ArticleNature communications2026
Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Img2EEG: A Scalable and Interpretable Encoding Framework for Simulating Human EEG Responses to Visual Inputs.bioRxiv : the preprint server for biology · 2026Article
- Uncovering Heart Rate Response Patterns to Threat Pictures Through Deep Latent Representation Learning with a Variational Autoencoder.Psychophysiology · 2026Article
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7 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.