Translation. Region: Russian Federation –
Source: People's Republic of China in Russian – People's Republic of China in Russian –
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Source: People's Republic of China – State Council News
BEIJING, December 21 (Xinhua) — A patient with tetraplegia navigated a smart wheelchair around the neighborhood with only his thoughts and commanded a robotic dog to fetch takeout food delivered by a courier. These scenes were the results of a clinical trial of an implantable brain-computer interface (BCI) recently conducted by a team of researchers from the Chinese Academy of Sciences (CAS).
These capabilities go beyond traditional rehabilitation, moving beyond two-dimensional cursor control on a screen and allowing interaction with the three-dimensional physical world.
BCIs are designed to create a direct communication channel between the brain and external devices. Research teams around the world have already demonstrated laboratory achievements, including mental typing and control of a robotic arm. However, the stable integration of this technology into patients' daily lives remains an ongoing challenge.
The aforementioned patient has suffered from tetraplegia since 2022 due to a spinal cord injury. In June 2025, he was implanted with an invasive brain-computer interface system developed by the Center for Research in Neurobiology and Intelligent Technologies at the Kanas Academy of Sciences. After several weeks of training and practice, he is now able to reliably control a computer cursor and a tablet.
The researchers used a high-bandwidth wireless implantable invasive BCI system to enable the patient to stably control a smart wheelchair and a robotic dog using neural signals, enabling autonomous mobility and object retrieval in real-world conditions.
This important milestone marks China's progress from restoring basic interaction capabilities to expanding the boundaries of functional activities for paralyzed patients in real-life settings.
The research team also made several technical breakthroughs, developing a high-compression, high-fidelity neural data compression technology and innovatively integrating two decoding methods. This hybrid decoding model effectively extracts useful information even in the presence of relatively noisy neural signals, improving overall brain control performance by more than 15%.
Furthermore, the researchers reduced the delay from signal receipt to command execution to less than 100 milliseconds – below the physiological latency threshold – providing patients with smoother and more natural control of the BCI system. -0-
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