BCI ontology: A context-based sense and actuation model for brain-computer interactions

Key developments in wearable sensors, wireless networks, and distributed computing will largely enable Brain-Computer Interaction (BCI) as a powerful, natural and intuitive mainstream human-computer interaction in real-world activities. BCI systems annotate the sensed signals in order to classify the analysis of brain states/dynamics in diverse daily-life circumstances. There is no any complete and standardized formal semantic structure to model the BCI metadata annotations, which are essential to capture the descriptive and predictive features of the brain signals. We present the BCI Ontology (BCI-O): The first OWL 2 ontology that formalizes relevant metadata for BCI data capture activities by integrating BCI-domain-specific Sense and Actuation Models along with a novel Context Model for describing any kind of real/virtual environments. At its core, BCI-O defines a human-environment interaction model for any BCI, based on design patterns and primarily aligned to the SOSA/SSN, SAN-IoT-O- A nd DUL ontologies. Its axiomatizations aid BCI systems to implement an ontological overlay upon vast data recording collections to support semantic query constructions (to perform Adaptive BCI) and reasoning for situation-specific data analytics (to apply inference rules for Transfer Learning in multimodal classification).