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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Vydáno v:	Computational intelligence and neuroscience Ročník 2019; číslo 2019; s. 1 - 19
Hlavní autoři:	Ramírez-Moreno, Mauricio Adolfo, Gutiérrez, David
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cairo, Egypt Hindawi Publishing Corporation 2019 Hindawi John Wiley & Sons, Inc
Témata:	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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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Gutiérrez, David Ramírez-Moreno, Mauricio Adolfo
AuthorAffiliation	Centro de Investigación y de Estudios Avanzados (Cinvestav), Unidad Monterrey, Apodaca, Nuevo Leon 66600, Mexico
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31885534$$D View this record in MEDLINE/PubMed
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Cites_doi	10.3389/fneur.2017.00696 10.3758/bf03212398 10.1155/2015/824175 10.1073/pnas.0403504101 10.1088/1741-2560/8/3/036012 10.1016/j.ergon.2004.12.003 10.1038/18581 10.1088/1741-2560/9/2/026014 10.2174/1874120701408010093 10.1088/1741-2552/aab2f2 10.1109/tsmcc.2012.2219046 10.1016/j.neunet.2019.07.008 10.1109/tcds.2017.2777180 10.1016/j.robot.2010.05.010 10.1088/1741-2560/3/1/r02 10.1152/jn.01052.2004 10.1155/2019/8068357 10.1016/s0896-6273(02)00719-5 10.1162/pres.19.1.35 10.1155/2007/79642 10.1109/tnsre.2003.814454 10.1155/2019/5627156 10.1088/1741-2560/5/2/012 10.13053/cys-19-3-2012 10.1109/tnsre.2003.814481 10.1109/tnsre.2009.2039594 10.1111/j.1469-8986.1980.tb00146.x 10.5709/acp-0189-9 10.1016/j.biopsycho.2008.08.004 10.1088/1741-2560/4/2/r01 10.3389/fneng.2012.00014 10.4173/mic.2016.1.6 10.1371/journal.pone.0191673 10.1023/A:1023437823106 10.1088/1741-2560/5/2/001 10.1007/s00006-008-0096-5 10.1016/j.neuroimage.2006.07.049 10.1016/s1388-2457(02)00057-3 10.1016/j.biopsycho.2014.07.014
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Copyright	Copyright © 2019 Mauricio Adolfo Ramírez-Moreno and David Gutiérrez. COPYRIGHT 2019 John Wiley & Sons, Inc. Copyright © 2019 Mauricio Adolfo Ramírez-Moreno and David Gutiérrez. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 Copyright © 2019 Mauricio Adolfo Ramírez-Moreno and David Gutiérrez. 2019
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SubjectTerms	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
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Title	Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision
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