Brain-monitoring is rapidly becoming an important field of research, with potentially significant impacts on how people interact with technology. As the inner- workings of the brain become better understood, sensing technologies are also advancing, becoming smaller, cheaper, and ubiquitous. It is expected that new forms of computing that take advantage of brain state information to deduce user mental contexts (emotions, intentions, and moods) will be developed. This capability would enable systems to perform streamlined user-interaction, monitoring, and assistance, as they would access, manage, and respond to real-time brain state dynamics for adaptive applications and services. In this new domain of brain-monitoring, particularly for non-rehabilitative purposes, there are few studies that consider how to leverage distributed agent architectures. Additionally, current approaches to brain monitoring systems have tended toward non-scalable, single user, single application situations. However, for a ubiquitous system, it is unrealistic for each possible application to have the specialized overhead required; hence a distributed, yet still personalized, approach is essential. To realize this, a multi-purpose agent system for brain-monitoring and management of brain context is the goal of this work. It involves the selection of a brain-monitoring paradigm, an agent architecture, an inferencing mechanism, and the combination of the three towards a unified framework. This general framework is implemented and tested on an application scenario, leveraging brain context as part of a service-oriented architecture. Finally, an assessment is conducted of the technology, studying the implications of the system. By contributing a unique methodology and approach to making such systems tenable, this work helps to pave the way toward making futuristic, adaptive, human-aware information systems that are both effective and practical.
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