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<p>Evolving methods rooted in artificial intelligence (AI) offer new opportunities for linking human behavior and experience to brain function. Here, we introduce the Concept2Brain model, a deep network designed to generate synthetic electrophysiological responses to semantic/emotional information conveyed through pictures or text. Leveraging AI solutions like CLIP from OpenAI, the model generates a representation of a stimulus and maps it into an electrophysiological latent space. We demonstrate that this openly available resource generates synthetic neural responses that closely resemble those observed empirically. The Concept2Brain model is provided as a web service tool for creating open and reproducible EEG datasets by predicting brain responses to any semantic concept or picture. Beyond its practical applications, it also paves the way for AI-driven brain activity modeling, offering new possibilities for studying how the brain represents the world.</p>

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Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

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  • Alejandro Santos-Mayo,
  • Faith Gilbert,
  • Arash Mirifar,
  • Anna-Lena Tebbe,
  • Ruogu Fang,
  • Mingzhou Ding,
  • Andreas Keil

摘要

Evolving methods rooted in artificial intelligence (AI) offer new opportunities for linking human behavior and experience to brain function. Here, we introduce the Concept2Brain model, a deep network designed to generate synthetic electrophysiological responses to semantic/emotional information conveyed through pictures or text. Leveraging AI solutions like CLIP from OpenAI, the model generates a representation of a stimulus and maps it into an electrophysiological latent space. We demonstrate that this openly available resource generates synthetic neural responses that closely resemble those observed empirically. The Concept2Brain model is provided as a web service tool for creating open and reproducible EEG datasets by predicting brain responses to any semantic concept or picture. Beyond its practical applications, it also paves the way for AI-driven brain activity modeling, offering new possibilities for studying how the brain represents the world.