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Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals (2308.02510v2)

Published 27 Jul 2023 in eess.IV, cs.AI, cs.CV, cs.MM, and q-bio.NC

Abstract: 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.

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Authors (7)
  1. Yu-Ting Lan (3 papers)
  2. Kan Ren (41 papers)
  3. Yansen Wang (21 papers)
  4. Wei-Long Zheng (14 papers)
  5. Dongsheng Li (240 papers)
  6. Bao-Liang Lu (26 papers)
  7. Lili Qiu (50 papers)
Citations (14)

Summary

  • The paper introduces NeuroImagen, a framework that efficiently reconstructs visual stimuli from noisy EEG data using a dual-layer semantic decoding approach.
  • It employs both pixel-level saliency mapping and sample-level semantic extraction via models like CLIP and BLIP, achieving high structural similarity and semantic accuracy.
  • Experimental results demonstrate superior performance with improved Inception Score and SSIM, highlighting its potential for cognitive neuroscience applications.

Image Reconstruction from EEG: A Focus on NeuroImagen

The paper "Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals" presents significant advancements in understanding the linkage between human visual perceptions and electroencephalography (EEG) signals. The authors propose NeuroImagen, a sophisticated framework designed to reconstruct images that have evoked visual stimuli in human subjects by analyzing EEG data. The methodology inclines heavily on recent developments in neuroscience and AI, aiming to bridge our understanding of visual perception through computational means.

NeuroImagen introduces a multi-level semantic extraction pipeline that processes the EEG signals extensively. These signals, typically characterized by noise and being dynamic time-series data, present significant challenges for extracting meaningful visual information. The authors address this by initiating a dual-layer semantic decoding approach. Firstly, the pixel-level saliency map extraction method is deployed, capturing coarse visual features such as color, shape, and position from the EEG data. Despite the inherent noise, this step was crucial in predicting rough structural information regarding the stimuli.

Subsequent to pixel-level processing, NeuroImagen employs a sample-level semantic extraction phase, which leverages text-based information extracted from neural network models such as CLIP and BLIP. This layer serves to encapsulate the broader contextual and categorical essence of the stimuli, thus complementing the pixel-level details. Integration of this two-tier information is pivotal as it allows for a finer control over reconstructing the images.

Central to the reconstruction process is a latent diffusion model orchestrated for synergizing extracted semantics into high-resolution image outputs. Notably, this process does not require full end-to-end fine-tuning of the diffusion models, which presents a scalable and efficient solution.

Experimental evaluations on the EEG-image dataset reveal that NeuroImagen outperforms existing methods, delivering reconstructions that bear a high semantic resemblance to the original stimuli. Metrics such as Inception Score (IS) and Structural Similarity Index Measure (SSIM) underscore its capability to reconstruct high-fidelity images.

The implications of this paper are multi-faceted. Practically, it pushes forward the feasibility of non-invasive brain signal decoding to reconstruct visual perceptions accurately. Theoretically, it fuels the understanding of cognitive processes via EEG, expanding potential applications across virtual reality enhancements, cognitive neuroscience research, and even brain-computer interfaces. The proposed framework may offer pathways towards developing robust, interpretable AI systems capable of elucidating the complex relationships between brain activities and cognitive perceptions.

Looking ahead, advancements in EEG signal processing and synergistic incorporation of other data modalities, possibly using more advanced neural encoding/decoding schemes or improved generative models, are avenues for exploration. As AI and neuroscience continue to intertwine more intimately, leveraging EEG for visual reconstruction carries the potential to unravel deeper cognitive mysteries and foster innovations across interdisciplinary research.

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