Japanese Scientists Create World’s First AI-Generated Images from Brain Activity

Japanese scientists have made a breakthrough by creating the world’s first images based on human brain activity using artificial intelligence (AI).

This new technology, called “brain decoding,” allows the visualization of what people perceive, such as objects and landscapes, from their brain signals.

A research team from the National Institutes for Quantum Science and Technology (QST) and other organizations worked on this project. They successfully generated rough images of a leopard, showing its mouth, ears, and spotted pattern. They also created images of other objects like an airplane with red lights on its wings.

The technology works by analyzing brain activity using functional magnetic resonance imaging (fMRI). During the study, participants viewed 1,200 different images of objects and landscapes.

Scientists measured their brain signals while they looked at these images. Then, they input these same images into AI to help it learn the relationship between the brain activity and the visual content.

This research could have practical applications in the medical and welfare fields. For example, it could help develop communication devices for people who cannot speak or assist in understanding how hallucinations and dreams work in the brain.

One of the researchers, Kei Majima, pointed out that while humans have long used devices like microscopes to see things invisible to the naked eye, this is the first time we have been able to look inside a person’s mind. The technology could allow us to better understand how the brain processes the world around us.

The team’s findings were recently published in the international scientific journal Neural Networks, marking a significant step forward in neuroscience and AI research.

Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.