EEG-to-Image Reconstruction

Latest papers

8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

E2I-Flow: EEG-to-Image Reconstruction via Wavelet-Temporal Cross-Attention and Conditional Flow Matching
Nov 3, 2026Haowei Pan, Hwa Hui Tew, Junn Yong Loo +3EEG-to-Image Reconstruction EEG-Based Visual Decoding
School of Information Technology, Monash University, Malaysia Campus · School of Electrical and Electronic Engineering, Nanyang Technological University · Statistics Program, King Abdullah University of Science and Technology
Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks
Sep 13, 2026Harshit GoyalEEG-to-Image Reconstruction EEG-Based Visual Decoding
BITS Pilani, India
Lost in Visual Translation: A VLM-Assisted Perceptual-Semantic Coherence Framework for EEG-to-Image Reconstruction
Jul 14, 2026Sukriti Tiwari, BHVSP Subrahmanyam, Nidhi Goyal +1VLM Evaluation EEG-Based Visual Decoding
Department of Computer Science and Engineering, Mahindra University, Hyderabad, India · MU-VT Interdisciplinary Advanced Research Centre for Transformative Technologies, Mahindra University, Hyderabad, India · Avyakt Ehsaas
EMAG: Differentiable 4D Gaussian Mixture Splatting for EEG Spatial Super-Resolution
May 28, 2026Alex Lazarovich, Ofir Itzhak Shahar, Gur Elkin +1Electroencephalography Gaussian Mixture Models
Stein Faculty of Computer and Information Science Ben-Gurion University of the Negev, Israel
Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
Apr 24, 2026Yongxiang Lian, Yueyang Cang, Pingge Hu +2Diffusion Model Guidance EEG-Based Visual Decoding
Department of Automation Tsinghua University Haidian District, 100084, Beijing, China · China Academy of Information and Communications Technology Haidian District, 100084, Beijing, China
EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
Apr 9, 2026Emanuele Balloni, Emanuele Frontoni, Chiara Matti +3EEG-to-Image Reconstruction EEG-Based Visual Decoding
Department of Civil Engineering Building and Architecture (DICEA), Universit`a Politecnica delle Marche, Via Brecce Bianche 12, Ancona, 60131, Italy · Department of Political Sciences, Communication and International Relations, University of Macerata, Via Don Minzoni 22A, Macerata, 62100, Italy · Horizon Intelligence Labs +1
Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding
Mar 20, 2026Zhijian Gong, Tianren Yao, Wenjia Dong +1EEG-to-Image Reconstruction Image Reconstruction
Beijing University of Technology, Beijing · Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing
CognitionCapturerPro: Towards High-Fidelity Visual Decoding from EEG/MEG via Multi-modal Information and Asymmetric Alignment
Mar 13, 2026Kaifan Zhang, Lihuo He, Junjie Ke +4EEG-to-Image Reconstruction EEG-Based Visual Decoding
Visual Information Processing Laboratory, School of Electronic Engineering, Xidian University, Xi’an 710071, China. · School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China

Nov 3, 2026J/K move · Enter open · S save

E2I-Flow: EEG-to-Image Reconstruction via Wavelet-Temporal Cross-Attention and Conditional Flow Matching

Haowei Pan, Hwa Hui Tew, Junn Yong Loo, Fuad Noman+2

School of Information Technology, Monash University, Malaysia Campus · School of Electrical and Electronic Engineering, Nanyang Technological University · Statistics Program, King Abdullah University of Science and Technology

EEG-to-Image Reconstruction EEG-Based Visual Decoding Temporal Representation Learning

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Electroencephalography (EEG)-based visual decoding and reconstruction aims to recover visual information from non-invasive brain signals. But there are challenges due to the complex temporal-frequency characteristics of EEG signals and the difficulty of discrete denoising framework in mapping neural activity to meaningful visual representations. Here, we propose E2I-Flow, an EEG-to-image decoding and reconstruction framework that combines a novel EEG encoder for time-frequency representation learning from EEG features, and conditional flow matching for visual decoding. A stationary-wavelet frequency branch and a temporal-mixing branch are integrated through bidirectional cross-attention and adaptive fusion, with the resulting EEG representations aligned to ground-truth image embeddings using contrastive and mean-squared-error loss supervisions. The aligned EEG representation is then used as a condition for a Transformer-based flow-matching model to predict the corresponding visual embedding for image synthesis. Experimental results show strong zero-shot EEG-to-image retrieval and improved visual reconstruction performance, outperforming several state-of-the-art baselines.