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

2013

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 could provide an effective feature extraction method that automatically determines the sensors that are informative for the recognition of SSVEFs.

References

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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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