This technology is a hardware architecture that enables real-time processing of neural data directly on an implanted brain-computer interface system-on-chip.
Brain-computer interfaces (BCIs) are systems that connect brain neural activity to external devices, allowing for a deeper understanding of the brain. However, BCIs can also be utilized in medical assistive technologies and the development of artificial intelligence. BCIs have developed to be implantable system-on-chips (SoCs), but require large volumes of transmitted data, have limited scalability in terms of the number of neural interface sensors, and contain communication bottlenecks. There is a need for BCI SoCs that handle large-scale, safe, and real-time processing of the brain’s neural signals.
This technology is a hardware architecture for an implanted system-on-chip (SoC) that can execute machine-learning (ML) models in brain-computer interface (BCI) systems. The architecture enables the real-time processing of neural data directly on the implanted SoC, significantly reducing the volume of data transmitted outside the body. By doing so, the technology minimizes communication overhead and supports an increasing number of neural interface sensors, while still meeting the safety and functionality requirements of implanted devices like SoCs. By reducing the amount of external data processing, this technology has the potential to advance implantable BCI SoCs.
Patent Pending
IR CU25171
Licensing Contact: Greg Maskel
