Development of Symbiotic Brain-Machine Interfaces Using a Neurophysiology Cyberworkstation

Authors:
Justin C. Sanchez;Renato Figueiredo;Jose Fortes;Jose C. Principe
Affiliations:
Department of Pediatrics, and Department of Neuroscience, and Department of Biomedical Engineering,;Department of Electrical and Computer Engineering, University of Florida, Gainesville,;Department of Electrical and Computer Engineering, University of Florida, Gainesville,;Department of Biomedical Engineering, and Department of Electrical and Computer Engineering, University of Florida, Gainesville,
Venue:
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Year:
2009

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Abstract

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.