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

Evaluating a Semi-Autonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision