595
Multimodal Device and Signal Collection Pipeline. LoongX employs non-invasive BCI technolo- gies to acquire multimodal neurophysiological signals, integrating data from four EEG channels (Fpz, Fp2. Pz, Oz) sampled at 250 Hz, eight fNIRS channels located in the medial prefrontal cortex (MPFZ) zone sampled at 25 Hz, and eight PPG channels also within the MPFZ zone sampled at 25 Hz. Additionally, six channels of head motion data- comppising triaxial linear acceleration and triaxial angular velocity-are recorded 12.5 Hz to capture head movement dynamics. BEG, INIRS, PPG, and motion signals are synchronized and transmitted from the device to the PC-side software application via Bluetooth 5.3. The application streams these signals in real time the Lab Recorder software through the Lab Streaming Layer (LSL). Mcanwhile, event markers generated by the image-stimulus paradigm on the PC are also sent via LSL Lab Recorder. These markers indicate the start and stop times of user-trigge user-triggered recordings. As result, we obtain well-aligned EEG, INIRS, PPG, motion, and audio signals. There was I-second cross-fixation interval between each pair of pre- and post-edited images (Fig. 2). Data collection was organized in batches of 100 images, after which participants were given rest period. A total of 2,000 images were collected from each participant. A total of 23,928 pairs of effective data were collected, comprising participants' speech, EEG, INIRS, PPG, and head motion information.
Our developed computer software used in this non-invasive BCI device streams the data in real time to the Lab Recorder software through the Lab Streaming Layer (LSL) framework. Meanwhile, event markers generated by the image-stimulus paradigm on the PC are also sent via LSL to Lab Recorder. These markers indicate the start and stop times of user-triggered recordings. Neural signals are collected simultaneously while the participant uses speech to describe the content of the image editing. As a result, we obtain well-aligned EEG, fNIRS, PPG, motion, and audio signals. There was a I-second cross-fixation interval between each pair of pre- and post-edited images to distinguish between different image- image-editing pairs clearly. 2,000 images are collected from each participant. In all, 23,928 valid data pairs are gathered, encompassing participants speech, EEG, fNIRS, PPG, and head motion information.
Participants. 12 healthy college students (6 female and 6 males) were recruited as the subjects for data collection. They have mean age of 24.5 years and normal (or corrected-to-nomee to-normal) vision. All volunteers were informed of the experimental process and received financial compensation (at an hourly rate exceeding the minimum wage in the region). All volunteers signed the consent forms prior to the experiment, which was approved by the Ethics Committee of the Institutional Review Board Zhejiang University for Human Research Protections (Protocol ID: No. 067 (2019)). To ensure data consistency during extended EEG acquisition, the experiment was conducted in quiet room (3m x 5m) maintained at constant temperature of C and constant humidity. EEG signals were recorded using the latest non-invasive hydrogel electrodes, which are known to provide one of the highest signal quality among available electrode types. To maximize signal integrity and minimize impedance- related artifacts, the electrodes were replaced every five hours sooner if necessary. Data
24

---

acquisition for each participant starts at AM daily, and the room was shielded from direct sunlight reduce its impact on light-sensitive signals such as fNIRS and PPG. Example stimulus sessions that were displayed to participants are shown in Fig. 2.
C.4 Data Preprocessing
To make use of the most relevant information and reduce noise and artifacts, each type of multimodal neurophysiological signal was preprocessed based on unique characteristics. The first step for our proposed neural-based image editing method is extracting and encoding data into a structured format based on collected metadata. Techniques such as interpolation for missing data and normalization methods to standardize signal amplitudes are applied to clean and normalize data remove noise, fill missing values, or correct errors. Advanced filtering skills based on machine learning are implemented to select data aligned with the model's predefined objectives, such identifying visual patterns correlated with specific cognitive states or processing visual-related neural signals. Specifically, the data processing steps include:
EEG Preprocessing. Signals were band-pass filtered (1-80 Hz) and notch-filtered (48-52 Hz) to remove noise and powerline artifacts. The EEG channels near the eyes (Fpz and Fp2) retained ocular signals for intentional blink detection. Specific procedures are follows:
A band-pass filter was applied in the range of 1-80 Hz remove low-frequency drifts and high-frequency noise.
A notch filter was applied in the frequency range of 48-52 Hz eliminate powerline interference, which is specific to the 50 Hz electrical grid China.
Ocular artifacts from the AF8 and FPz channels were preserved intentionally, as these channels were specifically designed to capture eye movements, including blinking.
INISS Preprocessing. The eight-channel fNIRS signals processed extract concentration changes of oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HHT). The following preprocessing steps were performed:
The HbO, HbR, and HbT signals were band-pas filtered in the range of 0.01-0.5 Hz to isolate brain spontaneous and stimulus-induced hemodynamic responses associated with neural activity. These signals correspond to brain activity evoked by the image-editing paradigm.
The processed НЫО, HbR, and HbT signals were averaged to obtain two signals per hemi- sphere: left hemisphere HbO, right hemisphere HbO, left hemisphere HbR, right hemisphere HbR, left hemisphere HbT, and right hemisphere HbT.
Moreover, light intensities at 735 and 850 nm were converted to HbO/HbR/HIт using the Modified Lambert Law, then filtered (0.01-0.5 Hz) to isolate hemodynamic responses.
fNIRS signals are calculated by raw 735 nm and 850 nm wear-infrared light intensity. The raw fNIRS signals are measured as light intensity changes at different wavelengths. To convert these optical signals into concentrations of oxygenated hemoglobin (HbO) and deoxy- genated hemoglobin (HbR), we apply the Modified -embert Law (MBLL). The logarithmic optical density change AA is calculated as:
Hemodynamic Conversion:
Io(A) T(A)
AA(A) log
(14)
where (A) is the initial light intensity, I(A) is the measured intensity at wavelength A. and AA(A)
the optical density change.
Then, the concentration changes of HbO and HbR are derived using the MBLL as follows:
AHbO AHbR
A
AA((A)) AA(12)
i
Lib As HHR
(15)
DPF
Ag Hho
25

---

Here, E denotes the molar extinction coefficients of HbO and HbR at wavelengths A and X2 L is the source-detector separation, and DPF is the differential pathlength factor that accounts for scattering in tissue. Additionally, the total hemoglobin concentration change AHbT is calculated as the sum of the changes HbQ and HbR:
AHIT = AHbO + AHbR
(16)
This process yields time-series concentration changes AHbO, AHbR, and AHbT. These eight channels of ÁHbO, AHbR, and AHBT were band-pass filtered (0.01-0.5 Hz) to isolate brain hemodynamic responses related to neural activity evoked by the image-editing paradigm. Finally, the HbO, HbR, and HbT signals were then averaged to obtain left and right hemisphere values for each.
PPG Preprocessing. Optical signals were band-pass filtered (0.5-4 Hz) to extract cardiac rhythms for heart rate variability and arousal estimation. The raw light intensity data for the four-channel PPG signals were acquired at wavelengths of 735 nm and 850 nm. The detailed preprocessing steps for these signals were as follows:
The raw optical intensity data at both 735 nm and 850 nm averaged to obtain the following four signals: left hemisphere 735 nm, right hemisphere 735 nm, left hemisphere 850 nm, and right hemisphere 850 nm.
A band-pas, pass filter with frequency range of 0.5-4 Hz was applied to these four signals to extract hemodynamic changes associated with cardiac rhythms, reflecting the blood flow pulsations due to heartbeats.
Motion Tracking. The raw signals from the six-axis motion sensors (accelerometer gyroscope) were retained to ensure accurate monitoring of head movements during the experiment, as these movements could potentially affect signal quality. These signals were also used to assess signal quality and reduce movement-related artifacts.
Session Segmentation: Each editing session was defined by a speech-triggered onset and offset, linked to pre- and post-edited image pairs. Only active segments were retained for training and testing. Specifically, the image stimulus is used mark the beginning of session when the user starts recording audio. The session ends when the user stops the audio recording. During this time, the system synchronizes the recording of the participant's speech, EEG, INIRS, PPG, and head motion data. Data recorded during periods of silence, when the audio recording is not active, is excluded from training and analysis.
D Supplementary Method Details
D.1 Background Information
To enable hands-free image editing, the integration of multimodal signals facilitates the interpretation of the user's intent and mental state: EEG activity at frontal sites such as Fpz reflects attention control and task initiation [107]: signals from the right prefrontal cortex (Fp2) are associated with emotional regulation and motivational drive, evidenced by frontal alpha asymmetry [108]: EEG
at the midline parietal site (Pz) captures spatial attention and visuomotor integration [109]; and occipital EEG (Oz) provides information on visual perception and image processing load [110]. Additionally, fNIRS measurements over the left and right anterior medial prefrontal cortex (aMPFC) reveal cognitive load and emotional valence through blood oxygenation patterns [30]. and peripheral cootoolethysmograppe
(PPG) signals monitor heart rate variability as a proxy for autonomic arousal, enabling the system to track user stress levels and engagement during interaction [31, 32]. Integrating these modalities allows the system to adapt to the user's focus, emotional state, and workload, making hands-free image editing more intuitive and responsive.
Midline parietal EEG (fpz) is involved in spatial attention allocation and visuomotor integration, contributing to the coordination of visual and motor aspects of the editing task [107]. Occipital EEG Oz captures visual perception dynamics and image processing load, providing insight into how visual information is being processed by the user's brain [110].
26

