title: Seeing through the Brain: Image Reconstruction of Visual Perception from
    Human Brain Signals
summary: Seeing is believing, however, the underlying mechanism of how human visual
  perceptions are intertwined with our cognitions is still a mystery. Thanks to
  the recent advances in both neuroscience and artificial intelligence, we have
  been able to record the visually evoked brain activities and mimic the visual
  perception ability through computational approaches. In this paper, we pay
  attention to visual stimuli reconstruction by reconstructing the observed
  images based on portably accessible brain signals, i.e., electroencephalography
  (EEG) data. Since EEG signals are dynamic in the time-series format and are
  notorious to be noisy, processing and extracting useful information requires
  more dedicated efforts; In this paper, we propose a comprehensive pipeline,
  named NeuroImagen, for reconstructing visual stimuli images from EEG signals.
  Specifically, we incorporate a novel multi-level perceptual information
  decoding to draw multi-grained outputs from the given EEG data. A latent
  diffusion model will then leverage the extracted information to reconstruct the
  high-resolution visual stimuli images. The experimental results have
  illustrated the effectiveness of image reconstruction and superior quantitative
  performance of our proposed method.
paper_authors._id: 64d1d53b44d373d7067453ac
paper_authors.name: Yu-Ting Lan
paper_authors._id: 64d1d53b44d373d7067453ad
paper_authors.name: Kan Ren
paper_authors._id: 64d1d53b44d373d7067453ae
paper_authors.name: Yansen Wang
paper_authors._id: 64d1d53b44d373d7067453af
paper_authors.name: Wei-Long Zheng
paper_authors._id: 64d1d53b44d373d7067453b0
paper_authors.name: Dongsheng Li
paper_authors._id: 64d1d53b44d373d7067453b1
paper_authors.name: Bao-Liang Lu
paper_authors._id: 64d1d53b44d373d7067453b2
paper_authors.name: Lili Qiu
paper_title: Seeing through the Brain: Image Reconstruction of Visual Perception from
    Human Brain Signals
paper_summary: Seeing is believing, however, the underlying mechanism of how human visual
  perceptions are intertwined with our cognitions is still a mystery. Thanks to
  the recent advances in both neuroscience and artificial intelligence, we have
  been able to record the visually evoked brain activities and mimic the visual
  perception ability through computational approaches. In this paper, we pay
  attention to visual stimuli reconstruction by reconstructing the observed
  images based on portably accessible brain signals, i.e., electroencephalography
  (EEG) data. Since EEG signals are dynamic in the time-series format and are
  notorious to be noisy, processing and extracting useful information requires
  more dedicated efforts; In this paper, we propose a comprehensive pipeline,
  named NeuroImagen, for reconstructing visual stimuli images from EEG signals.
  Specifically, we incorporate a novel multi-level perceptual information
  decoding to draw multi-grained outputs from the given EEG data. A latent
  diffusion model will then leverage the extracted information to reconstruct the
  high-resolution visual stimuli images. The experimental results have
  illustrated the effectiveness of image reconstruction and superior quantitative
  performance of our proposed method.
paper_ai_keywords: multi-level perceptual information decoding latent diffusion model high-resolution visual stimuli images