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An asynchronous BMI for autonomous robotic grasping based on SSVEF detection

: Reichert, Christoph; Kennel, Matthias; Kruse, Rudolf; Hinrichs, Hermann

Postprint urn:nbn:de:0011-n-3329700 (1.2 MByte PDF)
MD5 Fingerprint: 29a555c5b013d936a3dec71cb3409870
Erstellt am: 29.4.2015

Müller-Putz, Gernot:
6th International Brain-Computer Interface Conference 2014. Proceedings : The Future of Brain-Computer Interaction: Basics, Shortcomings, Users; September 16-19, 2014, Graz University of Technology, Austrian
Graz: Verlag der TU Graz, 2014
ISBN: 978-3-85125-378-8
Art. 047, 4 S.
International Brain-Computer Interface Conference <6, 2014, Graz>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IFF ()

Severely impaired persons could greatly benefit from assistive devices controlled by brain activity. However, the low information transfer rate of noninvasive neuroimaging techniques complicates complex and asynchronous control of robotic devices enormously. In this paper we present an asynchronous brain–machine interface (BMI) relying on autonomous grasp planning. The system enables a user to grasp and manipulate objects with a minimal set of commands. We successfully tested the system in a virtual environment with eight subjects. Our results suggest that the system represents a promising approach for real-world application of brain–controlled intelligent robotic devices.