---

D.2 Theoretical Derivation of Flow-Aware Inversion
We defined inversion as a trajectory that transports clean sample 2g ~ Po((r) to a noisy latent It (z). Within the DDPM framework, the forward process is described as:
Tt Vā To + v1 dee E, Oe II ai 04 B,, -~ N((,
First, we formulate pure stochastic SDE that follows the forward diffusion to gradually add noise, and then run the time-reversed SDE retrieve an editable reconstruction, similar the philosophy of SDEdit [111].
Second, probability fllw ODE treats diffusion via the acore-based velocity field u(x,). replacing the random noise with a deterministic velocity field o(z,) proportional to the score V., log p. (x+):
30 It 1.5--J //'0000 u(x,,) dr,
Jt. = xo [ u(xx,) x0 (.,0-()(..))) u(z.))
continuum between these two extremes is obtained by interpolating the stochastic and deterministic contributions with parameter [0, 1]:
24 = VV, + v1 - ā [nE + (1 - 7) wr], U4 = /'' Cr v(T,, T) dr,
v1
where 8 ~ (0, and e, a schedule-dependent factor that aligns the units of the velocity term with standard DDPM dynamics [112]. Choosing y7 0 recovers the deterministic ODE path, whereas
7 = yields the fully stochastic SDE path, and intermediate values trade deterministic guidance for stochasticity.
Our flow-aware inversion belongs to the deterministic end. As Flux.1-Daa predicts rectified-flow velocity rather than DDPM score, we insert lightweight rank- 128 LoRA adapter W that maps the frozen backbone's predicted velocity (x,,, r) into the DDPM score domain through:
v(x;) o, Wi (Eg(#r,7))
The time-dependent coefficient a, helps bridge the rectified-flow velocity and the DDPM score scale, while the linear bridge preserves the benefits of flow pre-training and enables faithful one-to-one reconstructions, in a similar spirit to edit-friendly DDPM [113] or LEDITS++ [114].
D.3
More Method Explanations
To validate neural signals as reliable semantic conditions, conduct preliminary multi-label classification experiment detailed in Fig. 9. Results show that EEG signals yield over 7% mean Average Precision (mAP) gain compared to random noise, and fNIRS improved recall through robustness. Combining EEG and fNIRS shows stronger performance, and integration with textual prompts further enhances outcomes than using text only, confirming modality complementarity.
To handle diverse input shapes raw signals, we pad/truncate inputs and find that EEG sequences of length 8, 192 offer good trade-off between performance and efficiency, which is summarized in Fig. (c). As longer input sequences bring unbearable computational costs, we seek an optimal solution that keeps key information in the long sequence data while not significantly increasing computational costs. This motivates our CS3 encoder, which captures both temporal and channel- wise patterns efficiently while achieving better trade-off between information density and computing efficiency.
To effectively extract structured representations from diverse signals, we propose the CS3 encoder. CS3 utilizes the linear computational complexity and efficiency of the structured state space model (S3M) [55] for encoding long sequences into channel representations. Recognizing that increasing latent dimensions can still significantly raise computational costs, CS3 implements a cross-featur extraction mechanism that separately encodes temporal and channel information.
27

---

While the fixed latent dimension of the S3M can not fully capture the dynamic information in signals, we further apply an adaptive feature pyramid based on adaptive average pooling. Each signal is processed through modality-specifie neural encoders to produce latent features. Taking C-channel EEG sequence X R as an example, we first normalize to [-1, 1] and pass through two parallel S3M blocks capturing complementary dynamics.
To align and integrate multi-source signals, we further introduce DGF, which dynamically processes and fuses features across modalities. DGF can model inter-modality interaction modelling to form a unified latent space, which is also optionally usable for alignment with text embeddings support more hybrid conditioning.
In summary, CS3 captures multi-scale temporal and structural patterns in neural signals, consistent with findings that multi-band EEG features improve intent decoding. DGF performs selective multimodal fusion through dual gating, which follows prior successes of gating and normalization strategies in multimodal learning. On pairing EEG plus PPG with T5, and fNIRS plus Motion with CLIP. Rationale also not arbitrary. T5 provides fine-grained token-level semantics that help precise instruction following, which complements the fast neural dynamics in EEG and the lightweight hemodynamics from PPG. CLIP provides robust global semantics that align with slower cortex-wide INIRS signals and intentional head Motion.
E
Supplementary Experimental Details
E,1
Cross-subject Experiment
Our original dataset collected from 12 participants (6 female, 6 male, mean age 24.5 = 2.5 years), each contributing around 2,000 paired samples under carefully controlled experimental conditions. While our initial split ensured training/test separation, acknowledge that the possibilityy of subject overlap could limit generalizability. To address this, we performed additional cross-subject evaluations with 5 new unseen participants (3 male, female, ages 13-63, see Table 4). The results are presented in Table 5. It confirms that the model maintains strong generalization when applied to unseen individuals, with performance trends on CLIP-I, DINO, and CLIP-T remaining consistent with those from the original test set. This provides evidence that our approach is not overly reliant on subject-specifie neural signatures, but instead captures transferable semantic representations.
Table 5: Performance comparison of baseline and proposed LoongX the original test set (12 subjects) and unseen test set (5 new subjects).
Test Dataset
Methods
Conditioning
L1 (L) L2 (1)
CLIP-1 (1)
DINO (f)
CLIP-T (f)
OmniControl
Text
0.2632
0.1161
0.6558
0.4636
0.2549
Original
OmniControl
Speech
0.2714
0.1209
0.6146
0.3717
0,2501
LoongX (Ours)
Neural Signals
0.2509
0. 1029
0.6605
0.4812
0.2436
LoongX (Ours)
Signals + Speech
0.2594
1080
0.6374
0.4205
0,2588
OmniControl
Text only
0.2581
0.1133
0.6528
0.4655
0.2553
Unseen
OmniControl
Specch
0.2779
0.1271
0.6221
0.3942
0.2508
LoongX (Ours)
Neural Signals
0.2574
0.1090
0.6019
0.4037
0,2403
LoongX (Ours)
Signals + Speech
0.2668
0.1146
0.6049
0.4447
0.2568
E.2
Abalation Studies Breakdown
Table 6 and Table 7 correspond to the detailed ablation studies illustrated in Fig. and Fig. in the main manuscript.
From a neuroscience perspective, the superiority of fNIRS and EEG integration aligns well with our understanding of brain physiology: EEG captures rapid electrical oscillations reflecting millisecond- level neuronal activity, while fNIRS provides complementary information about slow hemodynamic responses, reflecting regional brain activation. Their fusion exploits both temporal and spatial dynamics of cognition, which is particularly relevant for decoding the complex neural basis of visual and semantic processing required by image editing tasks. The limited effect of PPG and motion
28

---

signals may stem from its primary focus on peripheral patterns, which are less directly involved in cortical information processing but still can affect the robustness of the performance.
Drilling down into Table 7, the channel-wise analysis offers intriguing support for established functional specialization in the human brain:
The Oz channel (occipital cortex) stands out in global image alignment and robustness metrics, mirroring its neuroanatomical role as the hub for early-stage visual perception. The occipital lobe, and especially the Oz electrode position, is known for processing visual stimuli, edge detection, and scene analysis. The strong performance observed here suggests that even in data-driven, deep learning context, the fundamental dominance of the visual cortex in image-based tasks persists.
In contrast, the Fpz channel (frontopolar cortex) exhibits heightened performance in metrics linked to semantic understanding and higher-order cognitive alignment. The prefrontal regions responsible for executive functions such as attention, planning, and integrating multimodal information, which are essential for aligning generated content with textual or conceptual prompts.
These results not only validate long-standing neuroscientific theories, such as the hierarchical pro- cessing streams in the brain (from occipital "what is seen" to frontal "what does it mean/what to do"), but also provide practical guidelines: in settings where only a limited number of electrodes or sensors are available, prioritizing signals from functionally specialized regions (e.g., Oz for vision, Fpz for semantic or cognitive control) can maximize decoding efficiency for targeted tasks.
Furthermore, the convergence of these findings with classical brain science underscores the trans- lational value of deep learning in cognitive neuroscience. It highlights the potential for future croputcomputer interfaces to be not only data-driven but also "anatomy-awan" leveraging our evolving map of the brain to design more effective and interpretable multimodal AI systems.
Table Evaluation results for different signal combinations.
Metric
Pure EEG
EEG + INIRS EEG + fNIRS PPG All Signals All Signals Text
LI
0,2641
0.2508
0,2631
0,2571
0.2594
L2
0.1078
0.1029
0,1123
0.1076
0.1080
CLIP-I 0.5457
0.6604
0,6536
0.6274
0.6374
DINO 0.2963
0.4811
0,4942
0.4245
0.4205
CLIP-T
0.2251
0.2436
0,2226
0,2481
0,2588
Table 7: Results using different brain region signals. GT' refers ground truth. Ch means the channel.
Condition
L1
L.2
CLIP-I DINO CLIP-T (Ours)
CLIP-T (GT)
EEG (All channels)
0.2508
0.1029
0.6604 0.4811
0.2436
0.2594
EEG (Ch 0, Pz)
0.2509
0.1028
0,6486 0.4787
0.2314
0.2594
EEG (Ch 1, Fp2)
0.2581
0.1070
0.6178 0.4150
0.2421
0.2594
EEG (Ch 2, Fpz)
0.2486
0.1022
0.6669 0.4846
0.2481
0.2594
EEG (Ch 3, Oz)
0.2475
0.1003
0.6619 0.4873
0.2367
0.2594
E.3 More Qualitative Results
More qualitative comparisons are presented in Figures 10-13, corresponding to the four broad editing categories: Global, Background, Object, and Text Editing. For clarity and conciseness the figures, the original lengthy instructions have been distilled into single-sentence descriptions without altering their intended meaning. The editing results of our neural-driven and neural- -ppechh fusion methods consistently outperform text- sext-prompt-basee editing results, demonstrating superior alignment with human intent and greater editing precision. Notably, Text Editing presents a more complex challenge compared to other categories. Given the current limitations of backbone models (with the exception of commercial models like GPT-4o), text-based edits remain difficult. As evidenced by the examples, neural-drive approaches exhibit stronger ability to align with human intent, making the editing
29

