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

Christoph Reichert; Matthias Kennel; Rudolf Kruse; Hermann Hinrichs; Jochem W. Rieger

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Paper

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, 233-237, 2013 , Vilamoura, Algarve, Portugal

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

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 cou ld provide an effective feature extraction method that automatically determines the sensors that are informative for the recognition of SSVEFs. (More)

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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 (NEUROTECHNIX 2013) - BrainRehab; ISBN 978-989-8565-80-8, SciTePress, 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 (NEUROTECHNIX 2013) - BrainRehab},
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 (NEUROTECHNIX 2013) - BrainRehab
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
PB - SciTePress