The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.
Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.
Kim
Hyun K
HK
Mechatronics and Manufacturing Technology Center, Samsung Electronics, Suwon, Korea. hyunkim@mit.edu
Carmena
Jose M
JM
Biggs
S James
SJ
Hanson
Timothy L
TL
Nicolelis
Miguel A L
MA
Srinivasan
Mandayam A
MA
D001132:Arm / Q000502:physiology*; D001842:Bone and Bones / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D017097:Electric Impedance; D005071:Evoked Potentials / Q000502:physiology; D005246:Feedback; D006801:Humans; D008328:Man-Machine Systems; D008954:Models, Biological; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D014584:User-Computer Interface