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arXiv · 2507.11522

CATVis: Context-Aware Thought Visualization

Tariq Mehmood·Hamza Ahmad·Muhammad Haroon Shakeel·Murtaza Taj
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Abstract

EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) caption refinement via re-ranking, (4) weighted interpolation of concept and caption embeddings for richer semantics, and (5) image generation using a pre-trained Stable Diffusion model. We enable context-aware EEG-to-image generation through cross-modal alignment and re-ranking. Experimental results demonstrate that our method generates high-quality images aligned with visual stimuli, outperforming SOTA approaches by 13.43% in Classification Accuracy, 15.21% in Generation Accuracy and reducing Fr\'echet Inception Distance by 36.61%, indicating superior semantic alignment and image quality.

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Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj. 2025-07-15. CATVis: Context-Aware Thought Visualization. https://arxiv.org/abs/2507.11522

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