Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces.

Published

Journal Article

Research on brain-machine interfaces (BMI's) is directed toward enabling paralyzed individuals to manipulate their environment through slave robots. Even for able-bodied individuals, using a robot to reach and grasp objects in unstructured environments can be a difficult telemanipulation task. Controlling the slave directly with neural signals instead of a hand-master adds further challenges, such as uncertainty about the intended trajectory coupled with a low update rate for the command signal. To address these challenges, a continuous shared control (CSC) paradigm is introduced for BMI where robot sensors produce reflex-like reactions to augment brain-controlled trajectories. To test the merits of this approach, CSC was implemented on a 3-degree-of-freedom robot with a gripper bearing three co-located range sensors. The robot was commanded to follow eighty-three reach-and-grasp trajectories estimated previously from the outputs of a population of neurons recorded from the brain of a monkey. Five different levels of sensor-based reflexes were tested. Weighting brain commands 70% and sensor commands 30% produced the best task performance, better than brain signals alone by more than seven-fold. Such a marked performance improvement in this test case suggests that some level of machine autonomy will be an important component of successful BMI systems in general.

Full Text

Duke Authors

Cited Authors

  • Kim, HK; Biggs, SJ; Schloerb, DW; Carmena, JM; Lebedev, MA; Nicolelis, MAL; Srinivasan, MA

Published Date

  • June 2006

Published In

Volume / Issue

  • 53 / 6

Start / End Page

  • 1164 - 1173

PubMed ID

  • 16761843

Pubmed Central ID

  • 16761843

International Standard Serial Number (ISSN)

  • 0018-9294

Digital Object Identifier (DOI)

  • 10.1109/TBME.2006.870235

Language

  • eng

Conference Location

  • United States