Unlike a brain-computer interface, artificial intelligence can be combined with human brain activity to reconstruct high-resolution images. How so? Researchers from the Graduate School of Frontier Biosciences at Osaka University used a diffusion model (DM) to decode fMRI data from human brains and reconstruct visual experiences, relying on a latent diffusion model (LDM) that many know as Stable Diffusion.
Training or fine-tuning of complex deep generative models is not required, as this simple framework reconstructs high-resolution images from functional Magnetic Resonance Imaging (fMRI) signals using Stable Diffusion. The text-to-image conversion process implemented by Stable Diffusion incorporates the semantic information expressed by the conditional text, while simultaneously retaining the appearance of the original image.
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We show that our proposed method can reconstruct high-resolution images with high fidelity in straightforward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs,” said the researchers.
