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Published April 2005 | Published
Book Section - Chapter Open

Cognitive based neural prosthetics

Abstract

Intense activity in neural prosthetic research has recently demonstrated the possibility of robotic interfaces that respond directly to the nervous system. The question remains of how the flow of information between the patient and the prosthetic device should be designed to provide a safe, effective system that maximizes the patient's access to the outside world. Much recent work by other investigators has focused on using decoded neural signals as low-level commands to directly control the trajectory of screen cursors or robotic end-effectors. Here we review results that show that high-level, or cognitive, signals can be decoded from planned arm movements. These results, coupled with fundamental limitations in signal recording technology, motivate an approach in which cognitive neural signals play a larger role in the neural interface. This proposed paradigm predicates that neural signals should be used to instruct external devices, rather than control their detailed movement. This approach will reduce the effort required of the patient and will take advantage of established and on-going robotics research in intelligent systems and human-robot interfaces.

Additional Information

© 2005 IEEE. Issue Date: 18-22 April 2005. Date of Current Version: 10 January 2006. This work is partially supported by National Eye Institute, DARPA, ONR, the Boswell Foundation, NSF, the Christopher Reeve Paralysis Foundation, the Sloan-Swartz Center for Theoretical Neurobiology at Caltech, and the Human Frontier Science Program. We would like to thank the members of the Andersen lab at Caltech, especially C. Buneo, B. Pesaran, B. Corneil and B. Greger. We also thank E. Branchaud and Z. Nenadic.

Attached Files

Published - Andersen2005p93922008_Ieee_International_Conference_On_Robotics_And_Automation_Vols_1-9.pdf

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Andersen2005p93922008_Ieee_International_Conference_On_Robotics_And_Automation_Vols_1-9.pdf

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Created:
August 22, 2023
Modified:
January 13, 2024