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 sys
tem works most reliably when subjects freely select objects and receive virtual grasp feedback.
(More)