NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties

• URL: http://arxiv.org/abs/2402.01590v2
• Date: Sun, 12 May 2024 11:57:19 GMT
• Title: NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties
• Authors: Jingyuan Sun, Mingxiao Li, Zijiao Chen, Marie-Francine Moens,
• Abstract summary: We introduce NeuroCine, a novel dual-phase framework to targeting the inherent challenges of decoding fMRI data. tested on a publicly available fMRI dataset, our method shows promising results. Our attention analysis suggests that the model aligns with existing brain structures and functions, indicating its biological plausibility and interpretability.
• Score: 23.893490180665996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the pursuit to understand the intricacies of human brain's visual processing, reconstructing dynamic visual experiences from brain activities emerges as a challenging yet fascinating endeavor. While recent advancements have achieved success in reconstructing static images from non-invasive brain recordings, the domain of translating continuous brain activities into video format remains underexplored. In this work, we introduce NeuroCine, a novel dual-phase framework to targeting the inherent challenges of decoding fMRI data, such as noises, spatial redundancy and temporal lags. This framework proposes spatial masking and temporal interpolation-based augmentation for contrastive learning fMRI representations and a diffusion model enhanced by dependent prior noise for video generation. Tested on a publicly available fMRI dataset, our method shows promising results, outperforming the previous state-of-the-art models by a notable margin of ${20.97\%}$, ${31.00\%}$ and ${12.30\%}$ respectively on decoding the brain activities of three subjects in the fMRI dataset, as measured by SSIM. Additionally, our attention analysis suggests that the model aligns with existing brain structures and functions, indicating its biological plausibility and interpretability.

Related papers

• BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals [11.030708270737964]
  We propose Brain Masked Auto-Encoder (BrainMAE) for learning representations directly from fMRI time-series data. BrainMAE consistently outperforms established baseline methods by significant margins in four distinct downstream tasks.
  arXiv  Detail & Related papers  (2024-06-24T19:16:24Z)
• 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)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity [13.291585611137355]
  Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. This paper propose a two-stage model named Mind-Animator, which achieves state-of-the-art performance on three public datasets. We substantiate that the reconstructed video dynamics are indeed derived from fMRI, rather than hallucinations of the generative model.
  arXiv  Detail & Related papers  (2024-05-06T08:56:41Z)
• BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
  We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals. By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
  arXiv  Detail & Related papers  (2024-04-30T10:53:30Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• 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)
• 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)
• 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)
• 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)

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.