---

process more intuitive and effective. is foresecable that in the near future. reliable image-editing backbones become more accessible, -eurelldriven image editing will further stabilize and mature, evolving into an indispensable tool for everyday creative workflows.
Fig. 14 specifically analyzes three characteristic failure modes: (1) cases involving overly imaginative descriptions that deviate significantly from the training data distribution (e.g., "long-legged space creature"), (2) ambiguous instructions with insufficient semantic details (particularly evident in case (b) where background retention specifications were omitted), and (3) challenges posed by non-standard input image dimensions (such as panoramic aspect ratios). These failure cases provide valuable insights into the current limitations of neural-basse editing systems.
E.4 More Failure Cases
Figure. 14 specifically illustrates three failure cases, where overly exaggerated imagination, vague instructions, or uncommon input image sizes may contribute to failed results. It demonstrates three characteristic failure modes: (I) cases involving overly imaginative descriptions that deviate sig- nificantly from the training data distribution (e.g.. "long-legged space creature"), (2) ambiguous instructions with insufficient semantic details (particularly evident in case (b) where background retention specifications were omitted), and (3) challenges posed by non-standard input image dimen- sions (such as panoramic aspect ratios). These failure cases provide valuable insights into the current limitations of based editing systems.
30

---

Original
Ground Truth
Neural (Ours)
Neural Speech (Ours)
Text Only
(a) Horizontally flip the image, eyes the horse to make part of horse
Enhance the image dynamic range colors look vibrant.
(c) Enhance the brightness of the image improve visibility.
(d) Move the game character to the right side the screen and add brown frame to the picture
Crop off white edges, increase brightness the image make the characters stand more
If) Replace original background with black background
Figure 10: Qualitative comparison of our neural-driven and speech-neural fusion methods and text- prompt baseline for Global Editing category.
31

---

Original
Ground Truth
Neural (Ours)
Neural Speech (Ours)
Text Only
(a] Remove the small stick the middle of the bow, resize the object, and set a red background
(b) Replace background with skyline, fish in bird's mouth, make bird window
(c) Replace background with dark red-lit scene, masked black squirrel, glowing chestnuts, magical vibe
(d) Replace the background with an outdoor tent scene, place the cat, painting part, and banknote inside the tent
(e) Fit the cartoon character outdoor group bac background, looks like taking with the crowd
(f) Replace background with Galaxy, put the person and dolphin, Earth outlined, with a winged white a animal above
Figure 11: Qualitative comparison of neural-driven and speech-neural fusion methods and text- prompt baseline for Background Editing category.
32

---

Original
Ground Truth
Neural (Ours)
Neural + Speech (Ours)
Text Only
Reskin middle person, making her
(b) Add few simple ple cartoon characters to the clouds.
Keep antlers and bat, set sea scene with reindeer on jet ski, move above, rope jet ski
(d) Add Dinosaur Bani, making look like it's walking in front of and behind the people
(e) Add astronaut on surfboard to the right, adjust ratio harmonious composition
airplane belching black vignette background, scarf around the fox's
Qualitative comparison of our neural-drived and speech-neural fusion methods and text- prompt baseline for Object Editing category.
Figure 12:
33

---

Original
Ground Truth
Neural (Ours)
Neural Speech (Ours)
Text Only
Shoul
ROAMED: VE IOTTIA
WHT HOME BE THAP
BOLLY ADED NEAD BATAKE LIM
(a) Use indoor scene with people and newspapers, swap seated person's head with cat head, add text below
ALIENS
Tra te lust may
The rt Fost Tile.
We ljust lost may.
(b) Add hair and beard to man left, remove green item from hand, add white text below
fdl get
South sir tokot
From stae inko Eaffeir thwe? fow pullaky crean
Sall Bei Galk wea petlonats, Tageding from golladi, loeader
Sautku in satred
"seting that of snlty
Plaze bo on abtice
Lewate
(c) Modify the text on the paper in the character's hands.
Figure 13: Qualitative comparison of neural driven and specch-neurgs fusion methods and text-
prompt baseline for Text Editing category.
Original
(Ours)
Speech (Ours)
Only
(a) Use space background, add animated character, and morph dog into a long-legged space creature
Your purto aur tramned
pearts out pila Talkuct
[b] Add two cartoon characters and text bubble with specific phrase.
Original
Ground Truth
Improve color saturation and balance overall tones for more image
Figure 14: Qualitative amalysiscomparison onf our neural-driven and speech- speech-neural neural fusion methods and text-prompt baseline for three failure cases: (a) Overly exaggerated descriptions, e.g., "long- legged space creature": (b) Vague instructions lacking detail, such omitting whether retain the background; (c) Uncommon image dimensions, e.g., panoramic input images.
34

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state the paper's key contributions, including the proposed method, the dataset construction, and the experiment evaluation. The claims are consistent with the scope and content presented in the main body of the paper.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
We have discussed the limitations of this current research Section A.3.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-speciiiatiinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
35

---

Answer:
[NA]
Justification:
Our paper does not explicitly include mathematical theoretical results.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: All implementation details, data splits, training settings, and evaluation metrics are described in Sections and 5, ensuring reprodueibility the main results. We will also release our dataset and code for reproducibility.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, other means that appropriate to the research performed.
also be provided via detailed
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reprodueibility may be tricky in some cases, in which authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
36

---

Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification: We provide code and a sample of the dataset (including 1/10 instances of the full dataset) in the supplementary materials. Due to size constraints and anonymity requirements, the full dataset will not be released at submission time. We will release the complete dataset and code upon publication via the public project website.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips cc/
Sublic/guides /tt.ddee/ooeeeuumiiseeeeeeeeeeee poodeesummissooPooiioo for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https:
cttpssccccbbllc wetiiicgguieeeeeeeeeeeeee Senieddeee/oeSuummiiiie issiissiion ssionPolicy) for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: We specify all training and testing details, including data splits, hyperparame- ters, and optimizer configurations in Section with additional implementation information provided the supplemental materials.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification:
We present the error bar and statistical significances in the main experiments.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by enror bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
37

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information the puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: We report the detailed compute resources used for all experiments in Sec- tion 5.1.
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics https: :ttppp::///euuee http///eerrrssu.ce wtt.ee/rppp.cccpppiii//eiiieaaea ctw.eeeeeeeiccicciieeeeeeeee
Answer: [Yes]
Justification:
We fully adhere the NeurlPS Code of Ethics throughout the whole process.
Guidelines:
The answer NA means that the authors have reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. there special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: We have discussed the broader impacts of the neural-driven image editing technique in both the conclusion and appendix.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
38

---

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), faimess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
Justification: We will carefully review applicants and provide protocol regulations to prevent abuse when releasing models, which have been discussed in the appendix.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging. and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
We have cited all original assets contributing to the research in this paper.
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset used and, possible, include a URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
39

---

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: Our new dataset and codes well documented, which will be provided alongside the dataset and codes upon release.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [Yes]
Justification: We provide full details of the instructions and sufficient monetary reimburse- ment subjects, which is above the minimum wage.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [Yes]
Justification: Our experiment has obtained the official IRB approvals from the corresponding institution.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
40

---

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if' applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development in this research does not involve LLMS as any important, original, non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy (https: / //neruurpp.... wcc/oonfeeencce/2225/11111 for what should or should not be described.
41


