SHF: Small: A Ubiquitous Brain-Computer Interface for Supplementing Cognitive and Motor Functions

San Diego State University Foundation, San Diego CA

Investigators

Abstract

Brain-computer interfaces (BCIs) enable communication between the brain and external devices for individuals with neurological disorders, bypassing damaged neuromuscular pathways. However, most existing BCIs are designed for specific applications. They capture neural activity from a specific brain region associated with a given task, extract key features from the recorded signals, and translate them into the user’s intent. The decoded information is transmitted as commands for discrete goal selection or continuous control of assistive devices. Even within the same application, BCI technologies vary significantly depending on the user’s communication and control capabilities. As a result, the current BCI ecosystem is fragmented and lacks flexibility, necessitating a more adaptable solution that supports multiple applications simultaneously. This research aims to address these limitations by introducing a ubiquitous BCI (uBCI) framework — a versatile system designed to enhance both cognitive and motor functions through advanced neural signal processing and an energy-efficient digital architecture. Unlike conventional BCIs, which rely on a single brain region and predefined features, the uBCI system analyzes signals from multiple brain regions, leveraging diverse neural features for real-time, accurate decoding of user intent. The uBCI framework enables both discrete goal selection and continuous control, ensuring long-term reliability in neural signal interpretation for practical applications. Beyond its scientific contributions, this research has the potential to transform the lives of millions of people affected by neurological disorders. Educational initiatives include the 'Brain Chips' outreach program, which engages high school students; the integration of research findings into the Computer Engineering curriculum at San Diego State University; open-access dissemination of research results; and community engagement through workshops at the Disability Center San Diego. The uBCI framework is built on a robust technological foundation that ensures adaptability, efficiency, and seamless integration across multiple applications. The system integrates a custom digital integrated circuit for in vivo processing with a programmable processor featuring a heterogeneous architecture for in silico processing. To evaluate efficiency, the system's components are implemented and tested on field-programmable gate arrays (FPGAs), communicating wirelessly via Bluetooth. The uBCI is validated and optimized using neural datasets from human and non-human primates, along with prosthetic testing on the humanoid Baxter Robot System. By optimizing neural signal processing algorithms and associated digital circuits across in vivo (biological) and in silico (computational) domains, this project advances fundamental BCI research while bridging the gap between laboratory prototypes and real-world deployment, paving the way for scalable and user-adaptive neural interfaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.