  In this paper, we evaluate a semi-autonomous brain-computer interface (BCI)
for manipulation tasks. In such system, the user controls a robotic arm through
motor imagery commands. In traditional process-control BCI systems, the user
has to provide those commands continuously in order manipulate the effector of
the robot step-by-step, which results in a tiresome process for simple tasks
such as pick and replace an item from a surface. Here, we take a
semi-autonomous approach based on a conformal geometric algebra model that
solves the inverse kinematics of the robot on the fly, then the user only has
to decide on the start of the movement and the final position of the effector
(goal-selection approach). Under these conditions, we implemented
pick-and-place tasks with a disk as an item and two target areas placed on the
table at arbitrary positions. An artificial vision (AV) algorithm was used to
obtain the positions of the items expressed in the robot frame through images
captured with a webcam. Then, the AV algorithm is integrated to the inverse
kinematics model to perform the manipulation tasks. As proof-of-concept,
different users were trained to control the pick-and-place tasks through the
process-control and semi-autonomous goal-selection approaches, so that the
performance of both schemes could be compared. Our results show the superiority
in performance of the semi-autonomous approach, as well as evidence of less
mental fatigue with it.