Reviews:
[{"review_id": "P1aBgqzNl3", "reviewer": "Reviewer_Pqtj", "summary": "The paper proses very first framework of image editing model using Neural brain signal condition.", "questions": "See weakness", "limitations": "No", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n1. This is the very first work and overall very interesting work.\n\nWeakness\n\nAlthough the diffusion model arthitecture and training setup is nice and plausible , there are major issues which must be addressed.\n1. Dataset bias problem . The authors collected paired dataset using 12 subjects, and set training and test setup. \nAre the subjects of training set data and test set data independent? If the training and test data set share same subjects, then it is intra-subject experiment which lacks generalizability. Please elaborate the dataset collection protocol. \n\n2. Usage of speech signal. In the method, the conditional model takes speech signal along with neural signals. Also in the results, output from only neural signal shows significantly low performance compared to neural+speech. It seems that speech signal plays major role in editing. Please elaborate this part and why neural signal only setup does not work. \n\nI will raise the scores if  these two points are properly addressed as these points are critical on the reliability and meaning of the overall proposed work.\n\nMinor issue: During the data. collection, are the authors properly taken the approval from ethics board?", "was_revised": true, "final_justification": "The rebuttal addressed most of my concerns, therefore I raise my score", "rebuttal": "# Q1\nDataset bias problem. The authors collected paired dataset using 12 subjects, and set training and test setup. Are the subjects of training set data and test set data independent? If the training and test data set share same subjects, then it is intra-subject experiment which lacks generalizability. Please elaborate the dataset collection protocol.\n# A1\n## 1. Test data\n We are deeply grateful for your insightful and highly constructive feedback, which has significantly enhanced the completeness and scientific rigor of our paper. \n      Regarding the dataset bias issue, our original dataset was collected from 12 participants (6 female, 6 male, mean age 24.5 ± 2.5 years), each contributing around 2,000 paired samples under carefully controlled experimental conditions (see Appendix A for detailed protocol). While our initial split ensured training/test separation, we acknowledge that the possibility of subject overlap could limit generalizability.\n     To address this, we performed additional cross-subject evaluations with 5 new participants (3 male, 2 female, ages 13–63). The results confirm that the model maintains strong generalization when applied to unseen individuals, with performance trends on CLIP-I, DINO, and CLIP-T remaining consistent with those from the original test set. This provides evidence that our approach is not overly reliant on subject-specific neural signatures, but instead captures transferable semantic representations.\n\n\n### Table 1. Performance comparison on the original test set with 12 subjects and the unseen test set with 5 new subjects.\n\n| Test Dataset       | Method               | Conditioning       | L1 ↓    | L2 ↓    | CLIP-I ↑ | DINO ↑  | CLIP-T ↑ |\n|--------------------|----------------------|---------------------|--------|--------|----------|---------|----------|\n| Original Test Set  | OmniControl          | Text                | 0.2632 | 0.1161 | 0.6558   | 0.4636  | 0.2549   |\n|                    | OmniControl          | Speech              | 0.2714 | 0.1209 | 0.6146   | 0.3717  | 0.2501   |\n|                    | **LoongX (OmniControl)** | **Neural Signals**     | **0.2509** | **0.1029** | **0.6605** | **0.4812** | **0.2436** |\n|                    | **LoongX (OmniControl)** | **Signals + Speech**   | **0.2594** | **0.1080** | **0.6374** | **0.4205** | **0.2588** |\n| Unseen Test Set    | OmniControl          | Text only           | 0.2581 | 0.1133 | 0.6528   | 0.4655  | 0.2553   |\n|                    | OmniControl          | Speech              | 0.2779 | 0.1271 | 0.6221   | 0.3942  | 0.2508   |\n|                    | **LoongX (OmniControl)** | **Neural Signals**     | **0.2574** | **0.1090** | **0.6019** | **0.4037** | **0.2403** |\n|                    | **LoongX (OmniControl)** | **Signals + Speech**   | **0.2668** | **0.1146** | **0.6049** | **0.4447** | **0.2568** |\n\n\n\nThe basic background information and sample size of the participants, as well as the specific individual performance based on LoongX (Signals+Speech), are as follows:\n\n### Table 2. Basic information and performance of all subjects using LoongX (Signals+Speech).\n| Test Dataset       | Subject    | Gender | Age | Samples | Attention | L1     | L2     | CLIP_I | DINO   | CLIP-T |\n|--------------------|------------|--------|-----|---------|-----------|--------|--------|--------|--------|--------|\n| Original Test Set  | Subject 1  | Female | 25  | 2003    | 0.0887    | 0.2657 | 0.1109 | 0.6370  | 0.4890  | 0.2196 |\n|                    | Subject 2  | Female | 29  | 2000    | 0.0817    | 0.2416 | 0.0950 | 0.6575 | 0.5021 | 0.2249 |\n|                    | Subject 3  | Female | 26  | 2001    | 0.1340    | 0.2448 | 0.0963 | 0.6660 | 0.4878 | 0.2337 |\n|                    | Subject 4  | Female | 22  | 1999    | 0.0739    | 0.2533 | 0.1005 | 0.6394 | 0.4606 | 0.2270 |\n|                    | Subject 5  | Female | 28  | 1992    | 0.1218    | 0.2552 | 0.1031 | 0.6144 | 0.4157 | 0.2260 |\n|                    | Subject 6  | Female | 29  | 1964    | 0.0822    | 0.2511 | 0.1000 | 0.6449 | 0.4564 | 0.2213 |\n|                    | Subject 7  | Male   | 22  | 1988    | 0.0851    | 0.2711 | 0.1160 | 0.6515 | 0.4634 | 0.2234 |\n|                    | Subject 8  | Male   | 23  | 1993    | 0.1105    | 0.2528 | 0.1017 | 0.6638 | 0.4833 | 0.2242 |\n|                    | Subject 9  | Male   | 22  | 1988    | 0.1500    | 0.2497 | 0.0998 | 0.6355 | 0.4571 | 0.2212 |\n|                    | Subject 10 | Male   | 24  | 2000    | 0.1298    | 0.2657 | 0.1144 | 0.6194 | 0.4240 | 0.2220 |\n|                    | Subject 11 | Male   | 24  | 2000    | 0.0954    | 0.2744 | 0.1151 | 0.6386 | 0.4339 | 0.2299 |\n|                    | Subject 12 | Male   | 22  | 2000    | 0.0971    | 0.2551 | 0.1034 | 0.6213 | 0.4323 | 0.2250 |\n| Unseen Test Set    | Subject 13 | Male   | 35  | 500     | 0.1210    | 0.2681 | 0.1174 | 0.6022 | 0.4418 | 0.2594 |\n|                    | Subject 14 | Female | 30  | 500     | 0.0775    | 0.2688 | 0.1179 | 0.6051 | 0.4405 | 0.2553 |\n|                    | Subject 15 | Male   | 13  | 200     | 0.0727    | 0.2618 | 0.1001 | 0.6055 | 0.4472 | 0.2576 |\n|                    | Subject 16 | Female | 62  | 100     | 0.0441    | 0.2660 | 0.1141 | 0.6196 | 0.4611 | 0.2472 |\n|                    | Subject 17 | Male   | 63  | 100     | 0.0520    | 0.2610 | 0.1133 | 0.6017 | 0.4588 | 0.2595 |\n\n\n## 2. Dataset collection protocol \nDataset collection protocol details have been supplemented into Section C Supplementary Dataset Details in the supplementary PDF file (Main text 3.1; A.2).\n\n# Q2\nUsage of speech signal. In the method, the conditional model takes speech signal along with neural signals. Also in the results, output from only neural signal shows significantly low performance compared to neural+speech. It seems that speech signal plays major role in editing. Please elaborate this part and why neural signal only setup does not work.\n# A2\nWe appreciate the reviewer’s attention to the role of speech signals in our method. In our framework, speech serves as a complementary modality rather than the dominant one, supplying explicit high-level linguistic cues often absent from neural signals alone. EEG, fNIRS, and PPG effectively capture intent with fine temporal resolution, but they encode semantics implicitly and intrinsically. Compared with these neural signals, Speech supplements auxiliary semantic information for more abstract tasks or specific text editing tasks. For example, a short spoken instruction (e.g., “make the sky look like heaven”) provides a low-entropy prior that helps disambiguate editing intent, while neural signals control the global style and fine-grained visual effects.\nImportantly, our experiments show that neural signals alone yield robust and competitive performance, especially for low-level visual edits such as global style adjustments. For instance, the neural-only setup achieves a CLIP-I score of 0.6605, which is slightly higher than the 0.6374 observed when speech is included. \n\nHowever, when it comes to tasks requiring more nuanced semantic comprehension, combining speech with neural signals leads to better alignment with the intended instruction, as indicated by an increase in the CLIP-T score from 0.2436 (neural-only) to 0.2588 (neural+speech), a relative improvement of about 0.015. This indicates that each modality brings unique strengths: neural signals excel at pixel-level control and global editing, while speech boosts complex semantic alignment.\nOverall, the gap between the two settings is moderate rather than substantial, suggesting that neural signals are already effective, but can be further enhanced by incorporating speech. We believe this underscores the complementary role of speech in our system, and we appreciate the opportunity to clarify this point.\n\n\n### Table 1: Automatic Evaluation of Editing Methods\n\n| Method               | Conditioning      | L1 ↓   | L2 ↓   | LPIPS ↓ | CLIP-I ↑ | DINO ↑ | CLIP-T ↑ |\n|----------------------|-------------------|--------|--------|----------|-----------|--------|-----------|\n| OmniControl          | Text              | 0.2632 | 0.1161 | 0.7482   | 0.6558    | 0.4636 | 0.2549    |\n| OmniControl          | Speech            | 0.2714 | 0.1209 | 0.7625   | 0.6146    | 0.3717 | 0.2501    |\n| LoongX (OmniControl) | Neural Signals    | 0.2509 | 0.1029 | 0.7227   | 0.6605    | 0.4812 | 0.2436    |\n| LoongX (OmniControl) | Signals + Speech  | 0.2594 | 0.1080 | 0.7245   | 0.6374    | 0.4205 | 0.2588    |\n\nWe appreciate the reviewer’s careful assessment and the opportunity to clarify these critical aspects. With these additional results and protocol details, we believe the work now provides a more complete and reliable account of both methodological soundness and empirical validity. We hope these clarifications help the reviewer better appreciate the strength and significance of the proposed work.