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 practic