Toward a biomimetic, bidirectional, brain machine interface.
The interest in Brain Machine Interface (BMI) systems has increased tremendously in recent times; many groups have become involved in this type of research, and progress has been quite encouraging. However, two fundamental limitations remain: 1) With a few notable exceptions, BMIs extract only kinematic information from the brain, ignoring the wealth of force or kinetic information also present in the primary motor cortex, and 2) most existing BMIs depend exclusively on natural vision to guide movement, lacking the rapid proprioceptive feedback that is critical for normal movement. The work reported here describes our efforts to address both of these limitations.
Fagg
Andrew H
AH
School of Computer Science, University of Oklahoma, USA. fagg@cs.ou.edu
Hatsopoulos
Nicholas G
NG
London
Brian M
BM
Reimer
Jacob
J
Solla
Sara A
SA
Wang
Di
D
Miller
Lee E
LE
D000465:Algorithms; D001696:Biomechanical Phenomena; D032701:Biomimetics; D001921:Brain / Q000473:pathology / Q000502:physiology; D004867:Equipment Design; D006801:Humans; D007700:Kinetics; D008328:Man-Machine Systems; D015233:Models, Statistical; D009044:Motor Cortex / Q000473:pathology; D009068:Movement; D012371:Robotics; D019415:Torque; D014584:User-Computer Interface; D014785:Vision, Ocular