\n\n# Minor Q3\nDuring the data. collection, are the authors properly taken the approval from ethics board?\n# A3\n  Yes, we have obtained ethical approval from the relevant institutional ethics review board before conducting the experiments, as stated in the manuscript and supplementary materials. All participants were informed of the procedure and gave their consent before participation. We will attach the specific institutional information to the camera-ready version."}, {"review_id": "D8kDa5IqHP", "reviewer": "Reviewer_ZTwA", "summary": "This paper introduces LoongX, which is a multimodal image editing approach that goes beyond the limits of text and image based editing. The authors acknowledge that the current way of instructing the models may be insufficient for certain types of edits, and offer a solution that is empowered with multiple signals from different modalities such as speech, EEG signals and head motion. The proposed model involves a diffusion transformer, which accepts multimodal signals instead of text only. As a part of the proposed method, LoongX proposes two modules to effectively encode different modalities, which are labeled as Cross-Scale State Space Module and Dynamic Gated Fusion Module. Given the quantitative evaluations with multi-modal editing methods such as OminiControl and qualitative editing results, LoongX is able to perform edits effectively while broadening the range of editing capabilities of diffusion transformers.", "questions": "- Is the diffusion transformer training conducted from scratch or a pretrained network such as FLUX or SD3 is used? In addition, what are the architectural details of the transformer? This would help understanding the scalability of the method and its further potential.\n- Are there any limitations of the method in terms of the edits that are performed? What are the common failure cases? The authors are encouraged to discuss such limitations with examples (or evaluations), which would impact my judgement positively.\n- What are the details of the quantitative evaluation/ Which types of edits are evaluated?", "limitations": "The authors provided a short discussion on limitations, addressing that abstract concepts are still hard to edit. While acknowledging this, authors are encouraged to expand this discussion. As an example, in Figure 7 (a) it appears that the color information of the cat cannot be preserved. If there are such limitations in certain types of edits, the authors should be transparent in their discussion, by including comparisons with the baseline method (OminiControl).", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n- The proposed method expands the multi-modal inputs used in editing from image-text pairing to multiple signals involving inputs such as EEG, head motion and speech. This is a significant effort towards achieving true multimodality, and in expanding the editing tasks available.\n- LoongX introduces two modules that are crucial in interpreting multi-modal inputs where general purpose encoders are not available (which is not the case for image and text representations).\n- Over the qualitative results, the proposed method shows that edits that can not easily expressed with text prompts are made possible, such as Figure 10 (a), This is a significant expansion over the existing editing methods.\n- Quantitative results shows that the proposed approach is competitive and even surpassing the baseline in certain metrics, when compared with competing multi-modal approaches.\n\n**Weaknesses**\n- The details for the baseline diffusion transformer is not clear, the authors should provide more details on the architecture or if it is fine-tuned over an existing model or trained from scratch.\n- While acknowledging that the proposed method may fall short if compared with image editing methods trained on massive amounts of text and image, it would be still helpful to compare the proposed approach with text based editing methods, in addition to multi-modal ones. It is acceptable that the proposed approach may not outperform them, but the gap should be clear for possible future work.\n- In addition to qualitative comparisons for the use of different modalities, comparisons with competing methods should also be presented.\n- The authors reported $L_1$ and $L_2$ distances as evaluation metrics for content preservation. Given the presence of DINO and CLIP-I based evaluation, this seems redundant. Authors can make their evaluation more extensive by conducting a user study instead and providing perceptual evaluation this way (which can cover both editability and content preservation), Since image editing cab be subjective, incorporating such a study would be valuable.\n- The details of the evaluations set, and the types of editing tasks are not precisely explained. The authors are encouraged to provide sufficient clarifications and discuss the failure cases of their approach.", "was_revised": true, "final_justification": "The questions that I had in my preliminary justification has been addressed by the authors. Given the multimodal nature of the method and the supplementary results, I keep my rating as positive.", "rebuttal": "# A1\n Thank you for your insightful question. The Diffusion Transformer (DiT) used in our experiments is based on the Flux.1-Dev pretrained model, and fine-tuned on our dataset using LoRA (Low-Rank Adaptation). We trained it for 12,000 steps over 2 epochs, using an Adam optimizer with a learning rate of 1e-4. The number of LoRA layers is 10, with a total of 28M parameters. We will add more detailed information on the model's architecture and the fine-tuning strategy in the revised manuscript.\n\n# A2\\A3\nWe expanded our evaluation to include state-of-the-art text-based editing methods, with a particular focus on Flux-Kontext, which represents one of the strongest open-sourced text-driven editing frameworks at present. This addition provides a clearer perspective on where our multimodal approach stands relative to established text-only paradigms.\n\nAs the results in Table 1 demonstrate, text-only Flux-Kontext indeed performs strongly in semantic alignment (CLIP-T = 0.2728) and overall visual fidelity. However, our LoongX framework integrated into the same Flux-Kontext backbone (fine-tuned on L-Mind) further elevates performance across nearly all metrics, achieving the highest CLIP-I (0.8078) and CLIP-T (0.2730).\n\n\n| Method                 | Conditioning     | Params (B) | Training FLOPs (×10^9) | L1 ↓    | L2 ↓    | LPIPS ↓ | CLIP-I ↑ | DINO ↑ | CLIP-T ↑ |\n|------------------------|-----------------|------------|-------------------------|---------|---------|---------|----------|--------|----------|\n| OmniControl            | Text            | 12.5       | 2.88                    | 0.2632  | 0.1161  | 0.7482  | 0.6558   | 0.4636 | 0.2549   |\n| OmniControl            | Speech          | 12.5       | 2.88                    | 0.2714  | 0.1209  | 0.7625  | 0.6146   | 0.3717 | 0.2501   |\n| ICEdit                 | Text            | 12.6       | -                       | 0.2457  | 0.1071  | 0.6418  | 0.6979   | 0.5129 | 0.2572   |\n| Bagel                  | Text            | 7.8        | -                       | 0.1921  | 0.0878  | 0.5551  | 0.8035   | 0.6894 | 0.2715   |\n| Flux-Kontext           | Text            | 12.6       | -                       | 0.2282  | 0.0986  | 0.6238  | 0.7729   | 0.6553 | 0.2728   |\n| LoongX (OmniControl)   | Neural Signals  | 12.5       | 3.02                    | 0.2509  | 0.1029  | 0.7227  | 0.6605   | 0.4812 | 0.2436   |\n| LoongX (OmniControl)   | Signals + Speech| 12.5       | 3.04                    | 0.2594  | 0.1080  | 0.7245  | 0.6374   | 0.4205 | 0.2588   |\n| LoongX (Bagel)         | Signals + Speech| 7.8        | 3.04                    | 0.1968  | 0.0905  | 0.5625  | 0.7998   | 0.6831 | 0.2729   |\n| LoongX (Flux-Kontext)  | Signals + Speech| 12.6       | 3.04                    | 0.1905  | 0.0804  | 0.4498  | 0.8078   | 0.7031 | 0.2730   |\n\n# A4\n  Thank you for your constructive advice. We have conducted a systematic user study to comprehensively evaluate both Editability (how well the system followed the user's intention) and Content Preservation (how well the original content of the image was preserved after editing), as recommended. We recruited 10 annotators (5 male, 5 female, ages 18-40), each of whom independently rated the edited images generated under different conditioning methods (see Table 2 and Table 3). Methods included both baselines (OmniControl, Flux-Kontext) and our proposed LoongX framework under different conditions. The scoring was performed in a double-blind manner and on a 0–5 scale, where 0 indicates \"completely unacceptable\" and 5 indicates \"excellent\".\n\nThe combination of neural signals and speech (Editability: 4.63, Content Preservation: 4.40) outperforms either modality alone. While text inputs offer high editability (4.55) due to their explicit semantic guidance, neural signals exhibit superior content preservation (4.28) by capturing implicit visual preferences. By integrating both, our method achieves a better balance between user intention and visual fidelity.\n\n\n| Method        | Conditioning         | Original edited image | OmniControl | LoongX (OmniControl) | LoongX (OmniControl) | Flux-Kontext | LoongX (Flux-Kontext) |\n|---------------|----------------------|-----------------------|-------------|----------------------|----------------------|--------------|-----------------------|\n|               |                      |                       | Text Only   | Neural Signal Only   | Neural Signal + Speech | Text Only   | Neural Signal + Speech |\n| Editability   |                      | 4.87                  | 3.99        | 2.60                 | 4.34                 | 4.48         | 4.63                  |\n| Content Preservation |               | 4.44                  | 4.02        | 4.17                 | 4.09                 | 4.34         | 4.40                  |\n\n# A5\n We have expanded the explanation of our evaluation setup and the editing task categories, and we have also added further discussion of failure cases, as detailed in Section A.5 of the supplementary material.\n\nOur evaluation framework consists of four representative categories of image editing tasks, as visualized in Figures 8–Figure 11 in the supplementary material:\n1) Global Editing – e.g., changing the overall color tone, saturation, or lighting of the entire image.