Publications [#64797] of Miguel A Nicolelis
Papers Published
- Kim, H.K. and Biggs, J. and Schloerb, W. and Carmena, M. and Lebedev, M.A. and Nicolelis, M.A.L. and Srinivasan, M.A. (2006). Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces. IEEE Trans. Biomed. Eng. (USA), 53(6), 1164 - 73. [TBME.2006.870235]
(last updated on 2007/04/15)Abstract:
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