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Authors: Christoph Reichert 1 ; Matthias Kennel 2 ; Rudolf Kruse 3 ; Hans-Jochen Heinze 4 ; Ulrich Schmucker 2 ; Hermann Hinrichs 4 and Jochem W. Rieger 5

Affiliations: 1 University Medical Center A.ö.R. and Otto-von-Guericke University, Germany ; 2 Fraunhofer Institute for Factory Operation and Automation IFF, Germany ; 3 Otto-von-Guericke University, Germany ; 4 University Medical Center A.ö.R, Leibniz Institute for Neurobiology and German Center for Neurodegenerative Diseases (DZNE), Germany ; 5 Carl-von-Ossietzky University, Germany

Keyword(s): BCI, P300, Oddball Paradigm, Grasping, MEG.

Related Ontology Subjects/Areas/Topics: Applications ; Brain-Computer Interfaces ; Health Engineering and Technology Applications ; Neural Rehabilitation ; Neuro-Interface Prosthetic Devices ; NeuroSensing and Diagnosis ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Robotics ; Software Engineering

Abstract: Assistive devices controlled by human brain activity could help severely paralyzed patients to perform everyday tasks such as reaching and grasping objects. However, the continuous control of anthropomorphic prostheses requires control of a large number of degrees of freedom which is challenging with the currently achievable information transfer rate of noninvasive Brain Computer Interfaces (BCI). In this work we present an autonomous grasping system that allows grasping of natural objects even with the very low information transfer rates obtained in noninvasive BCIs. The grasp of one out of several objects is initiated by decoded voluntary brain wave modulations. A universal online grasp planning algorithm was developed that grasps the object selected by the user in a virtual reality environment. Our results with subjects demonstrate that training effort required to control the system is very low (<10 min) and that the decoding accuracy increases over time. We also found that the sy stem works most reliably when subjects freely select objects and receive virtual grasp feedback. (More)

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Paper citation in several formats:
Reichert, C.; Kennel, M.; Kruse, R.; Heinze, H.; Schmucker, U.; Hinrichs, H. and W. Rieger, J. (2013). Robotic Grasp Initiation by Gaze Independent Brain-controlled Selection of Virtual Reality Objects. In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX; ISBN 978-989-8565-80-8, SciTePress, pages 5-12. DOI: 10.5220/0004608800050012

@conference{neurotechnix13,
author={Christoph Reichert. and Matthias Kennel. and Rudolf Kruse. and Hans{-}Jochen Heinze. and Ulrich Schmucker. and Hermann Hinrichs. and Jochem {W. Rieger}.},
title={Robotic Grasp Initiation by Gaze Independent Brain-controlled Selection of Virtual Reality Objects},
booktitle={Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX},
year={2013},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004608800050012},
isbn={978-989-8565-80-8},
}

TY - CONF

JO - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - NEUROTECHNIX
TI - Robotic Grasp Initiation by Gaze Independent Brain-controlled Selection of Virtual Reality Objects
SN - 978-989-8565-80-8
AU - Reichert, C.
AU - Kennel, M.
AU - Kruse, R.
AU - Heinze, H.
AU - Schmucker, U.
AU - Hinrichs, H.
AU - W. Rieger, J.
PY - 2013
SP - 5
EP - 12
DO - 10.5220/0004608800050012
PB - SciTePress

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