loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

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

ISBN: 978-989-8565-80-8

Keyword(s): BCI, SSVEP, CCA, MEG, Virtual Reality Objects.

Abstract: 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 coul d provide an effective feature extraction method that automatically determines the sensors that are informative for the recognition of SSVEFs. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.92.28.84

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Reichert, C.; Kennel, M.; Kruse, R.; Hinrichs, H. and Rieger, J. (2013). Efficiency of SSVEF Recognition from the Magnetoencephalogram - A Comparison of Spectral Feature Classification and CCA-based Prediction.In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: BrainRehab, (NEUROTECHNIX 2013) ISBN 978-989-8565-80-8, pages 233-237. DOI: 10.5220/0004645602330237

@conference{brainrehab13,
author={Christoph Reichert. and Matthias Kennel. and Rudolf Kruse. and Hermann Hinrichs. and Jochem W. Rieger.},
title={Efficiency of SSVEF Recognition from the Magnetoencephalogram - A Comparison of Spectral Feature Classification and CCA-based Prediction},
booktitle={Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: BrainRehab, (NEUROTECHNIX 2013)},
year={2013},
pages={233-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004645602330237},
isbn={978-989-8565-80-8},
}

TY - CONF

JO - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics - Volume 1: BrainRehab, (NEUROTECHNIX 2013)
TI - Efficiency of SSVEF Recognition from the Magnetoencephalogram - A Comparison of Spectral Feature Classification and CCA-based Prediction
SN - 978-989-8565-80-8
AU - Reichert, C.
AU - Kennel, M.
AU - Kruse, R.
AU - Hinrichs, H.
AU - Rieger, J.
PY - 2013
SP - 233
EP - 237
DO - 10.5220/0004645602330237

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.