System Architecture for Stiffness Control in Brain-Machine Interfaces

Brain-machine interfaces (BMIs) provide a versatile tool for rehabilitation of severely disabled people. Current BMI systems focus on the control of kinematic variables. However, this approach limits the application space of BMI technology to simulated environments. Real-world rehabilitation robots,...

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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 40; no. 4; pp. 732 - 742
Main Authors Heliot, Rodolphe, Orsborn, Amy L., Ganguly, Karunesh, Carmena, Jose M.
Format Journal Article
LanguageEnglish
Published IEEE 01.07.2010
Subjects
Architecture
Bismaleimides
Brain modeling
Brain-machine interfaces (BMIs)
Control systems
Dynamical systems
Dynamics
Force control
impedance control
Kinematics
Mathematical models
musculoskeletal model
Musculoskeletal system
Neural prosthesis
optimal control
Orbital robotics
Prosthetics
Rehabilitation robotics
Space technology
Stiffness
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Summary:Brain-machine interfaces (BMIs) provide a versatile tool for rehabilitation of severely disabled people. Current BMI systems focus on the control of kinematic variables. However, this approach limits the application space of BMI technology to simulated environments. Real-world rehabilitation robots, on the other hand, must operate in a variety of complex physical situations. BMI systems that are aimed toward prostheses must, then, control interaction forces with their environments. In this paper, we design a BMI-driven architecture that provides a critical link between neuronal ensemble activity and real-world dynamics. In particular, our system allows simultaneous estimation of kinematic and stiffness variables of a prosthetic device. This approach is achieved by recording instantaneous activities from cortical neural ensembles that input to a musculoskeletal model of the arm, from which limb kinematics and dynamics are estimated and converted into control signals of a prosthetic device. Using real neural and behavioral data from nonhuman primates, we show that our architecture can accurately predict kinematic and stiffness variables in different dynamic situations. This architecture has strong implications in the development of the next generation of neural prosthetics that will restore motor function in neurologically impaired patients. Moreover, it demonstrates a novel framework for studying how the brain learns and adapts to new environments.
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2010.2044410