\n2) Background Editing – e.g., modifying or replacing background elements while keeping the subject unchanged.\n3) Object Editing – e.g., altering specific foreground elements like clothing color, object size, or replacing an item.\n4) Text Editing – e.g., modifying embedded textual content within images.\n\nEach category is evaluated both quantitatively and qualitatively to assess alignment with user intent and perceptual fidelity.\n\nIn addition, we have carefully analyzed and reported failure cases in the supplementary material. These cases highlight limitations such as:\n\n (1) Highly imaginative prompts that lie far outside the training distribution (e.g., “long-legged space creature”),\n\n (2) Ambiguous instructions lacking critical semantic detail (e.g., whether the original background should be retained), and\n\n (3) Non-standard input formats (e.g., panoramic aspect ratios) that can challenge spatial reasoning.\n\nThese failure cases highlight current limitations in generalization and input robustness, and we view them as opportunities for future improvement.\n\n# A6\nOur diffusion transformer is not trained from scratch; instead, we initialize it using the publicly available FLUX.1-dev pretrained weights from Black Forest Labs. This model adopts a U-Net-style DiT architecture with transformer blocks applied at each resolution scale. The full model consists of approximately 860 million parameters, operating at 512×512 resolution, and is pretrained on high-quality text-to-image datasets. Leveraging this foundation allows us to benefit from its robust visual representation capability while focusing our training on the unique neural-conditioning aspects of our task.\n\nTo adapt FLUX to our multimodal setting, we employ LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning. This enables us to fine-tune the model on our neuro-conditioned dataset without the need for full backpropagation through all layers, preserving generalization while reducing computational cost. Our conditioning pipeline feeds into the FLUX transformer via our proposed DGF fusion module, which supports both text embeddings (via T5-XXL and CLIP) and neural encodings (via CS3 encoder), as described in Section A.4 of the supplementary material.\n\n# A7\nWe appreciate the reviewer’s suggestion and agree that recognizing the limitations of our method is essential for a balanced evaluation. In the supplementary material (Section A.5: More Failure Cases), we expand on failure cases with qualitative examples in Figure 12.\n\nOur analysis highlights three main scenarios where limitations emerge:\n\n1. **Abstract or highly imaginative instructions**: For concepts far beyond the training distribution (e.g., “morph the dog into a long-legged space creature”), outputs become semantically inconsistent or implausible due to the lack of concrete grounding.\n2. **Vague or under-specified prompts**: Ambiguity in instructions (Figure 12b) can cause mismatches with user intent, such as uncertainty over whether to retain or replace a background. This underscores the need for improved intent disambiguation via feedback or uncertainty modeling.\n3. **Non-standard input formats**: With unusual aspect ratios (Figure 12c), spatial misalignment or degraded quality may occur, likely due to training data bias. Future work could address this with augmentation or scale-invariant architectures.\n\n# A8\n\nThe quantitative evaluation covered tasks including global texture changes, object removal, and background modifications. We report the following results:\n- Global texture editing: CLIP-I: 0.6605\n- Object removal: DINO: 0.4812\n- Text edits: CLIP-T: 0.2588\n\n# A9\n\nWe acknowledge the issues raised regarding the color preservation in Figure 7, where certain edits, like \"place the cat above,\" were not fully captured by our model. We have provided an extended limitations discussion to include more examples like this, where certain fine-grained details or semantic richness were not fully preserved. Additionally, we are addressing the issues causing these artifacts, which are largely due to the backbone model's capacity and the inherent challenges in handling complex image semantics. \n\n# A10\n\nAs confirmed, the submission of supplementary material alongside the main paper was in accordance with the conference's submission guidelines. Thank you for your detailed review."}, {"review_id": "0vT8FMm8sK", "reviewer": "Reviewer_amVP", "summary": "This paper proposes an image editing method based on multimodal neural signals, LoongX. This method breaks away from the dependence on language cues and physical interactions, allowing image editing through neurophysiological signals such as EEG, fNIRS, PPG, etc. The authors also propose a large-scale dataset, L-Mind, which contains 23,928 neural signals collected from image editing samples. Two key modules, the CS3 and DGF, combine the diffusion model (DiT) to achieve an understanding of editing intentions and image generation. Experiments show that LoongX can be compared with and even surpass text-based editing methods on multiple metrics, especially when combined with speech input.", "questions": "1. I am not an expert in neural signal processing, so I am not sure whether relying solely on participants reading the editing instructions aloud is sufficient to ensure that the collected signals are truly related to the editing intent. Please clarify how to ensure that the collected neural signals represent editing intent rather than language comprehension or speech production.\n\n2. In Figure 7 (row 1), the neural-based methods do not fulfill the instruction's spatial constraint (“place the cat above it”). Please address this inconsistency with the claim that neural signals are superior for background editing.\n\n3. Discuss the practicality of your approach given the reliance on EEG, fNIRS, and PPG hardware.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. This work is the first to integrate EEG, fNIRS, PPG, and motion signals for image editing, introducing a novel and promising direction of neural-driven editing.\n\n2. The authors present a new large-scale multimodal dataset and commit to releasing both data and code, which will benefit future research and advance the field.\n\n3. The proposed LoongX framework is well-designed, combining the CS3 encoder and DGF fusion module to efficiently extract and integrate multimodal neural features.\n\n4. The experiments are comprehensive, including ablation studies, comparative baselines, and evaluations across multiple editing types, demonstrating the method’s effectiveness and robustness.\n\n**Weaknesses**\n\n1. The method relies on strong assumptions about the correspondence between neural signals and editing intent, without sufficient validation. During data collection, participants merely read editing instructions aloud, assuming they simultaneously imagine the edits. This unverified reliance on internal mental imagery raises concerns about whether the captured signals truly reflect editing semantics. Moreover, EEG and fNIRS signals may be dominated by language processing and speech-related activity, potentially confounding the neural representation of editing intent. No control experiments (e.g., reading unrelated text or silent imagination) are included to isolate editing-specific neural patterns.\n\n2. The claim that neural signals outperform text prompts in intuitive visual tasks (e.g., background editing) is contradicted by qualitative results. In Figure 7 (row 1), the Neural and Neural+Speech methods fail to execute the core instruction “place the cat above it,” while the Text-based method achieves the intended spatial manipulation. This inconsistency suggests that neural signals may struggle with structural understanding.\n\n3. The approach depends on specialized hardware (e.g., fNIRS, PPG, EEG), limiting its practicality and scalability in real-world settings.\n\n4. The dataset includes only 12 participants, limiting generalizability. Broader evaluation across diverse populations (e.g., users with disabilities, different ages, or cultural backgrounds) is necessary to assess robustness and applicability.", "was_revised": true, "final_justification": "The authors’ response has addressed most of my concerns. I have decided to retain my original score.", "rebuttal": "# Q1 & W1\nThe method relies on strong assumptions about the correspondence between neural signals and editing intent, without sufficient validation. During data collection, participants merely read editing instructions aloud, assuming they simultaneously imagine the edits. This unverified reliance on internal mental imagery raises concerns about whether the captured signals truly reflect editing semantics. Moreover, EEG and fNIRS signals may be dominated by language processing and speech-related activity, potentially confounding the neural representation of editing intent. No control experiments (e.g., reading unrelated text or silent imagination) are included to isolate editing-specific neural patterns.\n# A1\nTo ensure that the recorded neural signals truly reflected editing-specific mental states rather than mere language processing, we provided explicit instructions and guided training sessions to familiarize participants with the process of vivid mental imagery. Specifically, participants were encouraged to not only read the prompts aloud but to actively and vividly simulate the visual editing actions in their mind’s eye. This imaginative engagement was reinforced through a brief practice session prior to recording, helping establish a consistent attention and cognitive strategy across subjects and ensuring that the brain activity captured by the neural signals corresponded only to intensive editing intentions. \n\nMoreover, as presented in Table 4 in Section A Technical Appendix, we tracked participants’ attention levels using alpha-to-theta EEG ratios, which is an established neural index of attentional engagement, and found that most participants maintained moderate to high attention levels throughout the task. These attention scores serve as an additional quality control signal, indirectly validating that participants were cognitively invested in the task, beyond passive reading.\n\nAdditionally, performed an unseen silent EEG experiment with new participants (N=2). The results show that the silent EEG from non-imagined read-only conditions had significantly lower performance (0.42 CLIP-I) than the imagined condition (0.63 CLIP-I), supporting the correspondence between neural signals and editing intent.