ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection

• URL: http://arxiv.org/abs/2604.26218v1
• Date: Wed, 29 Apr 2026 01:53:46 GMT
• Title: ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection
• Abstract summary: ViBE is a novel encoding framework for generating magnetoencephalography (MEG) and electroencephalography (EEG) signals from visual stimuli.<n>We propose a convolutional variational autoencoder (TS-CVAE) that captures thetemporal characteristics of M/EEG signals for effective neural response reconstruction.
• Score: 36.96202443284929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward visual prostheses that restore vision for patients with severe vision disorders. Brain encoding involves two fundamental steps: achieving faithful reconstruction of neural responses and establishing cross-modal alignment between visual stimuli and neural responses. To this end, we propose ViBE, a novel brain encoding framework for generating magnetoencephalography (MEG) and electroencephalography (EEG) signals from visual stimuli. Specifically, we first design a spatio-temporal convolutional variational autoencoder (TSC-VAE) that captures the spatio-temporal characteristics of M/EEG signals for effective neural response reconstruction. To bridge the modality gap between visual features and neural representations, we employ Q-Former to map CLIP image embeddings to the TSC-VAE latent space, producing neural proxy embeddings. For comprehensive cross-modal alignment, we combine mean squared error (MSE) loss for point-wise feature matching with sliced Wasserstein distance (SWD) for probability distribution alignment between the neural proxy embeddings and TSC-VAE latent embeddings. We conduct extensive experiments on the THINGS-EEG2 and THINGS-MEG datasets, demonstrating the effectiveness of our approach in generating high-quality M/EEG signals from visual stimuli.

Related papers

• NeuroCLIP: Brain-Inspired Prompt Tuning for EEG-to-Image Multimodal Contrastive Learning [13.254096454986318]
  We present NeuroCLIP, a prompt tuning framework tailored for EEG-to-image contrastive learning.<n>We are the first to introduce visual prompt tokens into EEG-image alignment, acting as global, modality-level prompts.<n>On the THINGS-EEG2 dataset, NeuroCLIP achieves a Top-1 accuracy of 63.2% in zero-shot image retrieval.
  arXiv  Detail & Related papers  (2025-11-12T12:13:24Z)
• WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities [55.00677513249723]
  EEG signals simultaneously encode both cognitive processes and intrinsic neural states.<n>We map EEG signals and their corresponding modalities into a unified semantic space to achieve generalized interpretation.<n>The resulting model demonstrates robust classification accuracy while supporting flexible, open-ended conversations.
  arXiv  Detail & Related papers  (2025-09-26T06:21:51Z)
• Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models [6.761875482596085]
  We present the first image-to-brain signal framework that generates M/EEG from images.<n>The proposed framework comprises two key components: a pretrained CLIP visual encoder and a cross-attention enhanced U-Net diffusion model.<n>Unlike conventional generative models that rely on simple concatenation for conditioning, our cross-attention modules capture the complex interplay between visual features and brain signal representations.
  arXiv  Detail & Related papers  (2025-08-31T10:29:58Z)
• SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning [54.390403684665834]
  Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience.<n>We propose SynBrain, a generative framework that simulates the transformation from visual semantics to neural responses in a probabilistic and biologically interpretable manner.<n> Experimental results demonstrate that SynBrain surpasses state-of-the-art methods in subject-specific visual-to-fMRI encoding performance.
  arXiv  Detail & Related papers  (2025-08-14T03:01:05Z)
• Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding [2.587640069216139]
  Decoding neural visual representations from electroencephalogram (EEG)-based brain activity is crucial for advancing brain-machine interfaces (BMI)<n>Existing methods often overlook semantic consistency and completeness within modalities and lack effective semantic alignment across modalities.<n>We propose Neural-MCRL, a novel framework that achieves multimodal alignment through semantic bridging and cross-attention mechanisms.
  arXiv  Detail & Related papers  (2024-12-23T07:02:44Z)
• MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding [50.55024115943266]
  We introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities [31.448924808940284]
  We introduce a two-phase fMRI representation learning framework. The first phase pre-trains an fMRI feature learner with a proposed Double-contrastive Mask Auto-encoder to learn denoised representations. The second phase tunes the feature learner to attend to neural activation patterns most informative for visual reconstruction with guidance from an image auto-encoder.
  arXiv  Detail & Related papers  (2023-05-26T19:16:23Z)
• Controllable Mind Visual Diffusion Model [58.83896307930354]
  Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. We propose a novel approach, referred to as Controllable Mind Visual Model Diffusion (CMVDM) CMVDM extracts semantic and silhouette information from fMRI data using attribute alignment and assistant networks. We then leverage a control model to fully exploit the extracted information for image synthesis, resulting in generated images that closely resemble the visual stimuli in terms of semantics and silhouette.
  arXiv  Detail & Related papers  (2023-05-17T11:36:40Z)
• Joint fMRI Decoding and Encoding with Latent Embedding Alignment [77.66508125297754]
  We introduce a unified framework that addresses both fMRI decoding and encoding. Our model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework.
  arXiv  Detail & Related papers  (2023-03-26T14:14:58Z)
• BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding [51.911473457195555]
  BrainCLIP is a task-agnostic fMRI-based brain decoding model. It bridges the modality gap between brain activity, image, and text. BrainCLIP can reconstruct visual stimuli with high semantic fidelity.
  arXiv  Detail & Related papers  (2023-02-25T03:28:54Z)
• Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses [68.96380145211093]
  Cortical prostheses are devices implanted in the visual cortex that attempt to restore lost vision by electrically stimulating neurons. Currently, the vision provided by these devices is limited, and accurately predicting the visual percepts resulting from stimulation is an open challenge. We propose to address this challenge by utilizing 'brain-like' convolutional neural networks (CNNs), which have emerged as promising models of the visual system.
  arXiv  Detail & Related papers  (2022-09-27T17:33:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.