Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI

• URL: http://arxiv.org/abs/2404.05468v5
• Date: Tue, 28 May 2024 16:03:21 GMT
• Title: Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI
• Authors: Hugo Caselles-Dupré, Charles Mellerio, Paul Hérent, Alizée Lopez-Persem, Benoit Béranger, Mathieu Soularue, Pierre Fautrel, Gauthier Vernier, Matthieu Cord,
• Abstract summary: Reconstructing visual imagination presents a greater challenge, with potentially revolutionary applications. For the first time, we have compiled a substantial dataset (around 6h of scans) on visual imagery. We train a modified version of an fMRI-to-image model and demonstrate the feasibility of reconstructing images from two modes of imagination.
• Score: 36.181302575642306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reconstruction of images observed by subjects from fMRI data collected during visual stimuli has made strong progress in the past decade, thanks to the availability of extensive fMRI datasets and advancements in generative models for image generation. However, the application of visual reconstruction has remained limited. Reconstructing visual imagination presents a greater challenge, with potentially revolutionary applications ranging from aiding individuals with disabilities to verifying witness accounts in court. The primary hurdles in this field are the absence of data collection protocols for visual imagery and the lack of datasets on the subject. Traditionally, fMRI-to-image relies on data collected from subjects exposed to visual stimuli, which poses issues for generating visual imagery based on the difference of brain activity between visual stimulation and visual imagery. For the first time, we have compiled a substantial dataset (around 6h of scans) on visual imagery along with a proposed data collection protocol. We then train a modified version of an fMRI-to-image model and demonstrate the feasibility of reconstructing images from two modes of imagination: from memory and from pure imagination. The resulting pipeline we call Mind-to-Image marks a step towards creating a technology that allow direct reconstruction of visual imagery.

Related papers

• Brain-Streams: fMRI-to-Image Reconstruction with Multi-modal Guidance [3.74142789780782]
  We show how modern LDMs incorporate multi-modal guidance for structurally and semantically plausible image generations. Brain-Streams maps fMRI signals from brain regions to appropriate embeddings. We validate the reconstruction ability of Brain-Streams both quantitatively and qualitatively on a real fMRI dataset.
  arXiv  Detail & Related papers  (2024-09-18T16:19:57Z)
• Brain3D: Generating 3D Objects from fMRI [76.41771117405973]
  We design a novel 3D object representation learning method, Brain3D, that takes as input the fMRI data of a subject. We show that our model captures the distinct functionalities of each region of human vision system. Preliminary evaluations indicate that Brain3D can successfully identify the disordered brain regions in simulated scenarios.
  arXiv  Detail & Related papers  (2024-05-24T06:06:11Z)
• Decoding Realistic Images from Brain Activity with Contrastive Self-supervision and Latent Diffusion [29.335943994256052]
  Reconstructing visual stimuli from human brain activities provides a promising opportunity to advance our understanding of the brain's visual system. We propose a two-phase framework named Contrast and Diffuse (CnD) to decode realistic images from functional magnetic resonance imaging (fMRI) recordings.
  arXiv  Detail & Related papers  (2023-09-30T09:15:22Z)
• Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals [27.92796103924193]
  We propose a comprehensive pipeline, named NeuroImagen, for reconstructing visual stimuli images from EEG signals. We incorporate a novel multi-level perceptual information decoding to draw multi-grained outputs from the given EEG data.
  arXiv  Detail & Related papers  (2023-07-27T12:54:16Z)
• Brain Captioning: Decoding human brain activity into images and text [1.5486926490986461]
  We present an innovative method for decoding brain activity into meaningful images and captions. Our approach takes advantage of cutting-edge image captioning models and incorporates a unique image reconstruction pipeline. We evaluate our methods using quantitative metrics for both generated captions and images.
  arXiv  Detail & Related papers  (2023-05-19T09:57:19Z)
• 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)
• Mind Reader: Reconstructing complex images from brain activities [16.78619734818198]
  We focus on reconstructing the complex image stimuli from fMRI (functional magnetic resonance imaging) signals. Unlike previous works that reconstruct images with single objects or simple shapes, our work aims to reconstruct image stimuli rich in semantics. We find that incorporating an additional text modality is beneficial for the reconstruction problem compared to directly translating brain signals to images.
  arXiv  Detail & Related papers  (2022-09-30T06:32:46Z)
• 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.