Coherent Language Reconstruction from Brain Recordings with Flexible Multi-Modal Input Stimuli

• URL: http://arxiv.org/abs/2505.10356v1
• Date: Thu, 15 May 2025 14:46:45 GMT
• Title: Coherent Language Reconstruction from Brain Recordings with Flexible Multi-Modal Input Stimuli
• Authors: Chunyu Ye, Shaonan Wang,
• Abstract summary: Decoding thoughts from brain activity offers valuable insights into human cognition and enables promising applications in brain-computer interaction.<n>We propose a unified and flexible framework for reconstructing coherent language from brain recordings elicited by diverse input modalities.
• Score: 5.589479682782169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decoding thoughts from brain activity offers valuable insights into human cognition and enables promising applications in brain-computer interaction. While prior studies have explored language reconstruction from fMRI data, they are typically limited to single-modality inputs such as images or audio. In contrast, human thought is inherently multimodal. To bridge this gap, we propose a unified and flexible framework for reconstructing coherent language from brain recordings elicited by diverse input modalities-visual, auditory, and textual. Our approach leverages visual-language models (VLMs), using modality-specific experts to jointly interpret information across modalities. Experiments demonstrate that our method achieves performance comparable to state-of-the-art systems while remaining adaptable and extensible. This work advances toward more ecologically valid and generalizable mind decoding.

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