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We seek to develop a new generation of brain-machine interfaces (BMI) that enable both the user and the computer to engage in a symbiotic relationship where they must co-adapt to each other to solve goal-directed tasks. Such a framework would allow the possibility real-time understanding and modeling of brain behavior and adaptation to a changing environment, a major departure from either offline learning and static models or one-way adaptive models in conventional BMIs. To achieve a symbiotic architecture requires a computing infrastructure that can accommodate multiple neural systems, respond within the processing deadlines of sensorimotor information, and can provide powerful computational resources to design new modeling approaches. To address these issues we present or ongoing work in the development of a neurophysiology Cyberworkstation for BMI design.

Development of Symbiotic Brain-Machine Interfaces Using a Neurophysiology Cyberworkstation