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Otto von Guericke University of Magdeburg, Germany
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2 Papers

Efficiency of SSVEF Recognition from the Magnetoencephalogram - A Comparison of Spectral Feature Classification and CCA-based Prediction

Authors: Christoph Reichert 1 , Matthias Kennel 2 , Rudolf Kruse 3 , 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 University Medical Center A.ö.R. and Carl-von-Ossietzky University, Germany

Digital Object Identifier: 10.5220/0004645602330237

Proceedings of BrainRehab 2013/NEUROTECHNIX, ISBN: 978-989-8565-80-8

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Steady-state visual evoked potentials (SSVEP) are a popular method to control brain–computer interfaces (BCI). Here, we present a BCI for selection of virtual reality (VR) objects by decoding the steady-state visual evoked fields (SSVEF), the magnetic analogue to the SSVEP in the magnetoencephalogram (MEG). In a conventional approach, we performed online prediction by Fourier transform (FT) in combination with a multivariate classifier. As a comparative study, we report our approach to increase the BCI-system performance in an offline evaluation. Therefore, we transferred the canonical correlation analysis (CCA), originally employed to recognize relatively low dimensional SSVEPs in the electroencephalogram (EEG), to SSVEF recognition in higher dimensional MEG recordings. We directly compare the performance of both approaches and conclude that CCA can greatly improve system performance in our MEG-based BCI-system. Moreover, we find that application of CCA to large multi-sensor MEG could provide an effective feature extraction method that automatically determines the sensors that are informative for the recognition of SSVEFs.

Robotic Grasp Initiation by Gaze Independent Brain-controlled Selection of Virtual Reality Objects

Topics: Brain Computer Interfaces; Robotics and Computer Vision; Neuro-Interface Prosthetic Devices

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

Digital Object Identifier: 10.5220/0004608800050012

Proceedings of NEUROTECHNIX 2013, ISBN: 978-989-8565-80-8

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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 system works most reliably when subjects freely select objects and receive virtual grasp feedback.
Collapse Button Related Ontology Subjects/Areas/Topics (10)
Applications

Neuro-Interface Prosthetic Devices

Pattern Recognition

Brain-Computer Interfaces

NeuroSensing and Diagnosis

Robotics

Health Engineering and Technology Applications

Neurotechnology, Electronics and Informatics

Software Engineering
Neural Rehabilitation

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