The system, called Brain-IT, uses data from functional magnetic resonance imaging (fMRI) to measure activity in the visual cortex, the part of the brain that processes visual input. A computer model then predicts both the broad layout of an image and high-level information about its content. It then passes those predictions to an AI image generator, researchers explained in a study uploaded to the preprint database arXiv. The research was presented at the ICLR 2026 conference, held April 23-25, and publicized by its authors in September.
"The key idea is that every voxel, a tiny cube of brain tissue, gets its own learned vector that captures what it cares about in images," said study first author Roman Beliy, a doctoral candidate in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science in Israel.
Traditional image-to-fMRI systems have generally been custom trained for each participant and dataset. The Universal Brain Encoder is more flexible. It learns from data from multiple people and can transfer information across participants, datasets or different MRI systems with varying magnetic-field strengths, according to the study.
The Universal Brain Encoder starts with an image and predicts brain activity; Brain-IT starts with measured brain activity and reconstructs the image the person saw. In other words,it asks "based on this brain activity, what was the subject looking at?". It sorts thousands of tiny brain-scan measurements from the visual cortex into 128 groups based on the kinds of visual information they tend to carry.
The decoder produces two types of visual predictions. One captures semantic information — the features associated with what is likely in a scene; for an image of a dog standing in grass, for instance, this would include things like the features of a dog and an outdoor, grassy setting. The other captures structural information, like the broad visual layout or how forms are arranged.
Although the new tool can recreate what people see, it can't reproduce memories or imagined scenes.
The original image of a baseball game (left) and the reconstruction (right) generated by the Brain-IT AI model based on the brain activity of a person viewing the image. (Image credit: Brain-IT)
How scientists tested the model
Brain-IT's synthetic images were compared with the images that participants saw. Brain-IT surpassed prior leading methods on standard objective reconstruction metrics and produced more faithful results, the study authors wrote. The system shined when limited fMRI data from a new participant was available, with just one hour of data from a new participant producing results as accurately as earlier approaches that had been trained on 40 hours of recordings.
In the final stage of processing, the system uses an off-the-shelf diffusion generator trained on massive numbers of images. Models like this generate plausible images and can smooth textures, sharpen facial features or fill in missing details.
Why this is not memory decoding
While the new system produces impressive results for seen images, it can't be used to reconstruct an imagined or remembered image.
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The new system could help brain researchers better understand which visual information is represented in different brain regions, how those regions interact and how individuals’ patterns differ. Such tools could eventually reveal differences in visual processing that could help clinicians spot neurological, neurodevelopmental and psychiatric conditions.
"There are numerous useful medical applications which we are currently striving toward," Irani said. "These include using our brain encoder/decoder to detect and analyze brain disorders such as MS, PTSD, autism and psychiatric disorders."
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