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

In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a 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 to manipulate...

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Vydané v:	Computational intelligence and neuroscience Ročník 2019; číslo 2019; s. 1 - 19
Hlavní autori:	Ramírez-Moreno, Mauricio Adolfo, Gutiérrez, David
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cairo, Egypt Hindawi Publishing Corporation 2019 Hindawi John Wiley & Sons, Inc
Predmet:	Algebra Algorithms Analysis Artificial Intelligence Artificial vision Biomechanical Phenomena Blindness Brain Brain - physiology Brain research Brain-Computer Interfaces Classification Computer applications Control systems Design Electroencephalography Electroencephalography - methods Evaluation Event-Related Potentials, P300 Fatigue Female Goals Human-computer interface Humans Imagery Imagination - physiology Implants Interfaces Inverse kinematics Kinematics Male Mental Fatigue - etiology Mental task performance Models, Theoretical Motor Activity - physiology Neurosciences Pick and place tasks Proof of Concept Study R&D Research & development Robot arms Robotics Robotics - methods Robots Signal Processing, Computer-Assisted Webcams Young Adult
ISSN:	1687-5265, 1687-5273, 1687-5273
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Shrnutí:	In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a 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 to 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 semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and 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 into 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 semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 Guest Editor: Hyun S. Kim
ISSN:	1687-5265 1687-5273 1687-5273
DOI:	10.1155/2019/9374802