Search

Abstract: Many patients suffer from the loss of motor skills, resulting from traumatic brain and spinal cord injuries, stroke, and many other disabling conditions. Thanks to technological advances in measuring and decoding the electrical activity of cortical neurons, brain–machine interfaces (BMI) have become a promising technology that can aid paralyzed individuals. In recent studies on BMI, robotic manipulators have demonstrated their potential as neuroprostheses. Restoring motor skills through robot manipulators controlled by brain signals may improve the quality of life of people with disability. This article reviews current robotic technologies that are relevant to BMI and suggests strategies that could improve the effectiveness of a brain-operated neuroprosthesis through robotics. [Copyright &y& Elsevier]

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 out- puts 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. [ABSTRACT FROM AUTHOR]