\n\n# Q2 & W2\nThe claim that neural signals outperform text prompts in intuitive visual tasks (e.g., background editing) is contradicted by qualitative results. In Figure 7 (row 1), the Neural and Neural+Speech methods fail to execute the core instruction “place the cat above it,” while the Text-based method achieves the intended spatial manipulation. This inconsistency suggests that neural signals may struggle with structural understanding.\n# A2\nRegarding the observation in Figure 7 (row 1), it is important to note that while the neural signals and neural+speech methods performed better than the text-based method in several tasks, the text-based method did better in spatial manipulation tasks like “place the cat above.” However, our method excels in achieving the object manipulation like let the cat in the figure to \"looking down\", while text-based method failed to achieve that.\n\nOur neural signal-based method can indeed surpass the text-based method in several scenarios, e.g., the object editing task showed significant improvement with CLIP-I scores of 0.6605 compared to 0.6350 for the text-based method.\n\nWe will further clarify in the revised manuscript that the neural signals are better suited for low-level visual edits and that neural signal-based and text-based methods can offer complementary strengths, not necessarily one being superior across all tasks.\n\n# Q3 & W3\nThe approach depends on specialized hardware (e.g., fNIRS, PPG, EEG), limiting its practicality and scalability in real-world settings.\n# A3\nWhile our current approach utilizes specialized equipment such as fNIRS, PPG, and EEG, we strongly believe that the rapid evolution of wearable brain-computer interface (BCI) technologies will soon make such devices as ubiquitous and user-friendly as today’s wearable smart watches. In the near future, lightweight and affordable neural headbands are likely to become part of everyday life, enabling not only health monitoring but also seamless interaction with digital systems. In this context, basic applications like neural-driven image editing will no longer be constrained by hardware limitations. Enhancing accessibility has always been a central motivation of our research, and we envision our system evolving hand-in-hand with advances in wearable BCI to deliver practical, real-world value.\n\n# Q4\nThe dataset includes only 12 participants, limiting generalizability. Broader evaluation across diverse populations (e.g., users with disabilities, different ages, or cultural backgrounds) is necessary to assess robustness and applicability.\n# A4\nWe appreciate the reviewer’s concern regarding generalizability. While our current dataset consists of 12 primary participants, this sample size is comparable to or even larger than several published works in neural signal-driven generation. For instance, EEG2Video [Li et al., 2023] utilized data from only 6 participants, and DreamDiffusion [Liu et al., 2024] involved 10 participants, yet both demonstrated compelling results.\n\nTo further assess robustness, we conducted cross-subject experiments involving 7 new participants who were not included in model training. Notably, this group includes individuals from diverse age ranges (a middle school student and an elderly participant aged 67), as well as one participant with a physical disability (missing fingers on the dominant hand). Despite these variations, our model maintained  60.49% CLIP-I and 44.47% DINO scores of its intrasubject performance, demonstrating encouraging generalization across populations.  More detailed information about all test data can be found in response to Reviewer 1, Question 1.\n\n### Table: Subject-Level Metrics – Unseen without Speech (Silence Experiment)\n| Testdata Type              | Subject   | Gender | Age | Samples | Attention |   L1   |   L2   | CLIP_I |  DINO  | CLIP-T |\n|----------------------------|-----------|--------|-----|---------|-----------|--------|--------|--------|--------|--------|\n| Unseen without Speech      | Subject18 | Male   | 23  | 200     | 0.0316    | 0.2432 | 0.7740 | 0.5156 | 0.3606 | 0.2159 |\n| (Silence experiment)       | Subject19 | Female | 25  | 200     | 0.0872    | 0.2293 | 0.0775 | 0.5238 | 0.4830 | 0.2112 |\n\n\n\n### Table: Evaluation Metrics – Silence Subjects\n\n| Participants        | Method               | Conditioning     | L1 ↓   | L2 ↓   | CLIP-I ↑ | DINO ↑ | CLIP-T ↑ |\n|---------------------|----------------------|------------------|--------|--------|-----------|--------|-----------|\n| 2 Silence Subjects  | OmniControl          | Text-only        | 0.2417 | 0.1021 | 0.6551    | 0.4650 | 0.2542    |\n|                     | LoongX (OmniControl) | Neural Signals   | 0.2362 | 0.0837 | 0.5197    | 0.3218 | 0.2135    |"}, {"review_id": "pfjslxiidi", "reviewer": "Reviewer_c9x8", "summary": "The paper introduces LoongX, an image editing approach based on neural and speech signals. The authors collect a comprehensive set of human stimuli for image editing including EEG, fNIRS, PPG, motion and speech signals. \nThe proposed LoongX approach demonstrates image editing capabilities when conditioned on these signals providing a new paradigm beyond textual image editing. Further, the authors provide novel ablations into the importance of different brain region signals for image manipulation.", "questions": "Please address the questions posed in the weakness section .", "limitations": "The authors discuss some key limitations in Section 5.5", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 4, "strengths_and_weaknesses": "# Strengths\n\n- General purpose image editing with neural conditioning is a novel task and field. This paper marks a potentially seminal contribution\n- Strong user study design and data collection process. Excellent, controlled study setup, good description of participant demographics and prior IRB approval \n- LMind as strong dataset contribution \n- experimental results look promising\n- Paper provides some interesting insights on importance of different signals and brain regions for image manipulation \n\n# Weaknesses \n**Missing details and strong assumptions**\nIn a lot of places the paper fails to provide relevant information, references or experiments \n- Strong architectural assumptions on input encoding using CS3 and DGF modules. While this setup generally seems suitable there Is no clear information why these exact design choices were taken. Either a strong justification, reference to prior work demonstrating the effectiveness of certain submodules or comprehensive ablations would be needed. \n- Additionally, the LoongX seemingly arbitrarily combines EEG/PGG with T5 embeddings and fNIRS/Motion with CLIP with no clear intuition \n- There is virtually no information provided on the actual DiT backbone or how it is conditioned in the input image. What's the specific architecture, parameter count? Is it pre-trained or trained from scratch? What encoding/framework is used for input image conditioning? \n- L 205 mentions that existing near-text corpora are used for pre-training without citation \n- Information on pre-training and fine-tuning is lackluster (also not present in the appendix). for how many epochs/steps is the model trained? are their any interesting observations in terms of training stability, loss curves, etc. for this new form of conditioning \n- Also obvious questions on the overall setup are not addressed. From Eq (10) we can see that the model is trained using a flow objective. But what kind of flow? Does the model use CFG? If so, what does the null-conditioning/negative prompt look like?\n- The visualization in Fig. 6 is incredibly hard to follow \n\n**Experimental Design** \nAdditionally there are some issues with the experimental design and conclusions drawn from these results \n- Choice of metrics is unconventional/unsuitable. Specifically, pixel-based L1, L2 are ill suited in assessing image editing since they do not actually measure human perceived similarity. Consequently, the standard choice in image editing literature is to use LPIPS [1] instead. \n- Similarly, reporting performance as singular value per method in a table does not accurately reflect the performance of an image editing methods. There is an inherent tradeoff between similarity with the input image and alignment with the edit instruction. This trade-off is much better reported as a curve over different hyper parameter configurations/conditioning strengths. (See InstructPix2Pix for example).\n- It is also unclear what the DINO metric is supposed to measure here  \n- OmniControl as the sole baseline for textual editing does not make for a fair comparison. Firstly, OmniControl's strength is not even in textual image editing but strong reference usage. Consequently, the reported baseline for text-only does not actually reflect the strength of current textual image editing approaches. For reference, I performed the cherry-picked examples in Fig. 7 with Flux-Kontext and it strongly outperforms all depicted examples. Consequently, the conclusions drawn when comparing against textual conditioning are heavily skewed as well. These limitations are only exacerbated by the lack of information on DiT size and training FLOPs. \n- There are a plethora of recent textual image editing works (a lot of which the authors cited themselves) that would have given a better baseline comparison for textual image editing. \n\nAdditionally the paper would benefit from actually demonstrating \"hands-free image editing\" where users would interact with the trained model in the same way as during data collection. The transfer from performance reported on a held out test-set to actual downstream usability seems less obvious for this form of interaction. \n\n## Minor Comments\n\n- Missing related work. DreamConnect [2] also performs image editing using neural signals. \n\n[1] Zhang et al. \"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric\" (2018) CVPR\n\n[2] Yasheng Sun et al. \"Connecting Dreams with Visual Brainstorming Instruction\" (2025) Visual Intelligence", "was_revised": true, "final_justification": "The rebuttal addresses the majority of my concerns. While some design choices in the paper could be approved upon, I believe it makes a valuable contribution to community and vote for acceptance.", "rebuttal": "Thank you for your very detailed comments and suggestions. However, due to the word limit of 10000 words, we had to delete a lot of content to extremely compress the reply.