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A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks.

Wei Huang1, Hengjiang Li1, Fan Qin1

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Brain-Computer Interface & Brain-Inspired Intelligence, Key Laboratory of Sichuan Province, Chengdu, P. R. China.

International Journal of Neural Systems
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM) for advanced brain-computer interfaces. The model achieves high accuracy in decoding visual information across subjects, improving neural decoding efficiency.

Keywords:
Visual decodingfunctional magnetic resonance imaginggenerative language model

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Current visual decoding models are subject- and task-specific, increasing costs and resource demands.
  • Developing generalized brain-computer interfaces requires more efficient and adaptable decoding methods.

Purpose of the Study:

  • To introduce a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM) for cross-subject and cross-task visual decoding.
  • To enhance neural decoding by integrating whole-brain activity and improving generalization.

Main Methods:

  • Developed a PG-GVLDM utilizing prompt text for subject and task information.
  • Incorporated a multi-head cross-attention module and whole-brain response activities.
  • Tested on the Natural Scenes Dataset (NSD).

Main Results:

  • Achieved 66.6% average category decoding accuracy across subjects, demonstrating strong generalization.
  • Reached state-of-the-art text decoding scores (METEOR, Sentence-Transformer, ROUGE-1, ROUGE-L).
  • Whole-brain activity integration significantly improved decoding performance.

Conclusions:

  • PG-GVLDM offers a breakthrough in visual neural decoding, enabling unified decoding across subjects and tasks.
  • The model provides a foundation for developing generalized brain-computer interfaces.
  • Integrating global semantic information from whole-brain activity is crucial for advanced neural decoding.