China has approved the world’s first invasive brain-computer chip—here’s what’s next

2026-06-01 · Source: MIT Technology Review · Field: Technology & Digital — Robotics & Autonomous Systems, Emerging Technologies & Innovation, Artificial Intelligence & Machine Learning · Depth: Novice, medium

Summary

China has approved NEO, the world's first invasive brain-computer interface (BCI) product for commercial use beyond clinical trials, developed by Shanghai-based Neuracle Technology and Tsinghua University. This coin-sized device, which places eight sensors on the dura mater, was approved in March, following 36 clinical trials since October 2023, with 32 conducted in 2025. Patient Dong Hui, 39, paralyzed from spinal cord injuries six years prior, regained the ability to write and grab a ball after an 11-month rehabilitation, starting a week post-surgery in November 2024. NEO's less invasive design, compared to Neuralink's N1, and China's expedited regulatory pathway contributed to its rapid approval. It is now available for patients aged 18-60 with limb paralysis due to spinal cord injuries who retain some arm function.

Key takeaway

For medical device developers aiming for rapid BCI commercialization, China's approval of Neuracle's NEO demonstrates the value of less invasive designs and strategic regulatory engagement. You should prioritize BCI solutions that minimize surgical risk, potentially accelerating market entry, and actively seek out national policies that offer expedited pathways and financial support to scale patient access. This approach could significantly reduce time-to-market and expand patient reach.

Key insights

China's NEO BCI, less invasive and government-backed, is the first globally approved for commercial use, offering hope for paralysis patients.

Principles

• Less invasive BCI designs can expedite regulatory approval.
• Strong government backing accelerates BCI industry development.
• Public acceptance is crucial for widespread BCI adoption.

Method

NEO's sensors are placed on the dura mater, transmitting signals to a skull implant, which a computer translates for a robotic glove during daily 2.5-hour training.

In practice

• NEO is approved for patients 18-60 with spinal cord injuries and residual arm function.
• Daily 2.5-hour training sessions improve motor control with the robotic glove.

Topics

• Brain-Computer Interface
• Neuracle Technology
• NEO Implant
• Medical Device Approval
• Spinal Cord Injury
• Neurotechnology Policy
• Dura Mater Implants

Best for: AI Scientist, Investor, Entrepreneur, Research Scientist, Policy Maker, Tech Journalist

Related on AIssential

• GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction — Lightweight 3D CNN with global topological features predicts preterm brain injury effectively.
• AI for Interoperability in Health Care: Philips’s Carla Goulart Peron — AI in healthcare augments clinicians to expand access and improve diagnostics, with interoperability identified as the most impactful global capability.
• ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents — ClinEnv reveals LLMs struggle with sequential, uncertain clinical decisions and information gathering in a multi-stage environment.
• Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video — QMT uses smartphone video and deep learning for accessible, accurate 3D kinematic biomarker extraction in clinical settings.
• Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel — An AI-driven system predicts colorectal anastomotic leak risk using CT imaging and deep learning for enhanced surgical planning.
• A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation — Frontal alpha asymmetry proved unreliable for voluntary emotion control in a real-time brain-computer musical interface.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Technology Review.