\n\n# A1\n**On CS3 and DGF design choices.**\nWe agree that clearer motivation is needed. CS3 captures multi scale temporal and structural patterns in neural signals, consistent with findings that multi band EEG features improve intent decoding [1]. DGF performs selective multimodal fusion through dual gating, which follows prior successes of gating and normalization strategies in multimodal learning [2,3,4].\n**Evidence:**\n\n1. In a 35 class editing type classification pre study, CS3 plus DGF achieved the best F1 0.285 and mAP 0.411.\n2. Replacing an MLP encoder with CS3 alone raised F1 from 24.9 percent to 28.1 percent.\n3. DGF beat simple concatenation by 1.2 mAP points and cross attention by 2.2 mAP points, which is important under low SNR neural signals.\n4. In downstream editing, adding CS3 plus DGF improved CLIP I to 63.19 percent while keeping CLIP T comparable to a simple MLP plus concatenation baseline.\n   We will integrate justification, citations, and ablations into the revised paper.\n\n\n[1] Altaheri, H., Muhammad, G., & Alsulaiman, M. (2022). Physics-informed attention temporal convolutional network for EEG-based motor imagery classification. IEEE transactions on industrial informatics, 19(2), 2249-2258.\n\n[2] Choi, Y., Uh, Y., Yoo, J., & Ha, J. W. (2020). Stargan v2: Diverse image synthesis for multiple domains. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8188-8197).\n\n[3] Yin, D., Ren, X., Luo, C., Wang, Y., Xiong, Z., & Zeng, W. Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph. In International Conference on Learning Representations.\n\n[4] Rahman, W., Hasan, M. K., Lee, S., Zadeh, A., Mao, C., Morency, L. P., & Hoque, E. (2020, July). Integrating multimodal information in large pretrained transformers. In Proceedings of the conference. Association for computational linguistics. Meeting (Vol. 2020, p. 2359).\n\n---\n\n# A2\n**On pairing EEG plus PPG with T5, and fNIRS plus Motion with CLIP.**\nRationale is not arbitrary. T5 provides fine grained token level semantics that help precise instruction following, which complements the fast neural dynamics in EEG and the lightweight hemodynamics from PPG. CLIP provides robust global semantics that align with slower cortex wide fNIRS signals and intentional head Motion.\n\n**Evidence from the same classification pre study.**\n\n1. EEG plus PPG with T5 outperformed the CLIP variant by 3.0 F1 points and 3.6 mAP points.\n2. fNIRS plus Motion with CLIP outperformed the T5 variant by 5.5 F1 points and 5.2 mAP points.\n3. In editing, the aligned pairing yielded higher CLIP T 25.88 percent than a heterogeneous swap 24.96 percent.\n   We will add this rationale and the supporting numbers.\n\n---\n\n# A3\n**On the DiT backbone and conditioning.**\nBackbone is FLUX.1 dev, a pretrained diffusion transformer with about 860 million parameters at 512 by 512 resolution. We integrate CS3 encoders and DGF to produce conditioning vectors that enter the DiT through cross attention, analogous to text prompts. Editing uses latent inversion of the input image and then applies neural or text conditioning during denoising. We will provide exact architecture blocks, parameter counts, and a conditioning diagram.\n\n---\n\n# A4\n**On the missing citation for near text pretraining.**\nWe will add citations and clarify a two phase modular pretraining. Phase one pretrains CS3 encoders per modality using public datasets such as Thinking Out Loud for EEG \\[1] and an open fNIRS resource \\[2], combined with our internal corpora that are disjoint from evaluation. Phase two aligns paired encoders such as EEG plus PPG and fNIRS plus Motion to instruction embeddings with a symmetric NT Xent objective. We will list dataset names, sizes, and disjoint splits in the appendix.\n\n[1] Nieto, N., Peterson, V., Rufiner, H. L., Kamienkowski, J. E., & Spies, R. (2022). Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition. Scientific data, 9(1), 52.\n[2] Ning, M., Duwadi, S., Yücel, M. A., Von Lühmann, A., Boas, D. A., & Sen, K. (2024). fNIRS dataset during complex scene analysis. Frontiers in Human Neuroscience, 18, 1329086.\n\n---\n\n# A5\n**On pretraining and fine tuning details and stability.**\nPretraining used about 12 thousand steps across 2 epochs with LoRA based adaptation. Fine tuning used about 8 thousand steps with a conservative schedule and optional layer freezing. Early stopping triggered at about 19 thousand total steps. Loss dropped quickly in the first 2 thousand steps, showed two small transients at about steps 3,001 and 4,001, then stabilized without divergence. Full configs and loss curves will be added to the appendix.\n\n---\n\n# A6\n**On flow versus diffusion objective and CFG details.**\nWe clarify that Eq. 10 describes a diffusion objective, not a flow objective. The model uses the DDPM loss adapted to a DiT backbone. Inference uses classifier free guidance with scale w = 3.5. Null conditioning is applied by zeroing modality embeddings: text only uses an empty string, neural only passes zero vectors from the CS3 encoder, and multimodal zeros both branches. This design remains fully compatible with standard CFG pipelines. We will update the text and include concise schematics of the conditioning path for reproducibility.\n\n---\n\n# A7\n**On Fig. 6 readability.**\nWe will revise the layout to show category groups and sub types more clearly, add labels, and if needed split into two subfigures. A short how to read caption will accompany the figure.\n\n---\n\n# A8\n\n**On metric choice and addition of LPIPS.**\nWe acknowledge that L1 and L2 do not capture perceptual similarity perfectly, yet they remain common in editing literature for spatial fidelity, for example Emu Edit and In Context Edit. We now also report LPIPS for perceptual quality. LoongX Neural Signals: LPIPS 0.1427. LoongX Signals plus Speech: LPIPS 0.1330. The updated table and confidence intervals will be included with references to LPIPS and recent editing works.\n\n---\n\n# A9\n\n**On reporting trade off curves rather than single numbers.**\nWe added an interpolation analysis following Imagic and inspired by InstructPix2Pix and Imagic [1]. Let e\\_ori be the reconstruction anchor and e\\_tgt be the instruction anchor extracted from neural plus speech. Conditioning is e\\_bar equals eta times e\\_tgt plus one minus eta times e\\_ori with eta in \\[0, 1]. Over 150 held out samples, eta about 0.75 gives the best alignment fidelity balance. We will include CLIP Text, CLIP Image, and LPIPS curves and the summary table in the supplement.\n\n| η Value | CLIP-Text Similarity (↑) | CLIP-Image Similarity (↑) | LPIPS (↓) |\n|---------|--------------------------|---------------------------|-----------|\n| 0.00    | 0.2306                   | 0.6819                    | 0.2056    |\n| 0.25    | 0.2481                   | 0.6530                    | 0.2082    |\n| 0.50    | 0.2523                   | 0.6450                    | 0.2111    |\n| 0.75    | 0.2565               | 0.6380                    | 0.2230    |\n| 1.00    | 0.2588                   | 0.6374                    | 0.2383    |\n\n[1] Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., ... & Irani, M. (2023). Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6007-6017).\n\n---\n\n# A10\n**On what DINO measures.**\nDINO features capture fine grained structural similarity that correlates with perceived preservation of identity, pose, and local geometry. This complements CLIP Image which emphasizes global semantics. In our ablations, higher DINO aligned better with perceived success on shape and motion sensitive edits. We will add a brief definition and a pointer to recent evidence on DINO guided structure preservation.\n\n---\n\n# A11\n\n\n**On OmniControl as the only text baseline and fairness of comparisons.**\nWe chose OmniControl for its adaptable multi conditional design and favorable speed quality trade off. To strengthen fairness, we now include ICEdit, Bagel, and Flux Kontext on the same data and also report LoongX integrated with these backbones. As seen in the A2\\A3 Table of responsing Reviewer ZTwA, LoongX on Flux Kontext with neural signals plus speech reaches L1 0.1905, L2 0.0804, CLIP I 0.8078, CLIP T 0.273. We also report sizes and training FLOPs. OmniControl based LoongX uses about 12.5B parameters and about 3.04 times 10 to the 9 training FLOPs. Bagel based LoongX uses about 7.8B. Flux Kontext is about 12.6B. Gains come mainly from multimodal conditioning rather than brute force scaling. Full tables and configs will be added.\n\n---\n\n# A12\n\n**On adding more textual editing baselines.**\nWe agree. We have added recent state of the art text editing methods and have also provided LoongX results when integrated into these SOTA backbones for a fair comparison.\n\n---\n\n# A13\n\n\n**On demonstrating hands free image editing in practice.**\nOur live protocol mirrors data collection. The main differences are buffering latency and attention variability, which we mitigate using temporal smoothing of neural embeddings and a lightweight confidence filter that drops weak signals. A real time demo is under finalization and will be released with the camera ready. In cross subject tests with 5 new users, live success reached about 92 percent of intra subject performance, which indicates strong transfer.\n\n---\n\n# A14\n\n**On missing related work DreamConnect.**\nWe will add DreamConnect to related work and clarify differences in sensing. DreamConnect uses fMRI, which is less accessible for everyday interaction. Our focus is on practical EEG and fNIRS. The conceptual link on neurosemantic alignment is valuable, and we plan to explore similar alignment analyses in future work."}]

Author rebuttal:

Meta-review: This paper introduces LoongX, an image editing system conditioned on neural and speech signals. The authors collect a multimodal dataset (EEG, fNIRS, PPG, motion, speech) and show that these signals can guide editing, extending beyond text prompts. Ablations reveal contributions of different brain regions.

The rebuttal addressed concerns about metrics and experiment details with additional evidence. While some design choices could be refined, the contribution is clear and novel. This is a valuable step toward multimodal human–AI interaction for editing. Therefore, the paper is recommended for acceptance.
Decision: Accept (poster)