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DreamDiffusion: Generating High-Quality Images from Brain EEG Signals (2306.16934v2)

Published 29 Jun 2023 in cs.CV

Abstract: This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, and image embeddings with limited EEG-image pairs. Overall, the proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences, and achieves promising results. Quantitative and qualitative results demonstrate the effectiveness of the proposed method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision. The code is available here \url{https://github.com/bbaaii/DreamDiffusion}.

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Citations (41)
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Summary

  • The paper introduces DreamDiffusion, a novel method that directly synthesizes high-quality images from noisy EEG signals using a diffusion model framework.
  • It employs a robust methodology combining EEG pre-training with temporal masked signal modeling and fine-tuning via CLIP-aligned cross-attention mechanisms.
  • Experiments show that DreamDiffusion outperforms previous methods by producing semantically accurate images, paving the way for innovative brain-computer interface applications.

DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

The paper presents a novel approach—DreamDiffusion—for generating images directly from brain electroencephalogram (EEG) signals using diffusion models. This method represents a progression from text-based image synthesis, leveraging brain activity data without intermediating text translation, thus enabling a more direct form of human-computer interaction.

Core Methodology

DreamDiffusion utilizes pre-trained text-to-image diffusion models, specifically the Stable Diffusion architecture, to synthesize high-quality images from EEG signals. The authors address two main challenges: the inherent noise and variability of EEG signals, and the need to align EEG-derived embeddings with pre-trained text and image embeddings.

  1. EEG Pre-training: The approach incorporates a large-scale pre-training task to enhance EEG signal representations. Temporal masked signal modeling enables the prediction of missing tokens in the time domain, allowing the extraction of robust EEG features from noisy data across diverse subjects.
  2. Stable Diffusion Fine-tuning: The pre-trained EEG encoder is fine-tuned using a limited set of EEG-image pairs. Adjustments focus on cross-attention mechanisms within the Stable Diffusion framework, adapting it for EEG-based conditions.
  3. CLIP Alignment: To ensure the alignment of EEG, text, and image embeddings, the method employs supervision through CLIP’s image encoder. This alignment is critical for achieving coherence between EEG signals and the pre-trained model's latent spaces.

Results and Implications

The experimental outcomes, validated with a classification task, highlight DreamDiffusion’s capability to produce semantically accurate images from EEG data. The method surpasses previous attempts, such as Brain2Image, in terms of image quality and semantic alignment. Ablation studies underlined the importance of pre-training and CLIP alignment in enhancing the model's overall performance.

Practical and Theoretical Implications

The method opens avenues for practical applications in areas requiring direct brain-interface interactions, such as artistic creation, dream visualization, and potentially aiding individuals with communication barriers. Theoretically, the approach advances understanding of brain-computer interfaces in generating coherent and contextually relevant visual data.

Future Directions

Future research may focus on refining EEG signal interpretation to enable more detailed and varied image outputs. Further exploration could involve adaptive models that personalize image generation based on individual neural patterns, potentially expanding the applicability of EEG-based image synthesis.

Overall, DreamDiffusion marks a promising convergence of neuroscience and AI, illustrating the potential of leveraging brain activity for creative and practical applications. The integration of EEG with diffusion models suggests new directions for research at the intersection of cognitive science and computer vision.

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