MINDFUL: Safe, Implantable, Large-Scale Brain-Computer Interfaces from a System-Level Design Perspective

Brain-computer interface (BCI) technology is among the fastest growing fields in research and development. On the application side, BCIs provide a deeper understanding of brain function, inspire the creation of complex computational models, and hold significant promise for assisting individuals with disabilities. On the system side, BCIs have evolved from non-invasive, low-resolution wearable devices to invasive, high-resolution, implantable systems-on-chip (SoCs) that offer higher-quality brain data, enabling more effective exploration of brain activity. However, implantable BCIs must acquire large-scale neural signals and run real-time BCI applications, all while relying on wireless communication for practical use. Unlike typical devices, BCIs must operate within strict power constraints to ensure safety, which is crucial for their deployment in real-world applications. This requires careful co-design and a balanced approach across the key components of the BCI system. In this work, we discuss why BCIs present unique design challenges compared to conventional computing systems. We develop equations based on the system-level structure of modern BCIs to estimate power consumption and explore trade-offs among key system components: data acquisition, on-chip computation, and wireless communication. Using these equations, we analyze BCI SoC designs that support wireless communication and examine how scaling trends, design constraints, and optimization strategies may impact the feasibility of future BCIs. Specifically, we show a clear discrepancy between certain cutting-edge, BCI-centric computations and the feasibility of their on-chip integration in power-constrained BCI systems, revealing a significant gap between the development of deep learning methods for BCI and the design of safe BCI systems. However, with targeted optimizations in BCI system design and greater specialization for specific applications, future BCI systems will be able to successfully integrate modern BCI applications and advance toward widespread adoption.