Imagine a brain implant that acts like a smart translator, turning thoughts into movement without needing a technician to calibrate it every day. Instead of bulky computers and wires, it uses tiny, low-power chips that learn and adapt on their own. This allows people with spinal injuries to control their own limbs or robotic aids independently.
Current neuroprosthetics require time-consuming, expert-led calibration in controlled labs, making them impractical for daily use by the 746,000 people injured annually.
An auto-adaptive brain decoding system and neuromorphic hardware for miniaturized, low-power neuroprosthetics.
If you are a medical device company dealing with the high cost of expert-led calibration for BMIs — this project developed auto-adaptive decoders that allow for unsupervised use. This reduces the need for constant clinical supervision and makes the device viable for daily home use.
If you are a robotics firm dealing with complex control interfaces for tetraplegic patients — this project developed a real-time neuronal activity decoder. This enables a more seamless connection between the user's brain and the robotic exoskeleton.
If you are a chip maker dealing with high power consumption in implantable electronics — this project developed neuromorphic hardware architecture. This ensures the neuroprosthetics remain miniaturized and low-power for long-term implantation.
Based on available project data, specific pricing or cost structures are not provided.
The project is currently exploring miniaturized embedded solutions and testing them in clinical trials, indicating it is in the development phase rather than full industrial scale.
Based on available project data, there is no specific information regarding IP or licensing terms.
The project runs from 2022-10-01 to 2026-09-30, suggesting that final results and specifications for the next-generation system will be available by late 2026.
It is designed to integrate with wireless ECoG recording implants and implantable pulse generators like the ARCIM Therapy platform.
The consortium is well-balanced for translation, consisting of 5 partners across 4 countries. With a 40% industry ratio (including 2 SMEs), the project bridges the gap between high-level research (CEA, Universities) and commercial application, ensuring that the neuromorphic hardware and decoders are designed for actual market viability.
Contact the Commissariat à l'énergie atomique et aux énergies alternatives (CEA) in France.
Contact us to explore licensing opportunities for auto-adaptive BMI decoders.
