China Records EEG From 1,000 People Across Multiple Regions Simultaneously for the First Time to Train AI

On July 22, 2026, a Chinese research team unveiled a new device for collecting electroencephalogram (EEG) data and, for the first time in the world, synchronously recorded brain signals from more than a thousand people located in different regions of the country. The news was reported by the TV channel CCTV Finance (央视财经) and carried by the business outlet 36Kr.

What the Breakthrough Is

For the first time in the world, researchers managed to record EEG simultaneously and in sync from more than a thousand geographically dispersed people. Until now, large-scale collection of brain signals remained a laboratory experiment involving a handful or a few dozen participants. According to the developers, this is a key step toward training large neural models and building universal brain-computer interface (BCI) technology.

CCTV Finance calls the brain-computer interface one of the most important industries of the future, and large-scale, high-quality EEG data the foundation for moving the technology from the lab into industry.

• Date — July 22, 2026; the presentation was shown in the afternoon
• Scale — simultaneous EEG collection from more than 1000 people at once
• Geography — participants were in different regions, not in a single laboratory
• World record — the first cross-regional synchronous collection of this scale in the world
• Goal — accumulating data to train neural foundation models

What Problems the Team Had to Solve

The development team named two main technical difficulties. The first was preserving recording accuracy while miniaturizing the device: the instrument had to get smaller without losing EEG signal quality. The second was overcoming the effect of network delays and achieving millisecond-level time synchronization across many devices in different regions.

Millisecond-level time alignment is essential here. Without it, signals from different people and instruments cannot be analyzed in a single frame of reference, which means a thousand separate recordings cannot be merged into one coherent dataset. Synchronization is what turns scattered EEG streams into a dataset suitable for training models.

Why This Matters for Artificial Intelligence

The collected data, synchronized EEG recordings from more than a thousand people, will be used to train neural foundation models. As CCTV Finance explains, the large-scale EEG collection technology will gradually accumulate such data and use it to train models.

Direct collection of neural signals changes the nature of the training material for AI. Until now, models have learned from indirect information: text, images, and video. Recording brain activity lets AI read a person's cognitive state directly, rather than through intermediary media.

AI training material is no longer limited to text, pictures, and video — now neural signals make it possible to understand a person's cognitive state directly. — from the CCTV

Finance (央视财经) report

What This Means

Scaling up EEG collection is first and foremost about data, not about a single gadget: for neural interfaces to move from the lab into industry, they need large, synchronized datasets of brain signals. The Chinese team showed that such collection is technically possible across thousands of people in different cities, and announced it as a world first. The CCTV Finance report discloses neither the name of the developing institute or company nor the device's exact specifications, such as channel count and sampling rate, so the methodology cannot yet be independently assessed.

Frequently Asked Questions

How many people took part in the synchronous EEG collection?

According to CCTV Finance, brain signals were recorded synchronously from more than a thousand people at the same time. It is the first case in the world of cross-regional EEG collection at this scale.

Why collect EEG from thousands of people at once?

Large-scale, high-quality EEG data is needed to train neural foundation models. According to the developers, this will let AI understand a person's cognitive state directly from neural signals, rather than only from text, pictures, and video.