AI model for predicting neurophysiological responses to text and pictures

The report introduces Concept2Brain, a deep neural network architecture that generates synthetic EEG signals from pictures or text, trained on EEG data from 88 observers viewing 360 naturalistic photographs and validated via 10-fold cross-validation. Synthetic signals reproduced empirical ERP components, time-frequency patterns, and participant-level differences, and out-of-sample checks against THINGS MEG/fMRI and ERP-CORE data showed expected correspondences. The model is available as an open cloud platform, and the authors note that text-based outputs should be treated with heightened caution because the architecture was trained on picture-evoked signals.