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

TL;DR

This study evaluates a semi-autonomous brain-computer interface that simplifies robotic manipulation tasks by combining conformal geometric algebra and artificial vision, demonstrating improved performance and reduced fatigue over traditional control methods.

Contribution

The paper introduces a semi-autonomous BCI system integrating geometric algebra and artificial vision for robotic manipulation, reducing user effort and increasing efficiency.

Findings

Semi-autonomous approach outperforms traditional control in task completion time.

Users experience less mental fatigue with the semi-autonomous system.

Artificial vision effectively localizes objects for robotic manipulation.

Abstract

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.