Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing

2026-07-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Life Sciences & Biology · Depth: Expert, extended

Summary

A novel fMRI decoding framework, "Decoding the Multimodal Mind," enables coherent language reconstruction from brain recordings elicited by diverse input modalities: visual, auditory, and textual. This unified approach utilizes visual-language models (VLMs) and a modular architecture with modality-specific "experts" to jointly interpret information across modalities. The framework incorporates prompt tuning to learn distinct soft prompts for each stimulus, facilitating alignment between brain signals and semantic spaces. Experiments conducted on the NSD, Pereira, and Huth datasets demonstrate that the "Ours Dual-Modal" model achieves competitive performance against state-of-the-art systems, notably scoring the highest METEOR (19.48) and competitive BLEU4 (14.87) and CIDEr (50.28) scores. This method advances toward more ecologically valid and generalizable mind decoding by supporting flexible stimulus types.

Key takeaway

For AI Scientists and Machine Learning Engineers developing brain-computer interfaces, this multimodal fMRI decoding framework offers a path to more robust and generalizable language reconstruction. You should consider integrating diverse visual, auditory, and textual stimuli into your BCI designs. This approach, utilizing VLMs and prompt tuning, improves decoding accuracy and adaptability, moving closer to ecologically valid mind decoding applications.

Key insights

The framework reconstructs language from fMRI using multimodal inputs, VLMs, and adaptive routing for generalizable brain-to-text translation.

Principles

• Human thought is inherently multimodal.
• Flexible decoding requires diverse stimuli.
• Modality-specific experts enhance interpretation.

Method

The framework uses a Dual-Modality Projector with a modality router and two experts ($E_T$, $E_I$) to combine visual and textual fMRI features. It employs prompt tuning and a Qwen2-VL LLM for text generation.

In practice

• Integrate multimodal stimuli for richer neural data.
• Use prompt tuning for efficient fMRI-to-semantic alignment.
• Employ modular VLM architectures for extensibility.

Topics

• fMRI Decoding
• Brain-Computer Interface
• Multimodal AI
• Visual-Language Models
• Prompt Tuning
• Language Reconstruction

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.